Category: News
Why does converting my model into a masked subsystem result in an error indicating a mismatch between the inferred signal size and the size determined by back propagation in MATLAB R2022a?
When I transform my model into a masked subsystem and run the Simulink model, I encounter the following error message:
Error:Inferred size (‘[<signal_size>]’) for data ‘y’ does not match back propagated size (‘scalar’) from Simulink.
Why does converting my model into a masked subsystem result in an error indicating a mismatch between the inferred signal size and the size determined by back propagation in MATLAB R2022a?When I transform my model into a masked subsystem and run the Simulink model, I encounter the following error message:
Error:Inferred size (‘[<signal_size>]’) for data ‘y’ does not match back propagated size (‘scalar’) from Simulink.
Why does converting my model into a masked subsystem result in an error indicating a mismatch between the inferred signal size and the size determined by back propagation in MATLAB R2022a? When I transform my model into a masked subsystem and run the Simulink model, I encounter the following error message:
Error:Inferred size (‘[<signal_size>]’) for data ‘y’ does not match back propagated size (‘scalar’) from Simulink.
Why does converting my model into a masked subsystem result in an error indicating a mismatch between the inferred signal size and the size determined by back propagation in MATLAB R2022a? inferred, signal, size, back, propagation, masked, subsystem MATLAB Answers — New Questions
How can I resolve the “Out of Memory” error without making any changes to the Simulink model?
I’m working with an large Simulink model and encounter an "Out of Memory" error during simulation. I prefer not to alter the model itself. How can I resolve the "Out of Memory" error without making any changes to the Simulink model?I’m working with an large Simulink model and encounter an "Out of Memory" error during simulation. I prefer not to alter the model itself. How can I resolve the "Out of Memory" error without making any changes to the Simulink model? I’m working with an large Simulink model and encounter an "Out of Memory" error during simulation. I prefer not to alter the model itself. How can I resolve the "Out of Memory" error without making any changes to the Simulink model? out, of, memory, operating, point, without, no, change MATLAB Answers — New Questions
Access class methods in one line only when you use brackets after class name.
Hello, I’m hoping someone can explain why you can access a class method in a single statement only when you use brackets after the class name.
For example, I have a class called Data, with method stats. Why does Data.stats raise and error, but Data().stats does not raise an error.
The brackets are not required to instantiate the class, i.e. D = Data is the same as D = Data().
Thanks,
Dale.Hello, I’m hoping someone can explain why you can access a class method in a single statement only when you use brackets after the class name.
For example, I have a class called Data, with method stats. Why does Data.stats raise and error, but Data().stats does not raise an error.
The brackets are not required to instantiate the class, i.e. D = Data is the same as D = Data().
Thanks,
Dale. Hello, I’m hoping someone can explain why you can access a class method in a single statement only when you use brackets after the class name.
For example, I have a class called Data, with method stats. Why does Data.stats raise and error, but Data().stats does not raise an error.
The brackets are not required to instantiate the class, i.e. D = Data is the same as D = Data().
Thanks,
Dale. object oriented, method calls MATLAB Answers — New Questions
How to find the switching losses (reverse recovery losses) of the antiparallel diodes that are in a power converter?
Hello,
My objective is to estimate both conduction and switching losses of semiconductor devices that are used in a DAB converter operation. I have built a simulator using the blocks which can be found in the Simscape library blocks. The semiconductors have been modeled using
1) IGBT (ideal switching) block – IGBT (Ideal, Switching) – Ideal insulated-gate bipolar transistor for switching applications – MATLAB
2) Diode block – Diode – Piecewise linear, exponential, or tabulated diode – MATLAB.
After building the simulator, I ran it for sometime and obtained the simscape results data log for further analysis. And as mentioned in ee_getPowerLossSummary – Calculate dissipated power losses and switching losses – MATLAB, I used that method to estimate both switching and conduction losses using log data. I suppose that the "power" output of the function corresponds to conduction losses. Please advice me otherwise. If the "power" provides the total losses. The provided link of the ee_getPowerLossSummary, it says that the function is capable of estimating both conduction and switching losses for the above devices. Also, highlights that the reverse recovery loss can also be obtained.
However, when I run the simulation and run the function ee_GetPowerLossSummary afterwards, It only provides the conduction losses of the diode. Always, the diode losses are given as zero. It provides the conduction and switching losses for the IGBT. I have modeled the diode for reverse recovery operation by adding values from the datasheet. I was wondering whether there are any additional options to configure to get this to work. Also, I can see that the switching losses of the IGBTs fall drastically when a deadtime is used between High and Low switches in a same leg. When deadtime is not used, a signficant amount of switching losses are present. These observations have been made using the aforementioned ee_getPowerLossSummary function and as well as post processing the instantaneuos power waveforms obtained through power sensors. I don’t understand this since switching losses apply even when the deadtime is used since it only happens due to the IGBT’s own voltage and current. Not by the temporary shootthrough that happens if deadtime is not used.
Any suggestion, comment or advice regarding this process is highly appreciated. Thank you.Hello,
My objective is to estimate both conduction and switching losses of semiconductor devices that are used in a DAB converter operation. I have built a simulator using the blocks which can be found in the Simscape library blocks. The semiconductors have been modeled using
1) IGBT (ideal switching) block – IGBT (Ideal, Switching) – Ideal insulated-gate bipolar transistor for switching applications – MATLAB
2) Diode block – Diode – Piecewise linear, exponential, or tabulated diode – MATLAB.
After building the simulator, I ran it for sometime and obtained the simscape results data log for further analysis. And as mentioned in ee_getPowerLossSummary – Calculate dissipated power losses and switching losses – MATLAB, I used that method to estimate both switching and conduction losses using log data. I suppose that the "power" output of the function corresponds to conduction losses. Please advice me otherwise. If the "power" provides the total losses. The provided link of the ee_getPowerLossSummary, it says that the function is capable of estimating both conduction and switching losses for the above devices. Also, highlights that the reverse recovery loss can also be obtained.
However, when I run the simulation and run the function ee_GetPowerLossSummary afterwards, It only provides the conduction losses of the diode. Always, the diode losses are given as zero. It provides the conduction and switching losses for the IGBT. I have modeled the diode for reverse recovery operation by adding values from the datasheet. I was wondering whether there are any additional options to configure to get this to work. Also, I can see that the switching losses of the IGBTs fall drastically when a deadtime is used between High and Low switches in a same leg. When deadtime is not used, a signficant amount of switching losses are present. These observations have been made using the aforementioned ee_getPowerLossSummary function and as well as post processing the instantaneuos power waveforms obtained through power sensors. I don’t understand this since switching losses apply even when the deadtime is used since it only happens due to the IGBT’s own voltage and current. Not by the temporary shootthrough that happens if deadtime is not used.
Any suggestion, comment or advice regarding this process is highly appreciated. Thank you. Hello,
My objective is to estimate both conduction and switching losses of semiconductor devices that are used in a DAB converter operation. I have built a simulator using the blocks which can be found in the Simscape library blocks. The semiconductors have been modeled using
1) IGBT (ideal switching) block – IGBT (Ideal, Switching) – Ideal insulated-gate bipolar transistor for switching applications – MATLAB
2) Diode block – Diode – Piecewise linear, exponential, or tabulated diode – MATLAB.
After building the simulator, I ran it for sometime and obtained the simscape results data log for further analysis. And as mentioned in ee_getPowerLossSummary – Calculate dissipated power losses and switching losses – MATLAB, I used that method to estimate both switching and conduction losses using log data. I suppose that the "power" output of the function corresponds to conduction losses. Please advice me otherwise. If the "power" provides the total losses. The provided link of the ee_getPowerLossSummary, it says that the function is capable of estimating both conduction and switching losses for the above devices. Also, highlights that the reverse recovery loss can also be obtained.
However, when I run the simulation and run the function ee_GetPowerLossSummary afterwards, It only provides the conduction losses of the diode. Always, the diode losses are given as zero. It provides the conduction and switching losses for the IGBT. I have modeled the diode for reverse recovery operation by adding values from the datasheet. I was wondering whether there are any additional options to configure to get this to work. Also, I can see that the switching losses of the IGBTs fall drastically when a deadtime is used between High and Low switches in a same leg. When deadtime is not used, a signficant amount of switching losses are present. These observations have been made using the aforementioned ee_getPowerLossSummary function and as well as post processing the instantaneuos power waveforms obtained through power sensors. I don’t understand this since switching losses apply even when the deadtime is used since it only happens due to the IGBT’s own voltage and current. Not by the temporary shootthrough that happens if deadtime is not used.
Any suggestion, comment or advice regarding this process is highly appreciated. Thank you. simulink, dab, loss, igbt, diode, switching losses, reverse recovery losses MATLAB Answers — New Questions
Creating a Microsoft 365 Retention Policy for Shared Mailboxes
No Need for Licenses to Include Shared Mailboxes in Retention Policies
After writing about how to replace Exchange litigation holds with Microsoft 365 retention policies, I received a question whether retention policies cover mailboxes that don’t have an Exchange Online Plan 2 service plan, like shared mailboxes. Exchange Online Plan 2 is included in many Office 365 and Microsoft 365 licenses and can be bought and assigned as a separate license. For instance, you can assign an Exchange Online Plan 2 license to a shared mailbox to allow that mailbox to use an archive mailbox, increase its storage quota from 50 GB to 100 GB, or be placed on litigation hold (here’s how to check shared mailboxes against licensing requirements).
The answer is that retention policies with static locations cover shared and other resource mailboxes without requiring a license. The relevant text says: “For many [data lifecycle management] features, a shared or resource mailbox does not need a license assigned.” Shared mailboxes do need licenses when use is made of data lifecycle management features that require user mailboxes to have E5 or Microsoft 365 Compliance or Microsoft 365 Information Protection and Governance licenses. Retention policies based on adaptive scopes are an example.
Adding a Shared Mailbox to a Retention Policy
When a new shared mailbox is created, it receives a default mailbox retention policy. The retention policy is defined in the mailbox plan applied by Exchange Online when it creates the mailbox. In many cases, the Exchange retention policy (called a legacy retention policy by Microsoft) is sufficient to clear out old messages from the mailbox on a regular basis (or move items to an archive mailbox). If a tenant has a Microsoft 365 retention policy covering all Exchange Online mailboxes, shared mailboxes are covered by that policy.
You can also add the shared mailbox to a specific Microsoft 365 retention policy by editing the policy and selecting the shared mailbox, just like any other mailbox (Figure 1).
Mixing the two types of retention policy doesn’t matter because the Exchange managed folder assistant (MFA) combines the settings from all the policies that apply when it processes a mailbox to remove expired items. After the shared mailbox is added to a Microsoft 365 retention policy, you should see the hold applied by the policy in the set of in-place holds noted for the mailbox. For example:
Get-Mailbox Customer.Services@office365itpros.com| Format-List InPlaceholds
InPlaceHolds : {UniHcac6fa1f-ce89-43ef-9e56-839a0e164662}
Creating a Special Microsoft 365 Retention Policy for Shared Mailboxes
Although Exchange retention policies are usually all that’s needed for shared mailboxes, you might feel that it’s best to use Microsoft 365 retention policies for all retention processing and create a Microsoft 365 retention policy for shared mailboxes. The advantage of using a targeted retention policy for shared mailboxes is the ability to apply different retention settings to those used for user mailboxes.
Remember the two downsides of this approach:
- Folder-specific processing isn’t possible with Microsoft 365 retention. All folders have the same retention setting.
- Microsoft 365 retention policies can’t move items to archive mailboxes. This feature requires shared mailboxes to have an Exchange Online Plan 2 license.
To illustrate the point, I wrote a PowerShell script to create a Microsoft 365 retention policy especially for shared mailboxes. The script does the following:
- Connect to Exchange Online and Compliance PowerShell endpoints.
- Find all shared mailboxes.
- Create a new Microsoft 365 retention policy.
- Add the shared mailboxes to the retention policy. Up to 1,000 individual mailboxes can be added to a single retention policy. If a tenant has more than 1,000 shared mailboxes, you’ll need to split the mailboxes across multiple policies. In this scenario, I would write the name of the policy used for a mailbox into one of the 15 custom attributes available for mailboxes.
- Create a retention rule to keep items for two years (730 days). Obviously, you can choose a different retention period.
- Remove the Exchange retention policy from the shared mailboxes.
You can download the script from the Office 365 for IT Pros repository).
Another step to consider is to exclude shared mailboxes from any Microsoft 365 retention policy that processes “All Exchange mailboxes.” This simplifies the retention processing for shared mailboxes to make sure that retention periods and actions from other policies don’t interfere with settings in the retention policy for shared mailboxes.
Some More Work
The retention policy created by the script processes shared mailboxes which exist at that time. More work is needed to maintain the retention policy by adding new shared mailboxes (deleted shared mailboxes are automatically removed from policies). That task is easily done with another script, which I’ll cover in a different post.
Support the work of the Office 365 for IT Pros team by subscribing to the Office 365 for IT Pros eBook. Your support pays for the time we need to track, analyze, and document the changing world of Microsoft 365 and Office 365. Only humans contribute to our work!
NXP S32k1xx Toolbox installation
Hi everyone,
I’m trying to install nxp toolbox but getting given below error kindly help me out.
Dot indexing is not supported for variables of this type.
Error in NXP_Support_Package_S32K1xx>ConvertToolbox_Callback (line 143)
user_selection = get(handles.mbdt_version,’Value’);
^^^^^^^^^^^^^^^^^^^^
Error in gui_mainfcn (line 95)
feval(varargin{:});
^^^^^^^^^^^^^^^^^^
Error in NXP_Support_Package_S32K1xx (line 34)
gui_mainfcn(gui_State, varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)NXP_Support_Package_S32K1xx(‘ConvertToolbox_Callback’,hObject,eventdata,guidata(hObject))
Error using matlab.ui.internal.controller.uicontrol.UIControlController/triggerActionEvent (line 76)
Error while evaluating UIControl Callback.
regards,
Durgaprasad BHi everyone,
I’m trying to install nxp toolbox but getting given below error kindly help me out.
Dot indexing is not supported for variables of this type.
Error in NXP_Support_Package_S32K1xx>ConvertToolbox_Callback (line 143)
user_selection = get(handles.mbdt_version,’Value’);
^^^^^^^^^^^^^^^^^^^^
Error in gui_mainfcn (line 95)
feval(varargin{:});
^^^^^^^^^^^^^^^^^^
Error in NXP_Support_Package_S32K1xx (line 34)
gui_mainfcn(gui_State, varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)NXP_Support_Package_S32K1xx(‘ConvertToolbox_Callback’,hObject,eventdata,guidata(hObject))
Error using matlab.ui.internal.controller.uicontrol.UIControlController/triggerActionEvent (line 76)
Error while evaluating UIControl Callback.
regards,
Durgaprasad B Hi everyone,
I’m trying to install nxp toolbox but getting given below error kindly help me out.
Dot indexing is not supported for variables of this type.
Error in NXP_Support_Package_S32K1xx>ConvertToolbox_Callback (line 143)
user_selection = get(handles.mbdt_version,’Value’);
^^^^^^^^^^^^^^^^^^^^
Error in gui_mainfcn (line 95)
feval(varargin{:});
^^^^^^^^^^^^^^^^^^
Error in NXP_Support_Package_S32K1xx (line 34)
gui_mainfcn(gui_State, varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)NXP_Support_Package_S32K1xx(‘ConvertToolbox_Callback’,hObject,eventdata,guidata(hObject))
Error using matlab.ui.internal.controller.uicontrol.UIControlController/triggerActionEvent (line 76)
Error while evaluating UIControl Callback.
regards,
Durgaprasad B nxp toolbox MATLAB Answers — New Questions
How can I edit the properties for multiple requirements in the Requirements Editor in MATLAB R2024b?
Consider the requirement set file shown in the figure below, which is open in MATLAB R2024b. When I select multiple requirements, the "Properties" pane is greyed out and I cannot edit the properties for multiple requirements. How can I get around this?Consider the requirement set file shown in the figure below, which is open in MATLAB R2024b. When I select multiple requirements, the "Properties" pane is greyed out and I cannot edit the properties for multiple requirements. How can I get around this? Consider the requirement set file shown in the figure below, which is open in MATLAB R2024b. When I select multiple requirements, the "Properties" pane is greyed out and I cannot edit the properties for multiple requirements. How can I get around this? simulink, requirements, requirements-toolbox MATLAB Answers — New Questions
Help with Plotting the Envelope of a Scatter Plot in MATLAB
Hello everyone,
I’m trying to plot the envelope of a scatter plot in MATLAB. However, when I use the envelope function, it returns the same plot without highlighting the upper or lower bounds.
This plot was created using a scatter plot, and when I attempt to filter it to retain only the extremum values, I end up with just a single slice of the data.
Has anyone encountered a similar issue or knows how I could extract and plot the envelope correctly (in red), as shown in the reference image?
Thanks in advance!Hello everyone,
I’m trying to plot the envelope of a scatter plot in MATLAB. However, when I use the envelope function, it returns the same plot without highlighting the upper or lower bounds.
This plot was created using a scatter plot, and when I attempt to filter it to retain only the extremum values, I end up with just a single slice of the data.
Has anyone encountered a similar issue or knows how I could extract and plot the envelope correctly (in red), as shown in the reference image?
Thanks in advance! Hello everyone,
I’m trying to plot the envelope of a scatter plot in MATLAB. However, when I use the envelope function, it returns the same plot without highlighting the upper or lower bounds.
This plot was created using a scatter plot, and when I attempt to filter it to retain only the extremum values, I end up with just a single slice of the data.
Has anyone encountered a similar issue or knows how I could extract and plot the envelope correctly (in red), as shown in the reference image?
Thanks in advance! scatter, enveloppe, filtering MATLAB Answers — New Questions
I AM TRYING TO CONTROL SYNCHRONOUS RELUCTANCE MOTOR USING FOC. (Rs) = 8 Ω, (Ld) = 1.2 H (Lq) = 0.1 H P= 2, (J) = 0.125 kg·m² , (B) = 0.009 IF THE SIMULATION DIAGRAM IS OK
Post Content Post Content synchronous reluctance motor using foc MATLAB Answers — New Questions
How Microsoft Graph PowerShell SDK Access Tokens Work
Automatic Management of Access Tokens
Some years ago, I wrote about the access (bearer) tokens used by Entra ID. At the time, I focused on the access tokens obtained by apps from https://login.microsoftonline.com rather than those used by the Microsoft Graph PowerShell SDK.
One of the big advantages of using the Microsoft Graph PowerShell SDK is that developers don’t need to manage token renewal. When a script or app runs the Connect-MgGraph cmdlet to authenticate, an access token is obtained to allow cmdlets to run. When that access token approaches its expiration time, the Graph SDK requests a new token automatically.
Unless you knew that automatic renewal happens, you probably won’t realize how the Graph PowerShell SDK acquires and manages access tokens because details of the access token aren’t surfaced by a cmdlet like Get-MgContext. Although Get-MgContext reveals details of the current authentication context such as whether delegated or app-only authentication was used and the scopes (permissions) available to the session, there’s no trace of the access token.
Finding the Access Token Used by a Microsoft Graph PowerShell SDK Interactive Session
Some might be surprised that it’s not easier to find what access token is being used during a Graph PowerShell SDK session. However, automatic token management means that knowing what an access token is and when the token will expire is not information that’s necessary for a session to function, so it’s reasonable to keep the data hidden behind the scenes.
To find the access token, it’s necessary to make a special form of request to any Graph API. The request can be made in an interactive session or an app-only session. This example uses a request against the drives endpoint to retrieve retention label information for a file, but any request will work:
$Uri = ("https://graph.microsoft.com/v1.0/drives/{0}/items/{1}/retentionLabel" -f $OneDriveInfo.Id, $File.Id)
$Data = Invoke-MgGraphRequest -Uri $Uri -Method Get -OutputType HttpResponseMessage
The key point here is that the Invoke-MgGraphRequest cmdlet specifies that it should receive a HTTP response rather than data. The request specified in the URI is simply a way to ask the Graph for the HTTP response. The response contains several interesting components:
Version : 2.0
Content : System.Net.Http.DecompressionHandler+GZipDecompressedContent
StatusCode : OK
ReasonPhrase : OK
Headers : {[Cache-Control, System.String[]], [Vary, System.String[]], [Strict-Transport-Security, System.String[]], [request-id, System.String[]]…}
TrailingHeaders : {}
RequestMessage : Method: GET, RequestUri: 'https://graph.microsoft.com/v1.0/drives/b!_xwZzApnQEeEWOYGdTfHR_FlEFWmBHl JixksigwWMZ_hpEW05Pd_R7OzPT4YdqXq/items/01R343MZ43HNLCSCCT3ZBLLUIJGB3GJ5B3/retentionLabel',
Version: 2.0, Content: <null>, Headers:
{
User-Agent: Mozilla/5.0
User-Agent: (Windows NT 10.0; Microsoft Windows 10.0.26100; en-IE)
User-Agent: PowerShell/7.5.2
User-Agent: Invoke-MgGraphRequest
FeatureFlag: 00000003
Cache-Control: no-store, no-cache
Authorization: Bearer eyJ0eXAiOiJKV1QiLCJub25jZSI6IlZrTmh0QjdFajZpSUhRVkRwdmZYeVVldUEyeFFBbFhyR1M
The access token is at the bottom of the output and can be retrieved with:
$Data.RequestMessage.Headers.Authorization.Parameter
Decrypting an Access Token
Isolating the access token makes it easier to copy and input into the jwt.io token decoder. Figure 1 shows the raw JSON output; selecting the claims table tab presents the information in a more easily understood fashion (this reference page also helps).
The decoded token reveals details like the app in use (Microsoft Graph Command Line Tools), its identifier, the user, and the available permissions
"app_displayname": "Microsoft Graph Command Line Tools", "appid": "14d82eec-204b-4c2f-b7e8-296a70dab67e", "family_name": "Redmond", "given_name": "Tony", "idtyp": "user", "ipaddr": "109.78.233.203", "name": "Tony Redmond", "oid": "eff4cd58-1bb8-4899-94de-795f656b4a18", "scp": "AccessReview.Read.All Agreement.Read.All Analytics.Read APIConnectors.Read.All Application.Read.All Application.ReadWrite.All AppRoleAssignment.ReadWrite.All AuditLog.Read.All AuditLogsQuery.Read.All ...
The token also contain timestamps in UNIX epoch format for when the token was issued and when it will expire. The claims table output shows the date in local time. You can also convert these dates with PowerShell:
$UnixEpochValue = 1752763429 $Date = [DateTimeOffset]::FromUnixTimeSeconds($UnixEpochValue).ToLocalTime().DateTime Write-Host "UNIX epoch $UnixEpochValue is" $(Get-Date $Date -format 'dd-MMM-yyyy HH:mm') UNIX epoch 1752763429 is 17-Jul-2025 15:43
Down further in the token you’ll find the wids array, which holds the identifiers for the Entra ID roles held by the user. Remember, during an interactive Graph SDK session the available permissions are the intersection between delegated permissions and administrative roles. In other words, if access to data isn’t available through a permission, it might be through a role.
Reusing a Graph Access Token
You can take the access token used by the Graph interactive session and use it to retrieve information without using a Graph SDK cmdlet. In this code snippet, we prepare a hash table containing the access token formatted in the way that Graph requests expect the data to be presented and use the token with the Invoke-RestMethod cmdlet to find the details of the signed-in user.
$Headers = @{}
$Headers.Add("Authorization", ("{0} {1}" -f $Data.RequestMessage.Headers.Authorization.Scheme, $Data.RequestMessage.Headers.Authorization.Parameter))
$Me = Invoke-RestMethod -Uri "https://graph.microsoft.com/v1.0/me" -Headers $Headers
$Me.displayName
Tony Redmond
Interesting But Not Very Useful Information
All of this is firmly in the interesting but not very useful category. If an app wants to make Graph API requests without using the Microsoft Graph PowerShell SDK, it will do the norm and obtain an access token programmatically before running any requests. The permissions available to the app are the set of delegated and application permissions held by the app’s service principal. If the app runs for over an hour, it will need to renew the access token.
Apart from testing code to write this article, I don’t think I have ever looked at the access token in a Microsoft Graph PowerShell SDK session. I might in the future, but right now I can’t think of a good reason why I should.
Need some assistance to write and manage PowerShell scripts for Microsoft 365? Get a copy of the Automating Microsoft 365 with PowerShell eBook, available standalone or as part of the Office 365 for IT Pros eBook bundle.
Error when applying coder to WLAN Toolbox example code
An error occurs when using Matlab coder with hWLANPacketDetector.m used in SDRBeaconReceiverExample.mlx.An error occurs when using Matlab coder with hWLANPacketDetector.m used in SDRBeaconReceiverExample.mlx. An error occurs when using Matlab coder with hWLANPacketDetector.m used in SDRBeaconReceiverExample.mlx. wlan coder MATLAB Answers — New Questions
Please guide me, I can’t find the F280049 Flash file, on TMS320F280049 when using Uniflash it will only work once, after reset it won’t work.
Please guide me, I can’t find the F280049 Flash file, on TMS320F280049 when using Uniflash it will only work once, after reset it won’t work. After reset, it might be from the MCU boosting from RAM?Please guide me, I can’t find the F280049 Flash file, on TMS320F280049 when using Uniflash it will only work once, after reset it won’t work. After reset, it might be from the MCU boosting from RAM? Please guide me, I can’t find the F280049 Flash file, on TMS320F280049 when using Uniflash it will only work once, after reset it won’t work. After reset, it might be from the MCU boosting from RAM? c2000, simulink MATLAB Answers — New Questions
Expansion of expressions involving Einstein summation convention with Levi-Civita tensors
Dear all
I am evaluating expressions of the type
where all the subindices , and the dot on the variable , , symbolizes the time derivative.
I’d be interested in being able to ask MatLab for the fully expanded , , and , so I can then numerically evaluate those terms. Ideally, it would be a plus if it also gave me the monstrous explicit expressions in LaTeX and/or could extrapolate the expanded expression to C++.
Do you have any ideas on how I could do this?Dear all
I am evaluating expressions of the type
where all the subindices , and the dot on the variable , , symbolizes the time derivative.
I’d be interested in being able to ask MatLab for the fully expanded , , and , so I can then numerically evaluate those terms. Ideally, it would be a plus if it also gave me the monstrous explicit expressions in LaTeX and/or could extrapolate the expanded expression to C++.
Do you have any ideas on how I could do this? Dear all
I am evaluating expressions of the type
where all the subindices , and the dot on the variable , , symbolizes the time derivative.
I’d be interested in being able to ask MatLab for the fully expanded , , and , so I can then numerically evaluate those terms. Ideally, it would be a plus if it also gave me the monstrous explicit expressions in LaTeX and/or could extrapolate the expanded expression to C++.
Do you have any ideas on how I could do this? symbolic to numeric expressions, einstein summation convention MATLAB Answers — New Questions
Resonance in moonpool type structures
I have the following code for my problem where i apply the match eigen function technique to find the unknown coefficients and then plotting one result wave motion inside a moonpool structure. However the expected plot for me should be decreasing after the resonance peak. But in my code it is decrease but then again blows up. I did not find any source of error. The analytical expressions which i used for my problem is write in pdf file at the end. Here is my code . I just want to figure out my source of error to fix it but i couldn’t. So I need help in acheiving my desired results. I also attached the refrence plot.
clear all;
close all;
clc;
tic;
% Parameters from your setup
N = 5; % Number of evanescent modes
M = 3; % Number of azimuthal modes
RE = 1.0; % Outer radius (m)
ratio = 2.0; % Ratio R_E / R_I
RI = RE / ratio; % Inner radius (m)
h = 4.0 * RE; % Water depth (m)
b = 2.0; % Distance from cylinder bottom to seabed (m)
d = h – b; % Interface depth (m)
g = 9.81; % Gravity (m/s^2)
l = 0; % Azimuthal order
% I_given_wavenumber = 1;
X_kRE = linspace(0.01, 4, 200);
% Initialize eta0 array before loop
eta0 = zeros(size(X_kRE)); % Preallocate
for idx = 1:length(X_kRE)
% if I_given_wavenumber == 1
wk = X_kRE(idx) / RE; % dimensional wavenumber
omega = sqrt(g * wk * tanh(wk * h)); % dispersion relation
fun_alpha = @(x) omega^2 + g*x.*tan(x*h); %dispersion equation for evanescent modes
for n = 1:N
if n == 1
k(n) = -1i * wk;
else
x0_left = (2*n – 3) * pi / (2*h);
x0_right = (2*n – 1) * pi / (2*h);
guess = (x0_left + x0_right)/2;
% Use lsqnonlin for better convergence
k(n) = lsqnonlin(fun_alpha, guess, x0_left, x0_right);
end
end
% % find evanescent modes k1…k_{N-1} stored in k_all(2)…k_all(N)
% for n = 2 : N
% m = n – 1; % mode index for evanescent
% a = ((2*m-1)*pi)/(2*h) + eps;
% b = (m*pi)/h – eps;
% % at a: tan→ -∞ so f(a)<0; at b: tan→0 so f(b)=+omega^2>0
% k_all(n) = fzero(@(k) g*k.*tan(k*h) + omega^2, [a, b]);
% end
% % % end
%% finding the root lemda_m =m*pi/b%%%%%%
for j=1:M
m(j)=(pi*(j-1))/b;
end
% %%%%%%%%%%%%%%%%%%%%% Define matrix A %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
A = zeros(M,N);
A(1,1) = (cosh(wk*h)*sinh(wk*b))/(wk*b*(2*wk*h+sinh(2*wk*h)))*(besselh(l, wk*RE)) /(dbesselh(l, wk*RE)); % Set A_00
for j = 2:N
A(1,j) =(cos(k(j)*h)*sin(k(j)*b) )/ (k(j)*b*(2*k(j)*h + sin(2*k(j)*h)))*( besselk(l, k(j)*RE) )…
/(dbesselk(l, k(j)*RE)); % Set A_0n
end
for i = 2:M
A(i,1) =(2 * cosh(wk*h) * (-1)^(i-1) * wk*sinh(wk*b)) / (b * (2*wk*h + sinh(2*wk*h))*(wk^2 + m(i)^2))…
* (besselh(l, wk*RE))/ (dbesselh(l, wk*RE)); % Set A_m0
end
for i = 2:M
for j = 2:N
A(i,j) = (2*cos(k(j)*h)*(-1)^(i-1) *k(j)* sin(k(j)*b))/ (b * (2*k(j)*h + sin(2*k(j)*h))*(k(j)^2-m(i)^2))…
*(besselk(l, k(j)*RE)) / (dbesselk(l, k(j)*RE));% Set A_mn
end
end
%%%%%%%%%%%%%%%%%%%%%%%% DefineMatrix B %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
B=zeros(N,M);
B(1,1) = (4*sinh(wk*b)) / (RE * wk*log(RE/RI) *cosh(wk*h)); %set B_00
%%%%%%%%%%%B_0m%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for j = 2:M
Rml_prime_RE = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RE) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
B(1,j) = Rml_prime_RE * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
%%%%%%%%%%%B_n0%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=2:N
B(i,1)=(4*sin(k(i)*b))/(RE*k(i)*log(RE/RI)*cos(k(i)*h));% Set B_n0
end
%%%%%%%%%%%%%%%%%%%%%%%%%%B_nm%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=2:N
for j=2:M
Rml_prime_RE = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RE) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
B(i,j) = Rml_prime_RE * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Define Matrix C %%%%%%%%%%%%%%%%%%%%%%%%
C = zeros(N,M);
C(1,1) = -4 * sinh(wk*b) / (RE * wk*log(RE/RI) * cosh(wk*h));
% %%%%% DEfine C0m%%%%%%
for j = 2:M
Rml_prime_star_RE = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RE) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
C(1,j) = Rml_prime_star_RE * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
% %%%%%%% Define Cn0%%%%%%
for i = 2:N
C(i,1) = -4 * sin(k(i)*b) / (RE *k(i)* log(RE/RI) * cos(k(i)*h));
end
% % %%%%%% Define Cnm%%%%%%
for i = 2:N
for j =2:M
Rml_prime_star_RE = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RE) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
C(i,j) = Rml_prime_star_RE * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%% write Matrix D %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
D = zeros(M,N);
%%% write D00 %%%%%%
D(1,1) = (cosh(wk*h) * sinh(wk*b)) / (wk*b * (2*wk*h + sinh(2*wk*h))) * (besselj(l, wk*RI))/ (dbesselj(l, wk*RI));
%%%%% write D0n %%%%%%%%%%
for j =2:N
D(1,j) = (cos(k(j)*h) * sin(k(j)*b)) / (k(j)*b * (2*k(j)*h + sin(2*k(j)*h))) * (besseli(l, k(j)*RI) )/ (dbesseli(l, k(j)*RI));
end
%%%%%% write Dm0 %%%%%%%%%
for i = 2:M
D(i,1) = (2 * cosh(wk*h) * (-1)^(i-1) * wk * sinh(wk*b)) /(b * (2*wk*h + sinh(2*wk*h)) * (wk^2 + m(i)^2))…
*(besselj(l, wk*RI) )/(dbesselj(l, wk*RI));
end
%%%%% Define Dmn%%%%%%%%
for i = 2:M
for j = 2:N
D(i,j) = (2 * cos(k(j)*h) * (-1)^(i-1) * k(j) * sin(k(j)*b)) /(b * (2*k(j)*h + sin(2*k(j)*h)) * (k(j)^2 – m(i)^2))…
*(besseli(l, k(j)*RI)) / (dbesseli(l, k(j)*RI));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%% Define Matrix E %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
E = zeros(N, M); % Preallocate the matrix E
%%%%% Define E00%%%%%%%%%%%%
E(1,1) = (4 * sinh(wk*b)) / (RI *wk* log(RE/RI) * cosh(wk*h));
%%%% Define Eom %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for j = 2:M
Rml_prime_RI = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RI) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RI))…
/ (besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
E(1,j) = Rml_prime_RI * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
%
end
%%%%%% Define Eno%%%%%%%%%%%%%%
for i = 2:N
E(i,1) = (4 * sin(k(i)*b)) / (RI *k(i)* log(RE/RI) * cos(k(i)*h));
end
for i = 2:N
for j = 2:M
Rml_prime_RI = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RI) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RI))…
/ (besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
E(i,j) = Rml_prime_RI * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%%%%%%%%%%%%%%%%%%% Now write matrix F %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
F = zeros(N,M);
%%%%%%%% Define F00 %%%%%%%%%%
F(1,1) = (-4 * sinh(wk*b)) / (RI * wk*log(RE/RI) * cosh(wk*h));
% %%%%%% Define F0m%%%%
for j = 2:M
Rml_star_prime_RI = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RI) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RI))/((besselk(l, m(j)*RI) …
* besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI)));
F(1,j) = Rml_star_prime_RI * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
%%%%% Defin Fn0%%%%%%
for i = 2:N
F(i,1) = (-4 * sin(k(i)*b)) / (RI *k(i)* log(RE/RI) * cos(k(i)*h));
end
%%%%%%% Define Fnm %%%%%%%
for i = 2:N
for j = 2:M
Rml_star_prime_RI = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RI) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RI))/((besselk(l, m(j)*RI) …
* besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI)));
F(i,j) = Rml_star_prime_RI * (4 * k(i)* (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % Define right-hand side vector G
G = zeros(2*M+2*N, 1); % Total length = M + N+ M+ N = 2M+2N
%
% Block 1: G (size M = 4)
for i = 1:M
if i == 1
G(i, 1) = (sinh(wk*b))/((b*wk)*cosh(wk*h))*besselj(l, wk*RE) ; % Z_0^l
else
G(i, 1) = (2 *wk*(-1)^(i-1)*sinh(wk*b))/(b *(wk^2 +m(i)^2))*besselj(l, wk*RE); %Z_m^l
end
end
%
% Block 2: Y (size N = 3)
for j = 1:N
if j == 1
G(j + M, 1) = -dbesselj(l, wk*RE) * (2*wk*h + sinh(2*wk*h)) /(cosh(wk*h)^2); % Y_0^l
else
G(j + M, 1) = 0; % Y_n^l
end
end
% Block 3: X (size M = 4)
for i = 1:M
G(i + M + N, 1) = 0; % X_0^l, X_m^l
end
% Block 4: W (size N = 3)
for j = 1:N
G(j + M + N + M, 1) = 0; % W_0^l, W_n^l
end
% Identity matrices
I_M = eye(M);
I_N = eye(N);
% Zero matrices
ZMM = zeros(M, M);
ZMN = zeros(M, N);
ZNM = zeros(N, M);
ZNN = zeros(N, N);
% Construct the full block matrix H
H = [ I_M, -A, ZMM, ZMN; % Block row 1
-B, I_N, -C, ZNN; % Block row 2
ZMM, ZMN, I_M, -D; % Block row 3
-E, ZNN, -F, I_N]; % Block row 4
% X = H G; % Solve the linear system
X = pinv(H) * G; % Use pseudoinverse for stability
%
b_vec = X(1:M); % Coefficients b^l
a_vec = X(M+1 : M+N); % Coefficients a^l
c_vec = X(M+N+1 : 2*M+N); % Coefficients c^l
d_vec = X(2*M+N+1 : end); % Coefficients d^l
%%%%%%%%% Wave motion%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
term1 = (d_vec(1)*cosh(wk*h)^2) / ((2*wk*h + sinh(2*wk*h)) * dbesselj(l, wk*RI));
sum1 = 0;
for n =2:N
sum1 = sum1 + d_vec(n)*cos(k(n)*h)^2/ ((2*k(n)*h + sin(2*k(n)*h))* dbesseli(l, k(n)*RI));
end
eta0(idx) = abs(term1+sum1);
% % disp(k.’) % Should all be real and positive for n ≥ 2
% %
% %
end
plot(X_kRE, eta0, ‘k’, ‘LineWidth’, 1.5);
xlabel(‘$k_0 R_E$’, ‘Interpreter’, ‘latex’);
ylabel(‘$eta / (iA)$’, ‘Interpreter’, ‘latex’);
title(‘Wave motion amplitude for RE/RI = 4.0’);
grid on;
xlim([0 4]); % Match the reference plot
ylim([0 6]); % Optional: based on expected peak height
elapsedTime = toc;
disp([‘Time consuming = ‘, num2str(elapsedTime), ‘ s’]);
function out = dbesselh(l, z)
if l == 0
out = -besselh(1, 1, z);
else
out = 0.5 * (besselh(l – 1, 1, z) – besselh(l + 1, 1, z));
end
end
function out = dbesseli(l, z)
if l == 0
out = besseli(1, z);
else
out = 0.5 * (besseli(l – 1, z) + besseli(l + 1, z));
end
end
function out = dbesselj(l, z)
if l == 0
out = -besselj(1, z);
else
out = 0.5 * (besselj(l – 1, z) – besselj(l + 1, z));
end
end
function out = dbesselk(l, z)
if l == 0
out = -besselk(1, z);
else
out = -0.5 * (besselk(l – 1, z) + besselk(l + 1, z));
end
endI have the following code for my problem where i apply the match eigen function technique to find the unknown coefficients and then plotting one result wave motion inside a moonpool structure. However the expected plot for me should be decreasing after the resonance peak. But in my code it is decrease but then again blows up. I did not find any source of error. The analytical expressions which i used for my problem is write in pdf file at the end. Here is my code . I just want to figure out my source of error to fix it but i couldn’t. So I need help in acheiving my desired results. I also attached the refrence plot.
clear all;
close all;
clc;
tic;
% Parameters from your setup
N = 5; % Number of evanescent modes
M = 3; % Number of azimuthal modes
RE = 1.0; % Outer radius (m)
ratio = 2.0; % Ratio R_E / R_I
RI = RE / ratio; % Inner radius (m)
h = 4.0 * RE; % Water depth (m)
b = 2.0; % Distance from cylinder bottom to seabed (m)
d = h – b; % Interface depth (m)
g = 9.81; % Gravity (m/s^2)
l = 0; % Azimuthal order
% I_given_wavenumber = 1;
X_kRE = linspace(0.01, 4, 200);
% Initialize eta0 array before loop
eta0 = zeros(size(X_kRE)); % Preallocate
for idx = 1:length(X_kRE)
% if I_given_wavenumber == 1
wk = X_kRE(idx) / RE; % dimensional wavenumber
omega = sqrt(g * wk * tanh(wk * h)); % dispersion relation
fun_alpha = @(x) omega^2 + g*x.*tan(x*h); %dispersion equation for evanescent modes
for n = 1:N
if n == 1
k(n) = -1i * wk;
else
x0_left = (2*n – 3) * pi / (2*h);
x0_right = (2*n – 1) * pi / (2*h);
guess = (x0_left + x0_right)/2;
% Use lsqnonlin for better convergence
k(n) = lsqnonlin(fun_alpha, guess, x0_left, x0_right);
end
end
% % find evanescent modes k1…k_{N-1} stored in k_all(2)…k_all(N)
% for n = 2 : N
% m = n – 1; % mode index for evanescent
% a = ((2*m-1)*pi)/(2*h) + eps;
% b = (m*pi)/h – eps;
% % at a: tan→ -∞ so f(a)<0; at b: tan→0 so f(b)=+omega^2>0
% k_all(n) = fzero(@(k) g*k.*tan(k*h) + omega^2, [a, b]);
% end
% % % end
%% finding the root lemda_m =m*pi/b%%%%%%
for j=1:M
m(j)=(pi*(j-1))/b;
end
% %%%%%%%%%%%%%%%%%%%%% Define matrix A %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
A = zeros(M,N);
A(1,1) = (cosh(wk*h)*sinh(wk*b))/(wk*b*(2*wk*h+sinh(2*wk*h)))*(besselh(l, wk*RE)) /(dbesselh(l, wk*RE)); % Set A_00
for j = 2:N
A(1,j) =(cos(k(j)*h)*sin(k(j)*b) )/ (k(j)*b*(2*k(j)*h + sin(2*k(j)*h)))*( besselk(l, k(j)*RE) )…
/(dbesselk(l, k(j)*RE)); % Set A_0n
end
for i = 2:M
A(i,1) =(2 * cosh(wk*h) * (-1)^(i-1) * wk*sinh(wk*b)) / (b * (2*wk*h + sinh(2*wk*h))*(wk^2 + m(i)^2))…
* (besselh(l, wk*RE))/ (dbesselh(l, wk*RE)); % Set A_m0
end
for i = 2:M
for j = 2:N
A(i,j) = (2*cos(k(j)*h)*(-1)^(i-1) *k(j)* sin(k(j)*b))/ (b * (2*k(j)*h + sin(2*k(j)*h))*(k(j)^2-m(i)^2))…
*(besselk(l, k(j)*RE)) / (dbesselk(l, k(j)*RE));% Set A_mn
end
end
%%%%%%%%%%%%%%%%%%%%%%%% DefineMatrix B %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
B=zeros(N,M);
B(1,1) = (4*sinh(wk*b)) / (RE * wk*log(RE/RI) *cosh(wk*h)); %set B_00
%%%%%%%%%%%B_0m%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for j = 2:M
Rml_prime_RE = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RE) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
B(1,j) = Rml_prime_RE * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
%%%%%%%%%%%B_n0%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=2:N
B(i,1)=(4*sin(k(i)*b))/(RE*k(i)*log(RE/RI)*cos(k(i)*h));% Set B_n0
end
%%%%%%%%%%%%%%%%%%%%%%%%%%B_nm%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=2:N
for j=2:M
Rml_prime_RE = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RE) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
B(i,j) = Rml_prime_RE * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Define Matrix C %%%%%%%%%%%%%%%%%%%%%%%%
C = zeros(N,M);
C(1,1) = -4 * sinh(wk*b) / (RE * wk*log(RE/RI) * cosh(wk*h));
% %%%%% DEfine C0m%%%%%%
for j = 2:M
Rml_prime_star_RE = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RE) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
C(1,j) = Rml_prime_star_RE * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
% %%%%%%% Define Cn0%%%%%%
for i = 2:N
C(i,1) = -4 * sin(k(i)*b) / (RE *k(i)* log(RE/RI) * cos(k(i)*h));
end
% % %%%%%% Define Cnm%%%%%%
for i = 2:N
for j =2:M
Rml_prime_star_RE = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RE) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
C(i,j) = Rml_prime_star_RE * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%% write Matrix D %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
D = zeros(M,N);
%%% write D00 %%%%%%
D(1,1) = (cosh(wk*h) * sinh(wk*b)) / (wk*b * (2*wk*h + sinh(2*wk*h))) * (besselj(l, wk*RI))/ (dbesselj(l, wk*RI));
%%%%% write D0n %%%%%%%%%%
for j =2:N
D(1,j) = (cos(k(j)*h) * sin(k(j)*b)) / (k(j)*b * (2*k(j)*h + sin(2*k(j)*h))) * (besseli(l, k(j)*RI) )/ (dbesseli(l, k(j)*RI));
end
%%%%%% write Dm0 %%%%%%%%%
for i = 2:M
D(i,1) = (2 * cosh(wk*h) * (-1)^(i-1) * wk * sinh(wk*b)) /(b * (2*wk*h + sinh(2*wk*h)) * (wk^2 + m(i)^2))…
*(besselj(l, wk*RI) )/(dbesselj(l, wk*RI));
end
%%%%% Define Dmn%%%%%%%%
for i = 2:M
for j = 2:N
D(i,j) = (2 * cos(k(j)*h) * (-1)^(i-1) * k(j) * sin(k(j)*b)) /(b * (2*k(j)*h + sin(2*k(j)*h)) * (k(j)^2 – m(i)^2))…
*(besseli(l, k(j)*RI)) / (dbesseli(l, k(j)*RI));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%% Define Matrix E %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
E = zeros(N, M); % Preallocate the matrix E
%%%%% Define E00%%%%%%%%%%%%
E(1,1) = (4 * sinh(wk*b)) / (RI *wk* log(RE/RI) * cosh(wk*h));
%%%% Define Eom %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for j = 2:M
Rml_prime_RI = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RI) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RI))…
/ (besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
E(1,j) = Rml_prime_RI * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
%
end
%%%%%% Define Eno%%%%%%%%%%%%%%
for i = 2:N
E(i,1) = (4 * sin(k(i)*b)) / (RI *k(i)* log(RE/RI) * cos(k(i)*h));
end
for i = 2:N
for j = 2:M
Rml_prime_RI = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RI) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RI))…
/ (besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
E(i,j) = Rml_prime_RI * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%%%%%%%%%%%%%%%%%%% Now write matrix F %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
F = zeros(N,M);
%%%%%%%% Define F00 %%%%%%%%%%
F(1,1) = (-4 * sinh(wk*b)) / (RI * wk*log(RE/RI) * cosh(wk*h));
% %%%%%% Define F0m%%%%
for j = 2:M
Rml_star_prime_RI = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RI) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RI))/((besselk(l, m(j)*RI) …
* besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI)));
F(1,j) = Rml_star_prime_RI * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
%%%%% Defin Fn0%%%%%%
for i = 2:N
F(i,1) = (-4 * sin(k(i)*b)) / (RI *k(i)* log(RE/RI) * cos(k(i)*h));
end
%%%%%%% Define Fnm %%%%%%%
for i = 2:N
for j = 2:M
Rml_star_prime_RI = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RI) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RI))/((besselk(l, m(j)*RI) …
* besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI)));
F(i,j) = Rml_star_prime_RI * (4 * k(i)* (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % Define right-hand side vector G
G = zeros(2*M+2*N, 1); % Total length = M + N+ M+ N = 2M+2N
%
% Block 1: G (size M = 4)
for i = 1:M
if i == 1
G(i, 1) = (sinh(wk*b))/((b*wk)*cosh(wk*h))*besselj(l, wk*RE) ; % Z_0^l
else
G(i, 1) = (2 *wk*(-1)^(i-1)*sinh(wk*b))/(b *(wk^2 +m(i)^2))*besselj(l, wk*RE); %Z_m^l
end
end
%
% Block 2: Y (size N = 3)
for j = 1:N
if j == 1
G(j + M, 1) = -dbesselj(l, wk*RE) * (2*wk*h + sinh(2*wk*h)) /(cosh(wk*h)^2); % Y_0^l
else
G(j + M, 1) = 0; % Y_n^l
end
end
% Block 3: X (size M = 4)
for i = 1:M
G(i + M + N, 1) = 0; % X_0^l, X_m^l
end
% Block 4: W (size N = 3)
for j = 1:N
G(j + M + N + M, 1) = 0; % W_0^l, W_n^l
end
% Identity matrices
I_M = eye(M);
I_N = eye(N);
% Zero matrices
ZMM = zeros(M, M);
ZMN = zeros(M, N);
ZNM = zeros(N, M);
ZNN = zeros(N, N);
% Construct the full block matrix H
H = [ I_M, -A, ZMM, ZMN; % Block row 1
-B, I_N, -C, ZNN; % Block row 2
ZMM, ZMN, I_M, -D; % Block row 3
-E, ZNN, -F, I_N]; % Block row 4
% X = H G; % Solve the linear system
X = pinv(H) * G; % Use pseudoinverse for stability
%
b_vec = X(1:M); % Coefficients b^l
a_vec = X(M+1 : M+N); % Coefficients a^l
c_vec = X(M+N+1 : 2*M+N); % Coefficients c^l
d_vec = X(2*M+N+1 : end); % Coefficients d^l
%%%%%%%%% Wave motion%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
term1 = (d_vec(1)*cosh(wk*h)^2) / ((2*wk*h + sinh(2*wk*h)) * dbesselj(l, wk*RI));
sum1 = 0;
for n =2:N
sum1 = sum1 + d_vec(n)*cos(k(n)*h)^2/ ((2*k(n)*h + sin(2*k(n)*h))* dbesseli(l, k(n)*RI));
end
eta0(idx) = abs(term1+sum1);
% % disp(k.’) % Should all be real and positive for n ≥ 2
% %
% %
end
plot(X_kRE, eta0, ‘k’, ‘LineWidth’, 1.5);
xlabel(‘$k_0 R_E$’, ‘Interpreter’, ‘latex’);
ylabel(‘$eta / (iA)$’, ‘Interpreter’, ‘latex’);
title(‘Wave motion amplitude for RE/RI = 4.0’);
grid on;
xlim([0 4]); % Match the reference plot
ylim([0 6]); % Optional: based on expected peak height
elapsedTime = toc;
disp([‘Time consuming = ‘, num2str(elapsedTime), ‘ s’]);
function out = dbesselh(l, z)
if l == 0
out = -besselh(1, 1, z);
else
out = 0.5 * (besselh(l – 1, 1, z) – besselh(l + 1, 1, z));
end
end
function out = dbesseli(l, z)
if l == 0
out = besseli(1, z);
else
out = 0.5 * (besseli(l – 1, z) + besseli(l + 1, z));
end
end
function out = dbesselj(l, z)
if l == 0
out = -besselj(1, z);
else
out = 0.5 * (besselj(l – 1, z) – besselj(l + 1, z));
end
end
function out = dbesselk(l, z)
if l == 0
out = -besselk(1, z);
else
out = -0.5 * (besselk(l – 1, z) + besselk(l + 1, z));
end
end I have the following code for my problem where i apply the match eigen function technique to find the unknown coefficients and then plotting one result wave motion inside a moonpool structure. However the expected plot for me should be decreasing after the resonance peak. But in my code it is decrease but then again blows up. I did not find any source of error. The analytical expressions which i used for my problem is write in pdf file at the end. Here is my code . I just want to figure out my source of error to fix it but i couldn’t. So I need help in acheiving my desired results. I also attached the refrence plot.
clear all;
close all;
clc;
tic;
% Parameters from your setup
N = 5; % Number of evanescent modes
M = 3; % Number of azimuthal modes
RE = 1.0; % Outer radius (m)
ratio = 2.0; % Ratio R_E / R_I
RI = RE / ratio; % Inner radius (m)
h = 4.0 * RE; % Water depth (m)
b = 2.0; % Distance from cylinder bottom to seabed (m)
d = h – b; % Interface depth (m)
g = 9.81; % Gravity (m/s^2)
l = 0; % Azimuthal order
% I_given_wavenumber = 1;
X_kRE = linspace(0.01, 4, 200);
% Initialize eta0 array before loop
eta0 = zeros(size(X_kRE)); % Preallocate
for idx = 1:length(X_kRE)
% if I_given_wavenumber == 1
wk = X_kRE(idx) / RE; % dimensional wavenumber
omega = sqrt(g * wk * tanh(wk * h)); % dispersion relation
fun_alpha = @(x) omega^2 + g*x.*tan(x*h); %dispersion equation for evanescent modes
for n = 1:N
if n == 1
k(n) = -1i * wk;
else
x0_left = (2*n – 3) * pi / (2*h);
x0_right = (2*n – 1) * pi / (2*h);
guess = (x0_left + x0_right)/2;
% Use lsqnonlin for better convergence
k(n) = lsqnonlin(fun_alpha, guess, x0_left, x0_right);
end
end
% % find evanescent modes k1…k_{N-1} stored in k_all(2)…k_all(N)
% for n = 2 : N
% m = n – 1; % mode index for evanescent
% a = ((2*m-1)*pi)/(2*h) + eps;
% b = (m*pi)/h – eps;
% % at a: tan→ -∞ so f(a)<0; at b: tan→0 so f(b)=+omega^2>0
% k_all(n) = fzero(@(k) g*k.*tan(k*h) + omega^2, [a, b]);
% end
% % % end
%% finding the root lemda_m =m*pi/b%%%%%%
for j=1:M
m(j)=(pi*(j-1))/b;
end
% %%%%%%%%%%%%%%%%%%%%% Define matrix A %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
A = zeros(M,N);
A(1,1) = (cosh(wk*h)*sinh(wk*b))/(wk*b*(2*wk*h+sinh(2*wk*h)))*(besselh(l, wk*RE)) /(dbesselh(l, wk*RE)); % Set A_00
for j = 2:N
A(1,j) =(cos(k(j)*h)*sin(k(j)*b) )/ (k(j)*b*(2*k(j)*h + sin(2*k(j)*h)))*( besselk(l, k(j)*RE) )…
/(dbesselk(l, k(j)*RE)); % Set A_0n
end
for i = 2:M
A(i,1) =(2 * cosh(wk*h) * (-1)^(i-1) * wk*sinh(wk*b)) / (b * (2*wk*h + sinh(2*wk*h))*(wk^2 + m(i)^2))…
* (besselh(l, wk*RE))/ (dbesselh(l, wk*RE)); % Set A_m0
end
for i = 2:M
for j = 2:N
A(i,j) = (2*cos(k(j)*h)*(-1)^(i-1) *k(j)* sin(k(j)*b))/ (b * (2*k(j)*h + sin(2*k(j)*h))*(k(j)^2-m(i)^2))…
*(besselk(l, k(j)*RE)) / (dbesselk(l, k(j)*RE));% Set A_mn
end
end
%%%%%%%%%%%%%%%%%%%%%%%% DefineMatrix B %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
B=zeros(N,M);
B(1,1) = (4*sinh(wk*b)) / (RE * wk*log(RE/RI) *cosh(wk*h)); %set B_00
%%%%%%%%%%%B_0m%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for j = 2:M
Rml_prime_RE = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RE) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
B(1,j) = Rml_prime_RE * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
%%%%%%%%%%%B_n0%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=2:N
B(i,1)=(4*sin(k(i)*b))/(RE*k(i)*log(RE/RI)*cos(k(i)*h));% Set B_n0
end
%%%%%%%%%%%%%%%%%%%%%%%%%%B_nm%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=2:N
for j=2:M
Rml_prime_RE = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RE) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
B(i,j) = Rml_prime_RE * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Define Matrix C %%%%%%%%%%%%%%%%%%%%%%%%
C = zeros(N,M);
C(1,1) = -4 * sinh(wk*b) / (RE * wk*log(RE/RI) * cosh(wk*h));
% %%%%% DEfine C0m%%%%%%
for j = 2:M
Rml_prime_star_RE = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RE) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
C(1,j) = Rml_prime_star_RE * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
% %%%%%%% Define Cn0%%%%%%
for i = 2:N
C(i,1) = -4 * sin(k(i)*b) / (RE *k(i)* log(RE/RI) * cos(k(i)*h));
end
% % %%%%%% Define Cnm%%%%%%
for i = 2:N
for j =2:M
Rml_prime_star_RE = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RE) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RE))…
/(besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
C(i,j) = Rml_prime_star_RE * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%% write Matrix D %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
D = zeros(M,N);
%%% write D00 %%%%%%
D(1,1) = (cosh(wk*h) * sinh(wk*b)) / (wk*b * (2*wk*h + sinh(2*wk*h))) * (besselj(l, wk*RI))/ (dbesselj(l, wk*RI));
%%%%% write D0n %%%%%%%%%%
for j =2:N
D(1,j) = (cos(k(j)*h) * sin(k(j)*b)) / (k(j)*b * (2*k(j)*h + sin(2*k(j)*h))) * (besseli(l, k(j)*RI) )/ (dbesseli(l, k(j)*RI));
end
%%%%%% write Dm0 %%%%%%%%%
for i = 2:M
D(i,1) = (2 * cosh(wk*h) * (-1)^(i-1) * wk * sinh(wk*b)) /(b * (2*wk*h + sinh(2*wk*h)) * (wk^2 + m(i)^2))…
*(besselj(l, wk*RI) )/(dbesselj(l, wk*RI));
end
%%%%% Define Dmn%%%%%%%%
for i = 2:M
for j = 2:N
D(i,j) = (2 * cos(k(j)*h) * (-1)^(i-1) * k(j) * sin(k(j)*b)) /(b * (2*k(j)*h + sin(2*k(j)*h)) * (k(j)^2 – m(i)^2))…
*(besseli(l, k(j)*RI)) / (dbesseli(l, k(j)*RI));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%% Define Matrix E %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
E = zeros(N, M); % Preallocate the matrix E
%%%%% Define E00%%%%%%%%%%%%
E(1,1) = (4 * sinh(wk*b)) / (RI *wk* log(RE/RI) * cosh(wk*h));
%%%% Define Eom %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for j = 2:M
Rml_prime_RI = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RI) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RI))…
/ (besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
E(1,j) = Rml_prime_RI * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
%
end
%%%%%% Define Eno%%%%%%%%%%%%%%
for i = 2:N
E(i,1) = (4 * sin(k(i)*b)) / (RI *k(i)* log(RE/RI) * cos(k(i)*h));
end
for i = 2:N
for j = 2:M
Rml_prime_RI = m(j)*(besselk(l, m(j)*RI) * dbesseli(l, m(j)*RI) …
– besseli(l, m(j)*RI) * dbesselk(l, m(j)*RI))…
/ (besselk(l, m(j)*RI) * besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI));
E(i,j) = Rml_prime_RI * (4 * k(i) * (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
%%%%%%%%%%%%%%%%%%%%%%%% Now write matrix F %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
F = zeros(N,M);
%%%%%%%% Define F00 %%%%%%%%%%
F(1,1) = (-4 * sinh(wk*b)) / (RI * wk*log(RE/RI) * cosh(wk*h));
% %%%%%% Define F0m%%%%
for j = 2:M
Rml_star_prime_RI = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RI) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RI))/((besselk(l, m(j)*RI) …
* besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI)));
F(1,j) = Rml_star_prime_RI * (4 * wk * (-1)^(j-1) * sinh(wk*b)) / (cosh(wk*h) * (wk^2 + m(j)^2));
end
%%%%% Defin Fn0%%%%%%
for i = 2:N
F(i,1) = (-4 * sin(k(i)*b)) / (RI *k(i)* log(RE/RI) * cos(k(i)*h));
end
%%%%%%% Define Fnm %%%%%%%
for i = 2:N
for j = 2:M
Rml_star_prime_RI = m(j)*(besseli(l, m(j)*RE) * dbesselk(l, m(j)*RI) …
– besselk(l, m(j)*RE) * dbesseli(l, m(j)*RI))/((besselk(l, m(j)*RI) …
* besseli(l, m(j)*RE) – besselk(l, m(j)*RE) * besseli(l, m(j)*RI)));
F(i,j) = Rml_star_prime_RI * (4 * k(i)* (-1)^(j-1) * sin(k(i)*b)) / (cos(k(i)*h) * (k(i)^2 – m(j)^2));
end
end
% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % Define right-hand side vector G
G = zeros(2*M+2*N, 1); % Total length = M + N+ M+ N = 2M+2N
%
% Block 1: G (size M = 4)
for i = 1:M
if i == 1
G(i, 1) = (sinh(wk*b))/((b*wk)*cosh(wk*h))*besselj(l, wk*RE) ; % Z_0^l
else
G(i, 1) = (2 *wk*(-1)^(i-1)*sinh(wk*b))/(b *(wk^2 +m(i)^2))*besselj(l, wk*RE); %Z_m^l
end
end
%
% Block 2: Y (size N = 3)
for j = 1:N
if j == 1
G(j + M, 1) = -dbesselj(l, wk*RE) * (2*wk*h + sinh(2*wk*h)) /(cosh(wk*h)^2); % Y_0^l
else
G(j + M, 1) = 0; % Y_n^l
end
end
% Block 3: X (size M = 4)
for i = 1:M
G(i + M + N, 1) = 0; % X_0^l, X_m^l
end
% Block 4: W (size N = 3)
for j = 1:N
G(j + M + N + M, 1) = 0; % W_0^l, W_n^l
end
% Identity matrices
I_M = eye(M);
I_N = eye(N);
% Zero matrices
ZMM = zeros(M, M);
ZMN = zeros(M, N);
ZNM = zeros(N, M);
ZNN = zeros(N, N);
% Construct the full block matrix H
H = [ I_M, -A, ZMM, ZMN; % Block row 1
-B, I_N, -C, ZNN; % Block row 2
ZMM, ZMN, I_M, -D; % Block row 3
-E, ZNN, -F, I_N]; % Block row 4
% X = H G; % Solve the linear system
X = pinv(H) * G; % Use pseudoinverse for stability
%
b_vec = X(1:M); % Coefficients b^l
a_vec = X(M+1 : M+N); % Coefficients a^l
c_vec = X(M+N+1 : 2*M+N); % Coefficients c^l
d_vec = X(2*M+N+1 : end); % Coefficients d^l
%%%%%%%%% Wave motion%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
term1 = (d_vec(1)*cosh(wk*h)^2) / ((2*wk*h + sinh(2*wk*h)) * dbesselj(l, wk*RI));
sum1 = 0;
for n =2:N
sum1 = sum1 + d_vec(n)*cos(k(n)*h)^2/ ((2*k(n)*h + sin(2*k(n)*h))* dbesseli(l, k(n)*RI));
end
eta0(idx) = abs(term1+sum1);
% % disp(k.’) % Should all be real and positive for n ≥ 2
% %
% %
end
plot(X_kRE, eta0, ‘k’, ‘LineWidth’, 1.5);
xlabel(‘$k_0 R_E$’, ‘Interpreter’, ‘latex’);
ylabel(‘$eta / (iA)$’, ‘Interpreter’, ‘latex’);
title(‘Wave motion amplitude for RE/RI = 4.0’);
grid on;
xlim([0 4]); % Match the reference plot
ylim([0 6]); % Optional: based on expected peak height
elapsedTime = toc;
disp([‘Time consuming = ‘, num2str(elapsedTime), ‘ s’]);
function out = dbesselh(l, z)
if l == 0
out = -besselh(1, 1, z);
else
out = 0.5 * (besselh(l – 1, 1, z) – besselh(l + 1, 1, z));
end
end
function out = dbesseli(l, z)
if l == 0
out = besseli(1, z);
else
out = 0.5 * (besseli(l – 1, z) + besseli(l + 1, z));
end
end
function out = dbesselj(l, z)
if l == 0
out = -besselj(1, z);
else
out = 0.5 * (besselj(l – 1, z) – besselj(l + 1, z));
end
end
function out = dbesselk(l, z)
if l == 0
out = -besselk(1, z);
else
out = -0.5 * (besselk(l – 1, z) + besselk(l + 1, z));
end
end moonpool, resonance, system of linear equation, semi analytical solution, wave motion MATLAB Answers — New Questions
How we can calculculate the MTF from PSF?
I want to calculate the MTF from this PSF.I want to calculate the MTF from this PSF. I want to calculate the MTF from this PSF. frequency MATLAB Answers — New Questions
The provided solution is coming up as incorrect.
I am currently doing the MATLAB onramp course and I am on task 6 of the final project Stellar moon 2 and I cant figure out the answer. When I select the solution I copied it and put it as my answer however it comes up as incorrect. This is beyond frusterating as I am unable to progress and I have tried deleting everything and trying again several times and I just want to finish this course. Can someone please help.I am currently doing the MATLAB onramp course and I am on task 6 of the final project Stellar moon 2 and I cant figure out the answer. When I select the solution I copied it and put it as my answer however it comes up as incorrect. This is beyond frusterating as I am unable to progress and I have tried deleting everything and trying again several times and I just want to finish this course. Can someone please help. I am currently doing the MATLAB onramp course and I am on task 6 of the final project Stellar moon 2 and I cant figure out the answer. When I select the solution I copied it and put it as my answer however it comes up as incorrect. This is beyond frusterating as I am unable to progress and I have tried deleting everything and trying again several times and I just want to finish this course. Can someone please help. solve, onramp MATLAB Answers — New Questions
speeding up the data analysis
I am working on a project where I have to analyse high frequency wind data (50 Hz) coming from a wind turbine data measurement system. this amount of data must be converted from .dat binary files to .mat files which I can use in matlab. The data must then be filtered and then averaged over 10 minutes to be compared to the data from another measurement system. Doing all this requires the analysis of thousands of data and it’s very time consuming (right now it is about 105 seconds just for the data of 2 days). How can I speed the process up?
for ii = 3:length(filedir)
filename = filedir(ii).name;
newdata = ReadFamosDataIntoTimeTable(filename);
% filter
IsConsidered = newdata.avbladeangleGRe<40 … % normal operation
& newdata.RAWS9>0.5 … % good availability
& ~isnan(newdata.iv10mswindspeed2GRe) & newdata.av100msabswinddirectionGRe>180 & newdata.av100msabswinddirectionGRe<250;
TurbineData(ii-2).date = filename;
TurbineData(ii-2).iv10mswindspeed2GRe = mean(newdata.iv10mswindspeed2GRe(IsConsidered));
TurbineData(ii-2).ivactivepowerGRe = mean(newdata.ivactivepowerGRe(IsConsidered));
TurbineData(ii-2).CalculatedAirdensity_GRe = mean(newdata.CalculatedAirdensity_GRe(IsConsidered));
TurbineData(ii-2).HWShub1 = mean(newdata.HWShub1(IsConsidered));
TurbineData(ii-2).av100msabswinddirectionGRe = mean(newdata.av100msabswinddirectionGRe(IsConsidered));
end
The function ReadFamosDataIntoTimeTable is to convert the .dat binary data into .mat dataI am working on a project where I have to analyse high frequency wind data (50 Hz) coming from a wind turbine data measurement system. this amount of data must be converted from .dat binary files to .mat files which I can use in matlab. The data must then be filtered and then averaged over 10 minutes to be compared to the data from another measurement system. Doing all this requires the analysis of thousands of data and it’s very time consuming (right now it is about 105 seconds just for the data of 2 days). How can I speed the process up?
for ii = 3:length(filedir)
filename = filedir(ii).name;
newdata = ReadFamosDataIntoTimeTable(filename);
% filter
IsConsidered = newdata.avbladeangleGRe<40 … % normal operation
& newdata.RAWS9>0.5 … % good availability
& ~isnan(newdata.iv10mswindspeed2GRe) & newdata.av100msabswinddirectionGRe>180 & newdata.av100msabswinddirectionGRe<250;
TurbineData(ii-2).date = filename;
TurbineData(ii-2).iv10mswindspeed2GRe = mean(newdata.iv10mswindspeed2GRe(IsConsidered));
TurbineData(ii-2).ivactivepowerGRe = mean(newdata.ivactivepowerGRe(IsConsidered));
TurbineData(ii-2).CalculatedAirdensity_GRe = mean(newdata.CalculatedAirdensity_GRe(IsConsidered));
TurbineData(ii-2).HWShub1 = mean(newdata.HWShub1(IsConsidered));
TurbineData(ii-2).av100msabswinddirectionGRe = mean(newdata.av100msabswinddirectionGRe(IsConsidered));
end
The function ReadFamosDataIntoTimeTable is to convert the .dat binary data into .mat data I am working on a project where I have to analyse high frequency wind data (50 Hz) coming from a wind turbine data measurement system. this amount of data must be converted from .dat binary files to .mat files which I can use in matlab. The data must then be filtered and then averaged over 10 minutes to be compared to the data from another measurement system. Doing all this requires the analysis of thousands of data and it’s very time consuming (right now it is about 105 seconds just for the data of 2 days). How can I speed the process up?
for ii = 3:length(filedir)
filename = filedir(ii).name;
newdata = ReadFamosDataIntoTimeTable(filename);
% filter
IsConsidered = newdata.avbladeangleGRe<40 … % normal operation
& newdata.RAWS9>0.5 … % good availability
& ~isnan(newdata.iv10mswindspeed2GRe) & newdata.av100msabswinddirectionGRe>180 & newdata.av100msabswinddirectionGRe<250;
TurbineData(ii-2).date = filename;
TurbineData(ii-2).iv10mswindspeed2GRe = mean(newdata.iv10mswindspeed2GRe(IsConsidered));
TurbineData(ii-2).ivactivepowerGRe = mean(newdata.ivactivepowerGRe(IsConsidered));
TurbineData(ii-2).CalculatedAirdensity_GRe = mean(newdata.CalculatedAirdensity_GRe(IsConsidered));
TurbineData(ii-2).HWShub1 = mean(newdata.HWShub1(IsConsidered));
TurbineData(ii-2).av100msabswinddirectionGRe = mean(newdata.av100msabswinddirectionGRe(IsConsidered));
end
The function ReadFamosDataIntoTimeTable is to convert the .dat binary data into .mat data optimization, for loop, speed, data conversion MATLAB Answers — New Questions
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Egypt country? all credit card i tried not works at your web site. specify libraries, or specific apis, matlab, license MATLAB Answers — New Questions
Switching values around in a matrix
Hi,
Say I have a 5 by 2 matrix in the form:
A = [2 5; 9 7; 10 2; 3 2; 1 9]
And I want to make a 10 by 1 matrix from these values so that I get:
B = [2;5;9;7;10;2;3;2;1;9]
How would I do this?
I know there is a probably a simple fix, but I haven’t been able to do it.
Many thanks,
ScottHi,
Say I have a 5 by 2 matrix in the form:
A = [2 5; 9 7; 10 2; 3 2; 1 9]
And I want to make a 10 by 1 matrix from these values so that I get:
B = [2;5;9;7;10;2;3;2;1;9]
How would I do this?
I know there is a probably a simple fix, but I haven’t been able to do it.
Many thanks,
Scott Hi,
Say I have a 5 by 2 matrix in the form:
A = [2 5; 9 7; 10 2; 3 2; 1 9]
And I want to make a 10 by 1 matrix from these values so that I get:
B = [2;5;9;7;10;2;3;2;1;9]
How would I do this?
I know there is a probably a simple fix, but I haven’t been able to do it.
Many thanks,
Scott matrices, matrix manipulation MATLAB Answers — New Questions
Why do I receive the error “Unable to save login information. You are currently signed in but you will be prompted to sign in again the next time that you start this application”?
When launching MATLAB, why do I receive the error "Unable to save login information. You are currently signed in but you will be prompted to sign in again the next time that you start this application"?When launching MATLAB, why do I receive the error "Unable to save login information. You are currently signed in but you will be prompted to sign in again the next time that you start this application"? When launching MATLAB, why do I receive the error "Unable to save login information. You are currently signed in but you will be prompted to sign in again the next time that you start this application"? MATLAB Answers — New Questions
