A Matlab code of how to do dynamic mode decomposition of a video sequence. (ChatGPT)
% DMD on a Video Sequence clear; close all; clc;
%% Load Video videoFile = 'video.mp4'; % Change to your video file v = VideoReader(videoFile);
% Parameters resizeFactor = 0.25; % Reduce size to speed up numFrames = min(100, floor(v.Duration * v.FrameRate)); % Limit number of frames
% Read and preprocess video frames X = []; for i = 1:numFrames frame = readFrame(v); grayFrame = im2double(rgb2gray(frame)); % Convert to grayscale grayFrame = imresize(grayFrame, resizeFactor); % Resize X(:,i) = grayFrame(:); % Vectorize and store as column end
%% Build data matrices X1 and X2 X1 = X(:, 1:end-1); X2 = X(:, 2:end);
%% Singular Value Decomposition [U, S, V] = svd(X1, 'econ');
% Choose r - number of modes r = 20; % Adjust based on singular value decay U_r = U(:, 1:r); S_r = S(1:r, 1:r); V_r = V(:, 1:r);
P1. Can you give me a Matlab code for Dynamic Mode Decomposition ? The code should be nicely commented so one can understand the purpose of each section.
P2. Modify the Matlab code to handle an array of flattened image data
P3. For the video sequence, how many snapshots, captured at what fps will be needed to show a mode at 200 Hz and a mode at 2000 Hz?
P4. So DMD gives the eigen modes, their amplitudes and their complex frequencies. From the complex frequencies, the real part gives the growth or decay rate and the imaginary part gives the circular frequncy. Is this interpretation correct ?
P5. How to get the amplitude of a mode ?
P6. Can you give an example of complex amplitude ?
The Bychkov limit, also known as the detonation cell size limit, is a concept in detonation physics that describes the minimum size of a detonation cell needed to sustain a propagating detonation wave. This limit is not fixed but depends on factors like the mixture composition, pressure, and tube diameter. [1, 1, 2, 2, 3, 3, 4, 4]. Detonation Cell Size: In a detonation, the reaction zone behind the shock wave propagates in a cellular structure, with each cell representing a single, self-sustaining detonation event. [1, 1, 3, 3, 5, 6]. Bychkov Limit: The Bychkov limit is a critical length scale related to the detonation cell size. It represents the minimum size of a detonation cell that can propagate a stable and sustained detonation wave. [1, 1, 2, 2, 7, 8]. Factors Affecting the Limit: [1, 2, 3, 9] (a) Mixture Composition: The type and ratio of fuel and oxidizer significantly impact the detonation cell size and thus the Bychkov limit. [1, 1, 3, 3, 9, 9] (b) Pressure: Higher pressures generally lead to smaller detonation cell sizes and a lower Bychkov limit. [1, 1, 3, 3, 9, 9, 10, 11, 12] (c) Tube Diameter: The diameter of the container or tube where the detonation is propagating can influence the minimum cell size required for sustained detonation. [1, 1, 2, 2, 3, 3]. Importance: Understanding the Bychkov limit is crucial for designing and analyzing detonation processes, predicting detonability, and mitigating the risks associated with explosions. [2, 2, 3, 3, 4, 4] (a) Experimental and Numerical Studies: The Bychkov limit and its relation to detonation cell size are extensively studied through experiments (e.g., in detonation tubes) and numerical simulations. [13, 13, 14, 14, 15, 15] (b) Examples: In experiments with hydrogen-oxygen mixtures, the Bychkov limit has been observed to be in the range of a few millimeters to several centimeters, depending on the specific conditions. [13, 13, 15, 15, 16, 16] (c) Transition to Deflagration: When the detonation cell size exceeds the Bychkov limit, the detonation wave may become unstable and transition to a deflagration (a slower, subsonic combustion process). [13, 13, 17, 17, 18, 18, 19]
%Here's a MATLAB code that performs Independent Component Analysis (ICA) on a video sequence. The goal is to treat each frame as a data %vector and then extract statistically independent components from this set using ICA.
%This is particularly useful for motion segmentation, source separation, or background subtraction tasks.
% ICA on a video sequence clc; clear; close all;
% Load video videoFile = 'your_video.mp4'; % Change to your video file v = VideoReader(videoFile); numFrames = floor(v.Duration * v.FrameRate);
% Read first frame to get dimensions frame = readFrame(v); grayFrame = rgb2gray(frame); [rows, cols] = size(grayFrame); frameSize = rows * cols;
% Reset video to the beginning v.CurrentTime = 0;
% Construct data matrix: each column is a vectorized frame dataMatrix = zeros(frameSize, numFrames); frameCount = 0;
while hasFrame(v) frame = readFrame(v); grayFrame = rgb2gray(frame); grayFrame = im2double(grayFrame); % Normalize to [0,1] frameCount = frameCount 1; dataMatrix(:, frameCount) = grayFrame(:); end
% Remove mean for ICA X = dataMatrix - mean(dataMatrix, 2);
% Perform ICA using FastICA addpath('fastica'); % Make sure FastICA is in your path (download from https://research.ics.aalto.fi/ica/fastica/) [icasig, A, W] = fastica(X, 'numOfIC', 10); % You can change the number of ICs
% Display some ICs as images figure; for i = 1:9 subplot(3,3,i); imagesc(reshape(icasig(i, :), [1, frameCount])); colormap gray; axis off; title(['IC ', num2str(i)]); end
% You can also reshape the spatial ICs if desired figure; for i = 1:9 subplot(3,3,i); imagesc(reshape(A(:, i), rows, cols)); colormap gray; axis off; title(['Spatial IC ', num2str(i)]); end
% Set image directory imageDir = 'images/'; % Change this to your image folder path imageFiles = dir(fullfile(imageDir, '*.png')); % Change extension if needed (e.g., .jpg, .bmp) numImages = length(imageFiles);
% Read first image to get dimensions firstImage = imread(fullfile(imageDir, imageFiles(1).name)); if size(firstImage, 3) == 3 firstImage = rgb2gray(firstImage); % Convert to grayscale if RGB end [rows, cols] = size(firstImage); imageSize = rows * cols;
% Create data matrix where each column is an image vector dataMatrix = zeros(imageSize, numImages);
for i = 1:numImages img = imread(fullfile(imageDir, imageFiles(i).name)); if size(img, 3) == 3 img = rgb2gray(img); end img = im2double(img); % Convert to double dataMatrix(:, i) = img(:); % Vectorize image end
% Mean normalization meanImage = mean(dataMatrix, 2); X = dataMatrix - meanImage;
% Compute covariance matrix C = cov(X');
% Eigen decomposition [eigVectors, eigValues] = eig(C); eigValues = diag(eigValues);
% Sort eigenvalues and vectors in descending order [~, idx] = sort(eigValues, 'descend'); eigVectors = eigVectors(:, idx);
% Project data onto principal components k = 10; % Number of principal components to keep eigVectors_k = eigVectors(:, 1:k); projectedData = eigVectors_k' * X;
% Reconstruct an image using top k components reconstructed = eigVectors_k * projectedData meanImage;
% Show original and reconstructed images figure; subplot(1, 2, 1); imshow(reshape(dataMatrix(:, 1), rows, cols), []); title('Original Image');
Above Image is from Arts, L.P.A., van den Broek, E.L. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis. Nat Comput Sci 2, 47–58 (2022). https://doi.org/10.1038/s43588-021-00183-z
How does wavelet transform differ from spectrogram when used on the same time series data ?
ChatGPT : Both wavelet transform and spectrograms are time-frequency analysis tools, but they differ significantly in how they analyze and represent the frequency content of time series data. Here's a breakdown of the differences:
1. Spectrogram (based on Short-Time Fourier Transform - STFT)
How it works: - Splits the signal into short, fixed-length time windows. - Applies the Fourier Transform to each window. - Gives a time vs frequency plot showing how frequency content changes over time.
Key Characteristics: - Fixed time-frequency resolution: The window length is constant, so you have a trade-off: - Good time resolution → poor frequency resolution. - Good frequency resolution → poor time resolution. - Best for stationary or slowly varying signals.
Analogy: Like taking snapshots of a signal using the same size lens—details get lost either in time or frequency depending on the window size.
How it works: - Decomposes the signal using scaled and shifted versions of a wavelet (a small waveform). - Automatically adjusts the resolution: - High frequencies → short time windows (good time resolution, poor frequency resolution). - Low frequencies → long time windows (good frequency resolution, poor time resolution).
Key Characteristics: - Multi-resolution analysis: Adapts to different scales. - Better suited for non-stationary or transient signals. - Captures short-lived features like bursts, edges, or spikes better than STFT.
Analogy: Like zooming in and out on a signal with a flexible lens—details appear at multiple scales.
Comparison Table
| Feature | Spectrogram (STFT) | Wavelet Transform (CWT) | |--------|---------------------|-------------------------| | Resolution | Fixed | Adaptive (multi-resolution) | | Time-Frequency Tradeoff | Yes (fixed window) | No (scale-dependent) | | Frequency Analysis | Global (within each window) | Localized (scale-wise) | | Best for | Stationary signals | Non-stationary/transient signals | | Window Function | Fixed (e.g., Hamming, Hann) | Wavelet (e.g., Morlet, Mexican Hat) | | Output | Time-Frequency plot | Time-Scale plot (can be converted to frequency) |
In Practice
- Spectrograms are simpler and computationally cheaper, good for general overview. - Wavelet transforms give richer detail and are often used in applications like: - Biomedical signal analysis (e.g., ECG, EEG) - Seismic analysis - Fault detection in mechanical systems
If you're working with signals where the frequency content changes quickly (like engine vibrations, fault signatures, or speech), wavelets often reveal more subtle features than spectrograms.
Understanding upstream flame propagation in scramjets is challenging, particularly concerning flame flashback in a combustor with a novel strut-cavity flame holder. Two-dimensional unsteady Reynolds-averaged Navier–Stokes (URANS) simulations were performed to investigate how Mach number and wall divergence affect flame behavior. The utility of the strut-cavity flame holder was highlighted through a study of its non-reacting flow characteristics. Flow dynamics are significantly altered as the shear layer above the cavity interacts with the downstream hydrogen jet. Shear layer dynamics and fuel-air mixing are improved through key factors such as shock-train behavior, cavity oscillations, and transverse fuel injection. The submerged fuel jet is less exposed to supersonic flow and demonstrates reduced entropy rise, achieving a 16% increase in mixing efficiency compared to standalone struts and a 46% improvement over transverse injection without a flame holder. Thermal choking shifts the shock train upstream, facilitating interactions with the shear layer and enhancing vortex formation, which decreases flow speed and promotes upstream flame propagation. The presence of OH radicals indicates that flame flashback follows a periodic pattern with an initial gradual slope, suggesting effective anchoring. Stability and flashback likelihood are affected by low-speed zones, vortex merging, and wall divergence. At Mach 3, combustion efficiency improves without wall divergence due to increased heat release, while wall divergence prevents flame flashback by sustaining supersonic core flow and managing flow-flame interactions. At higher core flow velocities, flame stabilization occurs at the cavity's separation corner, despite a tendency for upstream propagation, with validation of the URANS results achieved through two-dimensional large eddy simulations.
Aerodynamics, Thermodynamics, Heat Transfer and Combustion Chemistry. The building blocks of propusion systems. While feedback control is also an integral part, courses like these primarily cover the mechanical aspects of propulsion systems.
Dr. Koppillil Radhakrishnan visited APSYS lab in January 2023 as a part of a visit to IIT Kharagpur India. Dr. Radhakrishnan is an Indian space scientist who headed the Indian Space Research Organisation between November 2009 and December 2014 as Chairman of Space Commission, Secretary of the Department of Space and Chairman of ISRO.
In this recent paper from our group on SCRAM-RAM modes inside a Dual-Mode-Scramjet-Engine (DMSJ), Rajesh Kumar shows that for the Supersonic-at-Strut modes, shockwaves in the combustor respond predictably to an increase or decrease in fuel flow rates. Intermediate and final flow fields bear close resemblance to the steady state flow fields predicted by RANS for those fuel flow rates. Shocks adjust accordingly to match the combustion driven pressure rise. Under the Supersonic-at-Strut mode, when fuel flow rate is increased, the shocks steepen and concentrate near the base of the strut. When fuel flow rate is decreased, the opposite trend is observed wherein shocks become more oblique and spread away downstream from the base of the strut. For the Subsonic-at-Strut modes, shockwaves in the flow field are rather unstable to fuel flow rate modulations. Any change in fuel flow rates causes the isolator shocks to immediately move upstream irrespective of if the fuel flow rate is increased or decreased, suddenly or gradually. Because of this instability of isolator shocks for the Subsonic-at-Strut modes, feedback control of isolator shocks using fuel-flow rate modulations alone may not be feasible.
1503 0 0 0
A Matlab code of how to do dynamic mode decomposition of a video sequence.
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
A Matlab code of how to do dynamic mode decomposition of a video sequence. (ChatGPT)
% DMD on a Video Sequence
clear; close all; clc;
%% Load Video
videoFile = 'video.mp4'; % Change to your video file
v = VideoReader(videoFile);
% Parameters
resizeFactor = 0.25; % Reduce size to speed up
numFrames = min(100, floor(v.Duration * v.FrameRate)); % Limit number of frames
% Read and preprocess video frames
X = [];
for i = 1:numFrames
frame = readFrame(v);
grayFrame = im2double(rgb2gray(frame)); % Convert to grayscale
grayFrame = imresize(grayFrame, resizeFactor); % Resize
X(:,i) = grayFrame(:); % Vectorize and store as column
end
%% Build data matrices X1 and X2
X1 = X(:, 1:end-1);
X2 = X(:, 2:end);
%% Singular Value Decomposition
[U, S, V] = svd(X1, 'econ');
% Choose r - number of modes
r = 20; % Adjust based on singular value decay
U_r = U(:, 1:r);
S_r = S(1:r, 1:r);
V_r = V(:, 1:r);
%% Build A tilde
A_tilde = U_r' * X2 * V_r / S_r;
%% Eigen decomposition
[W, D] = eig(A_tilde);
Phi = X2 * V_r / S_r * W; % DMD modes
%% Compute DMD eigenvalues and frequencies
lambda = diag(D);
omega = log(lambda);
%% Compute initial amplitudes b
x1 = X(:,1);
b = Phi x1;
%% Time dynamics
time_dynamics = zeros(r, numFrames - 1);
t = (0:numFrames-2) / v.FrameRate; % Time vector in seconds
for i = 1:length(t)
time_dynamics(:, i) = (b .* exp(omega * t(i)));
end
X_dmd = real(Phi * time_dynamics);
%% Visualize original vs reconstructed frame
originalFrame = reshape(X(:, 30), size(grayFrame));
reconstructedFrame = reshape(X_dmd(:, 30), size(grayFrame));
figure;
subplot(1,2,1); imshow(originalFrame,[]); title('Original Frame 30');
subplot(1,2,2); imshow(reconstructedFrame,[]); title('DMD Reconstructed Frame 30');
Some prompts I used for ChatGPT
P1. Can you give me a Matlab code for Dynamic Mode Decomposition ? The code should be nicely commented so one can understand the purpose of each section.
P2. Modify the Matlab code to handle an array of flattened image data
P3. For the video sequence, how many snapshots, captured at what fps will be needed to show a mode at 200 Hz and a mode at 2000 Hz?
P4. So DMD gives the eigen modes, their amplitudes and their complex frequencies. From the complex frequencies, the real part gives the growth or decay rate and the imaginary part gives the circular frequncy. Is this interpretation correct ?
P5. How to get the amplitude of a mode ?
P6. Can you give an example of complex amplitude ?
1224 0 0 0
Bychkov Limit (ChatGPT, Google AI)
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
1308 0 0 0
Independent Component Analysis (ICA) for a video sequence. ChatGPT
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
%Here's a MATLAB code that performs Independent Component Analysis (ICA) on a video sequence. The goal is to treat each frame as a data %vector and then extract statistically independent components from this set using ICA.
%This is particularly useful for motion segmentation, source separation, or background subtraction tasks.
% ICA on a video sequence
clc; clear; close all;
% Load video
videoFile = 'your_video.mp4'; % Change to your video file
v = VideoReader(videoFile);
numFrames = floor(v.Duration * v.FrameRate);
% Read first frame to get dimensions
frame = readFrame(v);
grayFrame = rgb2gray(frame);
[rows, cols] = size(grayFrame);
frameSize = rows * cols;
% Reset video to the beginning
v.CurrentTime = 0;
% Construct data matrix: each column is a vectorized frame
dataMatrix = zeros(frameSize, numFrames);
frameCount = 0;
while hasFrame(v)
frame = readFrame(v);
grayFrame = rgb2gray(frame);
grayFrame = im2double(grayFrame); % Normalize to [0,1]
frameCount = frameCount 1;
dataMatrix(:, frameCount) = grayFrame(:);
end
% Remove mean for ICA
X = dataMatrix - mean(dataMatrix, 2);
% Perform ICA using FastICA
addpath('fastica'); % Make sure FastICA is in your path (download from https://research.ics.aalto.fi/ica/fastica/)
[icasig, A, W] = fastica(X, 'numOfIC', 10); % You can change the number of ICs
% Display some ICs as images
figure;
for i = 1:9
subplot(3,3,i);
imagesc(reshape(icasig(i, :), [1, frameCount]));
colormap gray; axis off;
title(['IC ', num2str(i)]);
end
% You can also reshape the spatial ICs if desired
figure;
for i = 1:9
subplot(3,3,i);
imagesc(reshape(A(:, i), rows, cols));
colormap gray; axis off;
title(['Spatial IC ', num2str(i)]);
end
disp('ICA completed successfully!');
1313 0 0 0
Principle Component Analysis (PCA) Matlab Code (ChatGPT)
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
% PCA on an image set
clc; clear; close all;
% Set image directory
imageDir = 'images/'; % Change this to your image folder path
imageFiles = dir(fullfile(imageDir, '*.png')); % Change extension if needed (e.g., .jpg, .bmp)
numImages = length(imageFiles);
% Read first image to get dimensions
firstImage = imread(fullfile(imageDir, imageFiles(1).name));
if size(firstImage, 3) == 3
firstImage = rgb2gray(firstImage); % Convert to grayscale if RGB
end
[rows, cols] = size(firstImage);
imageSize = rows * cols;
% Create data matrix where each column is an image vector
dataMatrix = zeros(imageSize, numImages);
for i = 1:numImages
img = imread(fullfile(imageDir, imageFiles(i).name));
if size(img, 3) == 3
img = rgb2gray(img);
end
img = im2double(img); % Convert to double
dataMatrix(:, i) = img(:); % Vectorize image
end
% Mean normalization
meanImage = mean(dataMatrix, 2);
X = dataMatrix - meanImage;
% Compute covariance matrix
C = cov(X');
% Eigen decomposition
[eigVectors, eigValues] = eig(C);
eigValues = diag(eigValues);
% Sort eigenvalues and vectors in descending order
[~, idx] = sort(eigValues, 'descend');
eigVectors = eigVectors(:, idx);
% Project data onto principal components
k = 10; % Number of principal components to keep
eigVectors_k = eigVectors(:, 1:k);
projectedData = eigVectors_k' * X;
% Reconstruct an image using top k components
reconstructed = eigVectors_k * projectedData meanImage;
% Show original and reconstructed images
figure;
subplot(1, 2, 1);
imshow(reshape(dataMatrix(:, 1), rows, cols), []);
title('Original Image');
subplot(1, 2, 2);
imshow(reshape(reconstructed(:, 1), rows, cols), []);
title(['Reconstructed with ', num2str(k), ' PCs']);
disp('PCA completed successfully!');
1453 0 0 0
Wavelets in Signal Processing
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
Above Image is from Arts, L.P.A., van den Broek, E.L. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis. Nat Comput Sci 2, 47–58 (2022). https://doi.org/10.1038/s43588-021-00183-z
How does wavelet transform differ from spectrogram when used on the same time series data ?
ChatGPT : Both wavelet transform and spectrograms are time-frequency analysis tools, but they differ significantly in how they analyze and represent the frequency content of time series data. Here's a breakdown of the differences:
1. Spectrogram (based on Short-Time Fourier Transform - STFT)
How it works:
- Splits the signal into short, fixed-length time windows.
- Applies the Fourier Transform to each window.
- Gives a time vs frequency plot showing how frequency content changes over time.
Key Characteristics:
- Fixed time-frequency resolution: The window length is constant, so you have a trade-off:
- Good time resolution → poor frequency resolution.
- Good frequency resolution → poor time resolution.
- Best for stationary or slowly varying signals.
Analogy: Like taking snapshots of a signal using the same size lens—details get lost either in time or frequency depending on the window size.
2. Wavelet Transform (especially Continuous Wavelet Transform - CWT)
How it works:
- Decomposes the signal using scaled and shifted versions of a wavelet (a small waveform).
- Automatically adjusts the resolution:
- High frequencies → short time windows (good time resolution, poor frequency resolution).
- Low frequencies → long time windows (good frequency resolution, poor time resolution).
Key Characteristics:
- Multi-resolution analysis: Adapts to different scales.
- Better suited for non-stationary or transient signals.
- Captures short-lived features like bursts, edges, or spikes better than STFT.
Analogy: Like zooming in and out on a signal with a flexible lens—details appear at multiple scales.
Comparison Table
| Feature | Spectrogram (STFT) | Wavelet Transform (CWT) |
|--------|---------------------|-------------------------|
| Resolution | Fixed | Adaptive (multi-resolution) |
| Time-Frequency Tradeoff | Yes (fixed window) | No (scale-dependent) |
| Frequency Analysis | Global (within each window) | Localized (scale-wise) |
| Best for | Stationary signals | Non-stationary/transient signals |
| Window Function | Fixed (e.g., Hamming, Hann) | Wavelet (e.g., Morlet, Mexican Hat) |
| Output | Time-Frequency plot | Time-Scale plot (can be converted to frequency) |
In Practice
- Spectrograms are simpler and computationally cheaper, good for general overview.
- Wavelet transforms give richer detail and are often used in applications like:
- Biomedical signal analysis (e.g., ECG, EEG)
- Seismic analysis
- Fault detection in mechanical systems
If you're working with signals where the frequency content changes quickly (like engine vibrations, fault signatures, or speech), wavelets often reveal more subtle features than spectrograms.
2659 0 0 0
Insights into flame flashback phenomenon utilizing a Strut-Cavity flame holder inside scramjet combustor
Pranay Singeetham #Advanced Propulsion Systems (APSYS) Lab @Amardip Ghosh
Understanding upstream flame propagation in scramjets is challenging, particularly concerning flame flashback in a combustor with a novel strut-cavity flame holder. Two-dimensional unsteady Reynolds-averaged Navier–Stokes (URANS) simulations were performed to investigate how Mach number and wall divergence affect flame behavior. The utility of the strut-cavity flame holder was highlighted through a study of its non-reacting flow characteristics. Flow dynamics are significantly altered as the shear layer above the cavity interacts with the downstream hydrogen jet. Shear layer dynamics and fuel-air mixing are improved through key factors such as shock-train behavior, cavity oscillations, and transverse fuel injection. The submerged fuel jet is less exposed to supersonic flow and demonstrates reduced entropy rise, achieving a 16% increase in mixing efficiency compared to standalone struts and a 46% improvement over transverse injection without a flame holder. Thermal choking shifts the shock train upstream, facilitating interactions with the shear layer and enhancing vortex formation, which decreases flow speed and promotes upstream flame propagation. The presence of OH radicals indicates that flame flashback follows a periodic pattern with an initial gradual slope, suggesting effective anchoring. Stability and flashback likelihood are affected by low-speed zones, vortex merging, and wall divergence. At Mach 3, combustion efficiency improves without wall divergence due to increased heat release, while wall divergence prevents flame flashback by sustaining supersonic core flow and managing flow-flame interactions. At higher core flow velocities, flame stabilization occurs at the cavity's separation corner, despite a tendency for upstream propagation, with validation of the URANS results achieved through two-dimensional large eddy simulations.
6370 1 1 1
SCRAMJET Test Rig Instrumentation
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
The rig is being instrumentated for transitioning from cold flow supersonic mixing studies to reacting flow mode transition studies.
Attachments
6096 4 3 3
Thermodynamics and Aerospace Propulsion Systems Class
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
Aerodynamics, Thermodynamics, Heat Transfer and Combustion Chemistry. The building blocks of propusion systems. While feedback control is also an integral part, courses like these primarily cover the mechanical aspects of propulsion systems.
7618 2 2 2
Ex-ISRO Chairman visits APSYS Labs
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
Dr. Koppillil Radhakrishnan visited APSYS lab in January 2023 as a part of a visit to IIT Kharagpur India. Dr. Radhakrishnan is an Indian space scientist who headed the Indian Space Research Organisation between November 2009 and December 2014 as Chairman of Space Commission, Secretary of the Department of Space and Chairman of ISRO.
4436 0 0 0
Instability of isolator shocks to fuel flow rate modulations in a strut-stabilised scramjet combustor
Amardip Ghosh #Advanced Propulsion Systems (APSYS) Lab
In this recent paper from our group on SCRAM-RAM modes inside a Dual-Mode-Scramjet-Engine (DMSJ), Rajesh Kumar shows that for the Supersonic-at-Strut modes, shockwaves in the combustor respond predictably to an increase or decrease in fuel flow rates. Intermediate and final flow fields bear close resemblance to the steady state flow fields predicted by RANS for those fuel flow rates. Shocks adjust accordingly to match the combustion driven pressure rise. Under the Supersonic-at-Strut mode, when fuel flow rate is increased, the shocks steepen and concentrate near the base of the strut. When fuel flow rate is decreased, the opposite trend is observed wherein shocks become more oblique and spread away downstream from the base of the strut. For the Subsonic-at-Strut modes, shockwaves in the flow field are rather unstable to fuel flow rate modulations. Any change in fuel flow rates causes the isolator shocks to immediately move upstream irrespective of if the fuel flow rate is increased or decreased, suddenly or gradually. Because of this instability of isolator shocks for the Subsonic-at-Strut modes, feedback control of isolator shocks using fuel-flow rate modulations alone may not be feasible.
Attachments