Results for “Model selection” — Page 97

NASA NTRS2019

Comprehensive Assessment of Models and Events Using Library Tools (CAMEL) Framework: Time Series Comparisons

The Comprehensive Assessment of Models and Events using Library Tools (CAMEL) framework leverages existing Community Coordinated Modeling Center services: Run on Request post processing tools that generate model time series outputs and the new Community Coordinated Modeling Center Metadata Registry that describes simulation runs using Space Physics Archive Search and Extract metadata. The new CAMEL visualization tool compares the modeled time series with observational data and computes a suite of skill scores such as Prediction Efficiency, Root Mean Square Error, and Symmetric Signed Percentage Bias. Model data pairs used for skill calculations are obtained considering a user selected maximum difference between the time of observation and the nearest model output. The system renders available data for all locations and time periods selected using interactive visualizations that allow the user to zoom, pan, and pick data values along traces. Skill scores are reported for each selected event or aggregated over all events for all participating model runs. Separately, scores are reported for all locations (satellites or stations) and for each location individually. We are building on past experiences with model data comparisons of magnetosphere and ionosphere model outputs from GEM2008, GEMCEDAR Electrodynamics Thermosphere Ionosphere, and the SWPC Operational Space Weather Model challenges. The CAMEL visualization tool is demonstrated using three validation studies: (a) Wang Sheeley Arge heliosphere simulations compared against OMNI solar wind data, (b) ground magnetic perturbations from several magnetosphere and ionosphere electrodynamics models as observed by magnetometers, and (c) electron fluxes from several ring current simulations compared to Radiation Belt Storm Probes Helium Oxygen Proton Electron instrument measurements, integrated over different energy ranges.

Rastätter, Lutz↗

DOE OSTI2023

An Online Prototype Toolset for Predicting and Optimizing P&T Performance (FY23 Status Report)

A new web-based toolset is being developed to support ongoing remediation optimization efforts and implementation of an adaptive site management strategy for the 200 West Area Pump-and-Treat (P&T) system at the Hanford Site. This toolset, comprising the well performance index tool and the well optimization pre-screening tool, will offer a user-friendly interface to predict and optimize the P&T well network’s performance at a preliminary level. Efforts in fiscal year (FY) 2023 focused on three main components: updating the existing deep learning model for predicting P&T performance, designing and developing a prototype of a web-based performance index tool, and initiating the conceptual design of the well optimization pre-screening tool. The well performance index tool is based on a pre-trained deep learning model that allows users to select a target contaminant and well screen length, then visualize the predicted performance of potential new wells across the site. The well optimization pre-screening tool includes two separate modules: the pre-computed scenario viewer, which organizes and visualizes offline optimization simulation results, and the quick analysis module, which provides real-time model prediction using user-specified well locations. In FY24, the plan is to add web-based applications to SOCRATES for both the well performance prediction tool and the optimization prescreening tool, with accompanying user and theory guides. These tools are intended to enable an accessible, easily applied, and transparent approach to remedy planning and decision-making.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

USSR Space Life Sciences Digest, issue 28

This is the twenty-eighth issue of NASA's Space Life Sciences Digest. It contains abstracts of 60 journal papers or book chapters published in Russian and of 3 Soviet monographs. Selected abstracts are illustrated with figures and tables from the original. The abstracts in this issue have been identified as relevant to 20 areas of space biology and medicine. These areas include: adaptation, aviation medicine, botany, cardiovascular and respiratory systems, developmental biology, endocrinology, enzymology, equipment and instrumentation, hematology, human performance, immunology, life support systems, mathematical modeling, musculoskeletal system, neurophysiology, personnel selection, psychology, radiobiology, reproductive system, and space medicine.

Stone, Lydia Razran↗

NASA NTRS2017

Updates on Modeling the Water Cycle with the NASA Ames Mars Global Climate Model

Global Circulation Models (GCMs) have made steady progress in simulating the current Mars water cycle. It is now widely recognized that clouds are a critical component that can significantly affect the nature of the simulated water cycle. Two processes in particular are key to implementing clouds in a GCM: the microphysical processes of formation and dissipation, and their radiative effects on heating/ cooling rates. Together, these processes alter the thermal structure, change the dynamics, and regulate inter-hemispheric transport. We have made considerable progress representing these processes in the NASA Ames GCM, particularly in the presence of radiatively active water ice clouds. We present the current state of our group's water cycle modeling efforts, show results from selected simulations, highlight some of the issues, and discuss avenues for further investigation.

Global Circulation Models (GCMs)↗

DOE OSTI2026

Multi-trait multi-environment genomic prediction strategies for Miscanthus sacchariflorus

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (G×E×T) and (2) two single-trait multi-environment (STME) models (with and without G×E interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomic prediction (GP)↗

NASA NTRS1997

A Study of the Effects of Blade Shape on Rotor Noise

A new rotor noise prediction system called the Tiltrotor Aeroacoustic Code (TRAC) has been developed under the Short Haul (Civil Tiltrotor) program between NASA, the Army, and the U.S. helicopter industry. This system couples the comprehensive rotorcraft code CAMRAD.Mod1 with either the high resolution sectional loads code HIRES or the full potential CFD code FPRBVI to predict unsteady blade loads, which are then input to the noise prediction program WOPWOP. In this paper, HIRES will be used to predict the blade-vortex interaction (BVI) noise trends associated with blade shape. The baseline shape selected was a 17% scale model of a contemporary design 4 bladed rotor. Measurements for this rotor were acquired in the Duits-Nederslandse Windtunnel (DNW). The code is used to predict noise for the base configuration and the results are compared to the measured data. This provides a firm foundation for investigating the BVI noise trends associated with blade shape. The shapes selected for study are based on variation of sweep and taper which reflect plausible "passive" design concepts. Comparisons of power required, integrated noise, and aerodynamics are made and important trends are noted.

Jones, Henry E.↗

NASA NTRS2021

Hypersonic Boundary-Layer Instabilities over Ogive-Cylinder Models

Computational investigations of an ogive-cylinder geometry with varying nosetips at zero degrees angle of attack are presented. The model geometry and conditions are selected to match experiments conducted in the Air Force Research Laboratory (AFRL) Mach 6 Ludwieg Tube. Five nosetips of interest were selected for the computational studies herein: two sharp ogives, two blunt ogives, and one hemispherical nosetip. Computations are performed at a freestream Reynolds number of 7.01 × 10 6 m -1 . Each sharp and blunt tip ogives had a 14 and 28 degree version for the tip angles. A cylindrical section follows the nosetip, resulting in a meter long model such as in the experiments. The laminar flow solutions are analyzed. The boundary-layer-edge properties and the velocity and temperature profiles are compared across streamwise locations aft of the ogive-cylinder junction. The blunt nosetips induce an entropy layer that envelopes the boundary-layer profiles. Modal stability analysis identifies most amplified frequencies corresponding to Mack’s second modes that agree with experimental results for the sharp tips. Similar to the experimental measurements based on wall-mounted pressure sensors, no unstable modes are found for the blunt models. Nonmodal analysis revealed a broadband set of disturbances present for the blunter tips, in agreement with experimental observations. Flow perturbation contours of most amplified planar and oblique disturbances are shown to qualitatively match wind tunnel Schlieren images, with a switch from rope-like to elongated structures, i.e., from high frequency Mack’s second modes to low frequency Mack’s first modes, as the nosetip angle is increased for the sharp tip, and from boundary-layer to entropy-layer disturbances as the bluntness is increased.

Boundary layer transition↗

NASA NTRS2022

Hypersonic Boundary-Layer Instabilities over Ogive-Cylinder Models

Computational investigations of an ogive-cylinder geometry with varying nosetips at zero degrees angle of attack are presented. The model geometry and conditions are selected to match experiments conducted in the Air Force Research Laboratory (AFRL) Mach 6 Ludwieg Tube. Five nosetips of interest were selected for the computational studies herein: two sharp ogives, two blunt ogives, and one hemispherical nosetip. Computations are performed at a freestream Reynolds number of 7.01 × 10 6 m -1 . Each sharp and blunt tip ogives had a 14 and 28 degree version for the tip angles. A cylindrical section follows the nosetip, resulting in a meter long model such as in the experiments. The laminar flow solutions are analyzed. The boundary-layer-edge properties and the velocity and temperature profiles are compared across streamwise locations aft of the ogive-cylinder junction. The blunt nosetips induce an entropy layer that envelopes the boundary-layer profiles. Modal stability analysis identifies most amplified frequencies corresponding to Mack’s second modes that agree with experimental results for the sharp tips. Similar to the experimental measurements based on wall-mounted pressure sensors, no unstable modes are found for the blunt models. Nonmodal analysis revealed a broadband set of disturbances present for the blunter tips, in agreement with experimental observations. Flow perturbation contours of most amplified planar and oblique disturbances are shown to qualitatively match wind tunnel Schlieren images, with a switch from rope-like to elongated structures, i.e., from high frequency Mack’s second modes to low frequency Mack’s first modes, as the nosetip angle is increased for the sharp tip, and from boundary-layer to entropy-layer disturbances as the bluntness is increased.

Boundary layer transition↗

DOE OSTI2021

A selective WDR5 degrader inhibits acute myeloid leukemia in patient-derived mouse models

Interactions between WD40 repeat domain protein 5 (WDR5) and its various partners such as mixed lineage leukemia (MLL) and c-MYC are essential for sustaining oncogenesis in human cancers. However, inhibitors that block protein-protein interactions (PPIs) between WDR5 and its binding partners exhibit modest cancer cell killing effects and lack in vivo efficacy. Here, we present pharmacological degradation of WDR5 as a promising therapeutic strategy for treating WDR5-dependent tumors and report two high-resolution crystal structures of WDR5-degrader-E3 ligase ternary complexes. We identified an effective WDR5 degrader via structure-based design and demonstrated its in vitro and in vivo antitumor activities. On the basis of the crystal structure of an initial WDR5 degrader in complex with WDR5 and the E3 ligase von Hippel–Lindau (VHL), we designed a WDR5 degrader, MS67, and demonstrated the high cooperativity of MS67 binding to WDR5 and VHL by another ternary complex structure and biophysical characterization. MS67 potently and selectively depleted WDR5 and was more effective than WDR5 PPI inhibitors in suppressing transcription of WDR5-regulated genes, decreasing the chromatin-bound fraction of MLL complex components and c-MYC, and inhibiting the proliferation of cancer cells. In addition, MS67 suppressed malignant growth of MLL-rearranged acute myeloid leukemia patient cells in vitro and in vivo and was well tolerated in vivo. Collectively, our results demonstrate that structure-based design can be an effective strategy to identify highly active degraders and suggest that pharmacological degradation of WDR5 might be a promising treatment for WDR5-dependent cancers.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2004

Survey of Technologies Relevant to Defense From Near-Earth Objects

Several recent near-miss encounters with asteroids and comets have focused attention on the threat of a catastrophic impact with the Earth. This Technical Publication reviews the historical impact record and current understanding of the number and location of near-Earth objects (NEOs) to address their impact probability. Various ongoing projects intended to survey and catalog the NEO population are also reviewed. Details are given of a Marshall Space Flight Center-led study intended to develop and assess various candidate systems for protection of the Earth against NEOs. Details of analytical tools, trajectory tools, and a tool that was created to model both the undeflected inbound path of an NEO as well as the modified, postdeflection path are given. A representative selection of these possible options was modeled and evaluated. It is hoped that this study will raise the level of attention about this very real threat and also demonstrate that successful defense is both possible and practicable, provided appropriate steps are taken.

Adams, R. B.↗

DOE OSTI2022

Abundance matching analysis of the emission-line galaxy sample in the extended Baryon Oscillation Spectroscopic Survey

We present the measurements of the small-scale clustering for the emission-line galaxy (ELG) sample from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) in the Sloan Digital Sky Survey IV (SDSS-IV). We use conditional abundance matching method to interpret the clustering measurements from 0.34 to 70 h -1 Mpc. In order to account for the correlation between properties of ELGs and their environment, we add a secondary connection between star formation rate of ELGs and halo accretion rate. Three parameters are introduced to model the ELG [O II ] luminosity and to mimic the target selection of eBOSS ELGs. The parameters in our models are optimized using Markov Chain Monte Carlo (MCMC) method. We find that by conditionally matching star formation rate of galaxies and the halo accretion rate, we are able to reproduce the eBOSS ELG small-scale clustering within 1σ error level. Our best-fitting model shows that the eBOSS ELG sample only consists of ~ 12 per cent of all star-forming galaxies, and the satellite fraction of eBOSS ELG sample is 19.3 per cent. We show that the effect of assembly bias is ~ 20 per cent on the two-point correlation function and ~ 5 per cent on the void probability function at scale of r ~ 20 h -1 Mpc.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1985

Aerosol measurement program strategy for global aerosol backscatter model development

The purpose was to propose a balanced program of aerosol backscatter research leading to the development of a global model of aerosol backscatter. Such a model is needed for feasibility studies and systems simulation studies for NASA's prospective satellite-based Doppler lidar wind measurement system. Systems of this kind measure the Doppler shift in the backscatter return from small atmospheric aerosol wind tracers (of order 1 micrometer diameter). The accuracy of the derived local wind estimates and the degree of global wind coverage for such a system are limited by the local availability and by the global scale distribution of natural aerosol particles. The discussions here refer primarily to backscatter model requirements at CO2 wavelengths, which have been selected for most of the Doppler lidar systems studies to date. Model requirements for other potential wavelengths would be similar.

Bowdle, David A.↗

DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗

DOE OSTI2023

Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

42 ENGINEERING↗

NASA NTRS2006

System Architecture Modeling for Technology Portfolio Management using ATLAS

Strategic planners and technology portfolio managers have traditionally relied on consensus-based tools, such as Analytical Hierarchy Process (AHP) and Quality Function Deployment (QFD) in planning the funding of technology development. While useful to a certain extent, these tools are limited in the ability to fully quantify the impact of a technology choice on system mass, system reliability, project schedule, and lifecycle cost. The Advanced Technology Lifecycle Analysis System (ATLAS) aims to provide strategic planners a decision support tool for analyzing technology selections within a Space Exploration Architecture (SEA). Using ATLAS, strategic planners can select physics-based system models from a library, configure the systems with technologies and performance parameters, and plan the deployment of a SEA. Key parameters for current and future technologies have been collected from subject-matter experts and other documented sources in the Technology Tool Box (TTB). ATLAS can be used to compare the technical feasibility and economic viability of a set of technology choices for one SEA, and compare it against another set of technology choices or another SEA. System architecture modeling in ATLAS is a multi-step process. First, the modeler defines the system level requirements. Second, the modeler identifies technologies of interest whose impact on an SEA. Third, the system modeling team creates models of architecture elements (e.g. launch vehicles, in-space transfer vehicles, crew vehicles) if they are not already in the model library. Finally, the architecture modeler develops a script for the ATLAS tool to run, and the results for comparison are generated.

Thompson, Robert W.↗

DOE OSTI2021

Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.

42 ENGINEERING↗

DOE OSTI2023

Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

47 OTHER INSTRUMENTATION↗

NASA NTRS2018

Thermal Analysis of Propulsion Components for the Europa Clipper Mission

This presentation describes the thermal analysis and model development that occurred for selected components on the propulsion module subsystem of the Europa Clipper mission, which will fly to Jupiter's icy moon Europa and collect science data from orbit. An overview of a bipropellant system is given, as well as a description of a typical thermal propulsion design. A comparison is also provided, describing the unique Europa Clipper thermal design, which is atypical in many respects. The engine thermal model development is also discussed, including hot-firing tests with nozzle convection correlation, as well as thermal vacuum tests to measure and correlate the emissivity of critical nozzle surfaces. A description of engine firing, as well as valve soak back, is also provided, including temperature maps and results of engine cases. A summary is also provided, of lessons learned regarding thermal propulsion considerations.

propulsion module subsystem↗