Debus, Charlotte

Kobald, Mario

Petrarolo, Anna

Rüttgers, Alexander

Siggel, Martin

Institute of Transport Research:Publications

Parallelized Machine Learning for the Analysis of Hybrid Rocket Combustion Data14th World Congress on Computational Mechanics (WCCM)ECCOMAS Congress 2020January 11th – 15th 2021Alexander Rüttgers (SC-HPC)Institute for Software TechnologyGerman Aerospace Center (DLR)Joint work with Anna Petrarolo, Mario Kobald, Martin Siggel (all DLR) and Charlotte Debus (KIT)> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 1Outline1. Rocket engine combustion analysis at DLR2. Helmholtz Analytics Toolkit (HeAT) for distributed ML3. Resultsa) Spectral Clusteringb) Anomaly Detection> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 2Rocket engine combustion analysis• Aim: Cost reduction of rocket engines, be competitive with e.g. Space-X> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 3Traditional liquid rocket engine:• 2 pumps transporting fluid fuel and oxidizer at very high pressure and flow• Advantages• Burning rate can be controlled precisely• Disadvantages• Pumps are mechanically very complex• Expensive©2011, University of WaikatoRocket engine combustion analysis• Aim: Cost reduction of rocket engines, be competitive with e.g. Space-X> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 4Solid propellant rocket engine• Fuel and oxidizer are mixed in solid form• Advantage• Cheap• Disadvantage• Burning rate can not be varied during flight©2011, University of WaikatoRocket engine combustion analysis• Aim: Cost reduction of rocket engines, be competitive with e.g. Space-X> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 5Hybrid rocket engine• Pressurized fluid oxidizer• Solid fuel• A valve controls, how much oxidizer gets into the combustion chamber• Advantages• Cheap• Controllable©2011, University of WaikatoExperiments on new hybrid rocket fuels at DLR• DLR investigates new hybrid rocket fuels on a paraffin basis at Institute of Space Propulsion in Lampoldshausen.           • About 300 combustion tests were performed with single-slab paraffin-based fuel with 20° forward facing ramp angle + gaseous oxygen.• Combustion is captured with high-speed video camera with 10 000 frames / second> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 6Fig. 1: Fuel slap configuration before (top) and after (bottom) combustion test.Fig. 2: Side view of combustion chamber> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 7Video extract oftest 284fuel oxidizer massflowCH*-filter durationIgnition, steadycombustion, extinctionpure paraffin 6805 50 g/s, yes, i.e. onlywavelengths emittedfrom CH* are filmed3 s = 30 000 frames / 8GB raw data per testTest 284Outline1. Rocket engine combustion analysis at DLR2. Helmholtz Analytics Toolkit (HeAT) for distributed ML3. Resultsa) Spectral Clusteringb) Anomaly Detection> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 8HeAT• HeAT = Helmholtz Analytics Toolkit• Python framework for parallel, distributed data analytics and machine learning• Developed within the Helmholtz Analytics FrameworkProject since 2018• Aim: Bridge data analytics and high-performance computing• Open Source licensed, MIThelmholtz-analytics/heat> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 9Scope> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDesignFacilitating analysis ofHelmholtz applicationsBringing HPC and MachineLearning / Data Analytics closer togetherEase of useDLR.de  •  Chart 10k-meansSVMmpi4pyDeepLearningDistributed Parallelism (MPI)NumPy-likeinterfaceAutomaticDifferentiationTensor LinearAlgebraGPU supportAnd more machinelearning algorithmsData DistributionServer#1PyTorch Tensor#1Server#2PyTorch Tensor#2Server#3PyTorch Tensor#3HeAT TensorExample:Server#1[0, 1]Server#2[2, 3]Server#3[4, 5]split=1Server#1 PyTorch Tensor#1Server#2 PyTorch Tensor#2Server#3 PyTorch Tensor#3HeAT Tensorsplit=0> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 11What has been done so far?• The core technology has been identified• Implementation of a distributed parallel tensorcore framework• NumPy-compatible core functionality• Some linear algebra routines• Parallel data I/O via HDF 5 and NETCDF• K-means and spectral clustering algorithms are available> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 12𝑥0,0 … 𝑥0,𝑚⋮ ⋱ ⋮𝑥𝑛,0 … 𝑥𝑛,𝑚𝑥0,0 … 𝑥0,𝑀⋮ 𝑥𝑖𝑗 ⋮𝑥𝑁,0 … 𝑥𝑁,𝑀𝑥𝑛+1,𝑚+1 … 𝑥𝑛+1,𝑚⋮ ⋱ ⋮𝑥2𝑛,𝑚+1 … 𝑥2𝑛,2𝑚𝑥𝑟∙𝑛,𝑟∙𝑚 … 𝑥𝑟∙𝑛,𝑀⋮ ⋱ ⋮𝑥𝑁,𝑟∙𝑚 … 𝑥𝑁,𝑀…PyTorch tensorDistributed tensorOutline1. Rocket engine combustion analysis at DLR2. Helmholtz Analytics Toolkit (HeAT) for distributed ML3. Resultsa) Spectral Clusteringb) Anomaly Detection> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 13Dissimilarity measure for image data• Algorithms often require pairwise dissimilarity of images (matrix of size nr_of_images x nr_of_images).• Standard approaches such as mean squared error (MSE) / discrete L2-norm often differ from human recognition.• Advanced dissimilarity measures such as structural similarity (SSIM) often perform better (considers luminance, contrast and structure) but are much more expensive.• Structural similarity (SSIM)/ structural dissimilarity (DSSIM) is not a distance metric. Example: (b)-(f) with same MSE, SSIM decreases*> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 14*https://nsf.gov/news/mmg/mmg_disp.jsp?med_id=79419&from=Pairwise distance matrices for test 284 Computing time: 3-4 minutes Computing time: 5 days (OpenMP parallel, 56 cores)one comparison ≈ 0.1 s (scikit-image)potential anomaliespotential anomaliesmore irregularmatrix structure?better for anomalydetection?more regularmatrix structure?better forclustering?> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 15potential clustersSpectral Clustering of test 284> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 16• Fig. 1: Results of spectralclustering with ssim affinitymatrix.• Using an Euclidean affinitymatrix leads to a separation ofthe extinction phase intovarious clusters.• Note that the number ofclusters k is a hyperparameterof the clustering algorithm.Anomaly Detection: Local Outlier Factor (LOF)• Algorithm that bases on local density of data points.• Shares some concepts with clustering algorithms such as DBSCAN and OPTICS.• Does not show a decision boundary, i.e. cannot directly be used on new data (not necessary here).• Core idea: Compare local density of an object to the local densities of its neighbors.• Ratio „Density of neighbors / local density of an objects”• ≈ 1.0 means similar density as neighbors• > 1.0 means lower density than neighbors (outlier candidate) Point density with respect to k=3 closest neighbors> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 17• Euclidean distance norm returns larger outlier score values (due to irregular matrix?).• SSIM and Euclidean distance share some anomalies but there are differences.> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 18Peak outliers of Euclidean metric (test 284)Flame fluctuations in ignition phase at t = 0.1078 sDroplet detection towards end of combustion at t = 2.2055 s> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 19Some outliers found in other combustion tests> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 20Test 291: satellite droplet at t = 0.0253 sTest 296: satellite droplet at t = 0.0017 sTest 296: satellite droplet at t = 0.0223 sConclusion and outlook• Clustering and anomaly detection in rocket combustion image data is possible provided that distance measure is adequate.• Further insights are possible if datasets are combined (e.g. anomaly detection in spectral and image data).• Future work is spent on distance measures that are more adapted to the „interesting anomalies“.Thank you for your attention!> WCCM 2020 > Alexander Rüttgers  •  Local Anomaly Detection > Parallelized Machine LearningDLR.de  •  Chart 21

Parallelized Machine Learning for the Analysis of Hybrid Rocket Combustion Data

https://elib.dlr.de/141719/1/WCCM_Ruettgers.pdf

Parallelized Machine Learning for the Analysis of Hybrid Rocket Combustion Data

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