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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2017

High-Lift Propeller Noise Prediction for a Distributed Electric Propulsion Flight Demonstrator

Over the past several years, the use of electric propulsion technologies within aircraft design has received increased attention. The characteristics of electric propulsion systems open up new areas of the aircraft design space, such as the use of distributed electric propulsion (DEP). In this approach, electric motors are placed in many different locations to achieve increased efficiency through integration of the propulsion system with the airframe. Under a project called Scalable Convergent Electric Propulsion Technology Operations Research (SCEPTOR), NASA is designing a flight demonstrator aircraft that employs many "high-lift propellers" distributed upstream of the wing leading edge and two cruise propellers (one at each wingtip). As the high-lift propellers are operational at low flight speeds (take-off/approach flight conditions), the impact of the DEP configuration on the aircraft noise signature is also an important design consideration. This paper describes efforts toward the development of a mulit-fidelity aerodynamic and acoustic methodology for DEP high-lift propeller aeroacoustic modeling. Specifically, the PAS, OVERFLOW 2, and FUN3D codes are used to predict the aerodynamic performance of a baseline high-lift propeller blade set. Blade surface pressure results from the aerodynamic predictions are then used with PSU-WOPWOP and the F1A module of the NASA second generation Aircraft NOise Prediction Program to predict the isolated high-lift propeller noise source. Comparisons of predictions indicate that general trends related to angle of attack effects at the blade passage frequency are captured well with the various codes. Results for higher harmonics of the blade passage frequency appear consistent for the CFD based methods. Conversely, evidence of the need for a study of the effects of increased azimuthal grid resolution on the PAS based results is indicated and will be pursued in future work. Overall, the results indicate that the computational approach is acceptable for fundamental assessment of low-noise high-lift propeller designs. The extent to which the various approaches may be used in a complementary manner will be further established as measured data becomes available for validation. Ultimately, it is anticipated that this combined approach may be used to provide realistic incident source fields for acoustic shielding/scattering studies on various aircraft configurations.

Nark, Douglas M.↗
NASA NTRS2017

Comparison of Two Bioinformatics Tools Used to Characterize the Microbial Diversity and Predictive Functional Attributes of Microbial Mats from Lake Obersee, Antarctica

In this study, using NextGen sequencing of the collective 16S rRNA genes obtained from two sets of samples collected from Lake Obersee, Antarctica, we compared and contrasted two bioinformatics tools, PICRUSt and Tax4Fun. We then developed an R script to assess the taxonomic and predictive functional profiles of the microbial communities within the samples. Taxa such as Pseudoxanthomonas, Planctomycetaceae, Cyanobacteria Subsection III, Nitrosomonadaceae, Leptothrix, and Rhodobacter were exclusively identified by Tax4Fun that uses SILVA database; whereas PICRUSt that uses Greengenes database uniquely identified Pirellulaceae, Gemmatimonadetes A1-B1, Pseudanabaena, Salinibacterium and Sinibacteraceae. Predictive functional profiling of the microbial communities using Tax4Fun and PICRUSt separately revealed common metabolic capabilities, while also showing specific functional IDs not shared between the two approaches. Combining these functional predictions using a customized R script revealed a more inclusive metabolic profile, such as hydrolases, oxidoreductases, transferases; enzymes involved in carbohydrate and amino acid metabolisms; and membrane transport proteins known for nutrient uptake from the surrounding environment. Our results present the first molecular-phylogenetic characterization and predictive functional profiles of the microbial mat communities in Lake Obersee, while demonstrating the efficacy of combining both the taxonomic assignment information and functional IDs using the R script created in this study for a more streamlined evaluation of predictive functional profiles of microbial communities.

Hyunmin Koo↗
NASA NTRS2018

Prediction Skill of U.S. Flash Droughts in Subseasonal Experiment (SubX) Model Hindcasts

Droughts that establish themselves over a short period of time (weeks to a few months), referred to as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict such droughts in advance would greatly enhance our preparation for them and potentially reduce their impacts. The sub-seasonal time scale at which flash droughts occur emphasizes the importance of producing forecasts at weekly or finer intervals that extend beyond the numerical weather prediction time frame. Here we assess the ability of eight global forecast systems, each participating in the Sub-seasonal Experiment project (SubX), to predict key features associated with rapidly developing droughts over the United States during the last two decades. MERRA2 reanalysis is used as observations. Prediction skill for temperature and precipitation anomalies during these events is limited to the first 1-2 weeks after initialization for most hindcasts. However, there are some hindcasts in which large anomalies are well predicted 3-4 weeks or more in advance. The physical mechanisms that are key to the development of surface anomalies, including quasi-stationary atmospheric waves, were also evaluated. Most hindcasts were unable to capture the development or progression of such drought-inducing circulation features more than 1-2 weeks in advance.

Deangelis, Anthony M.↗
NASA NTRS2013

Algorithm Development for Predicting Biodiversity Based on Phytoplankton Absorption

Ocean color remote sensing has provided the scientific community with unprecedented global coverage of chlorophyll a, an indicator of phytoplankton biomass. Together, satellite-derived chlorophyll a and knowledge of Phytoplankton Functional Types (PFTs) will improve our limited understanding of marine ecosystem responses to physiochemical climate drivers involved in carbon cycle dynamics and linkages. Using cruise data from the Gulf of Maine and the Middle Atlantic Bight (N 269 pairs of HPLC and phytoplankton absorption samples), two modeling approaches were utilized to predict phytoplankton absorption and pigments. Algorithm I predicts the chlorophyll-specific absorption coefficient (a*(sub ph) (m(exp 2) mg chl a(exp - 1)) using inputs of temperature, light, and chlorophyll a. Modeled r(exp 2) values (400700 nm) ranged from 0.79 to 0.99 when compared to in situ observations with approximately 25 percent lower r(exp 2) values in the UV region. Algorithm II-a utilizes matrix inversion analysis to predict a(sub ph)(m(exp -1), 400700 nm) and r(exp 2) values ranged from 0.89 to 0.99. The prediction of phytoplankton pigments with Algorithm II-b produced r(exp 2) values that ranged from 0.40 to 0.93. When used in combination, Algorithm I, and Algorithm II-a are able to use satellite products of SST, PAR, and chlorophyll a (Algorithm I) to predict pigment concentrations and ratios to describe the phytoplankton community. The results of this study demonstrate that the spatial variation in modeled pigment ratios differ significantly from the 10-year SeaWiFS average chlorophyll a data set. Contiguous observations of chlorophyll a and phytoplankton biodiversity will elucidate ecosystem responses with unprecedented complexity.

phytoplankton↗
NASA NTRS2019

Jet Noise Prediction Comparisons with Scale Model Tests and Learjet Flyover Data

Recent interest in commercial supersonic flight has highlighted the need to accurately predict Effective Perceived Noise Levels (EPNL) for aircraft and, since the dominant noise source at takeoff will likely be jet noise, specifically jet noise contributions. The current study compares predictions from historical jet-noise models within NASA’s Aircraft Noise Prediction Program and scale-model data to measurements made in a Learjet 25 flight test. The noise levels from the predictions and scale-model data were below those for the flight data by 2.5 – 3.5, 1 – 2, and 3 – 5 EPNdB for the SAE model, the Stone Jet model, and the scale-model data, respectively. Tones and broadband haystacks were identified in the flight spectra that are not associated with jet noise which increased the flight EPNL by at least 0.5 EPNdB over that computed from spectra with the tones and haystacks removed. The study highlights the need for accurate exhaust temperature measurements, aircraft flight position information, and averaging data across a line of microphones in flight tests. For example, a 100° F to 200° F difference in jet exhaust temperature is enough to explain the differences between flight, model scale, and prediction comparisons.

Henderson, Brenda↗
NASA NTRS2019

An Evaluation and Recommendations for Further CFD Research Based on the NASA Common Research Model (CRM) Analysis from the AIAA Drag Prediction Workshop (DPW) Series

The six Drag Prediction Workshops (DPW) have drawn a total of 125 participants submitting 225 data entries. Over half of these occurred in the last three workshops dealing with the NASA Common Research Model (CRM). Participants have ranged from North America, Europe, Asia, and South America, representing Government, Industry, Academia, and Commercial/Vendors. The DPW Series has generated a tremendous amount of CFD data, freely available to the public domain. A continuous improvement of the results as measured by: the reduction of the spread of drag predictions for the specified “nominal” cruise point, accuracy of drag increment, and the angle-of-attack sweep behavior, has been noted. Although it has been difficult separating out the parts and pieces that contribute to errors in CFD drag predictions, part of the improvements over the years can be attributed to grid resolution. The scatter in predicted drag in the continuum has been reduced dramatically from plus or minus 50 counts in DPW-I to around plus or minus 5 counts for DPW-V and - VI. During the six workshops, the grid size has grown dramatically. The average size of the medium wing-body meshes in DPW-I through DPW-IV have been 3.2, 5.4, 7.8 and 10.9 million, respectively. This represents a growth rate of approximately 17 percent per year during the eight years between DPW-I and DPW-IV. For DPW-VI, this number has grown from 25 to 50 million points for the various families of grids available. The finest level grids have increased steadily, from just over 3 million grid points in DPW-I to 225 million for the WBNP (Wing-Body-Nacelle-Pylon) in DPW-VI. While progress has been made, persistent problems with grid generation and separated flow prediction continue. The experiences and recommendations presented here are consistent with those espoused in the “CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences,” NASA/CR-2014-218178.

Tinoco, Edward N.↗
NASA NTRS2019

CASPEr: an Approach to Characterize the Performance of Onboard Airplane Energy State and Automation Mode Prediction Functions

The Commercial Aviation Safety Team (CAST) has identified a set of safety enhancements to mitigate the risks of loss of control in-flight (LOCI) accidents and incidents involving commercial transport airplanes. In support of this, NASA has been developing technologies intended to enhance flight crew awareness of airplane systems, attitude, and energy state. This report describes preliminary ideas for a methodology to assess the goodness of onboard airplane energy state and automation mode prediction functions. The methodology is intended to contribute to the goal of moving these prediction technologies to the readiness level required for transition to industry and reduce the technology certification risks. In addition, this report describes a simulation-based approach named CASPEr (Characterization of Airplane State Prediction Error) to characterize the performance of these predictive functions over a wide range of operational conditions. The first exploratory version of this approach is described. The bulk of the report documents the initial results of tests to characterize the performance of an airplane trajectory prediction function. Future reports will give additional performance characterization results for this function and a complete description of the proposed methodology to assess such functions.

Torres-Pomales, Wilfredo↗
NASA NTRS2017

Predicting Cell Death and Mutation Frequency for a Wide Spectrum of LET by Assuming DNA Break Clustering Inside Repair Domains

The high relative biological effectiveness (RBE) of high charged and energy (HZE) particles for cell death, DNA mutations and cancer remain based on experimental data. In this work, we propose that the existence of DNA repair domains is sufficient to predict both cell death and mutation frequencies for any LET by only taking into account experimental data from low-LET, offering one mechanism for RBE across LET. We hypothesize that whenever multiple DNA double-strand breaks (DSBs) are generated within the same DNA repair domain, DSBs are actively regrouped for more efficient repair [1]. This hypothesis has been supported by the low-LET sublinear dose response observed at doses greater than ~1Gy for 53BP1 radiation-induced foci (RIF) reflecting increasing DSB/RIF with dose [2]. Previously, we modeled radiation-induced cell death of human breast cells by first inferring the size of these domains from the dose dependence of low-LET RIF, and by associating a lethality factor to the number of pairs of DSBs in each RIF [1]. In this work, we first integrate the new NASA computer models RITCARD (Relativistic Ion Tracks, Chromosome Aberrations, Repair, and Damage) [3] and BDSTracks (Biological Damage by Stochastic Tracks) for a more accurate microdosimetry and a better model of the nuclear organization to predict the location of DSBs. A large array of particles and energy are simulated, covering more than three orders of magnitude for LET (~1-1000 keV/µm). Next, we extend our previous model to predict mutation frequencies by assuming that clustered DSBs increase mutation probability, which is formalized by the mutation frequency being linearly dependent on both the number of DSBs and the number of pairs of DSBs inside individual RIF. Linear coefficients are estimated so that simulations predict accurately mutation frequencies observed in Chinese hamster cells exposed to low-LET. Keeping these coefficients unchanged, we then predict mutation frequencies induced by HZE by simulating DSBs and obtain RBEs for mutations and cell death following the expected experimental bell shape for LET dependence. We also observe an orientation effect that needs to be confirmed, showing different RBE depending on the angle of the HZE beam hitting the main axis of the cell.

Plante, Ianik↗
NASA NTRS2020

Jet Noise Prediction Comparisons with Scale Model Tests and Learjet Flyover Data

Recent interest in commercial supersonic flight has highlighted the need to accurately predict Effective Perceived Noise Levels (EPNL) for aircraft and, since the dominant noise source at takeoff will likely be jet noise, specifically jet noise contributions. The current study compares predictions from historical jet-noise models within NASA’s Aircraft Noise Prediction Program and scale-model data to measurements made in a Learjet 25 flight test. The noise levels from the predictions and scale-model data were below those for the flight data by 2.5 – 3.5, 1 – 2, and 3 – 5 EPNdB for the SAE model, the Stone Jet model, and the scale-model data, respectively. Tones and broadband haystacks were identified in the flight spectra that are not associated with jet noise which increased the flight EPNL by at least 0.5 EPNdB over that computed from spectra with the tones and haystacks removed. The study highlights the need for accurate exhaust temperature measurements, aircraft flight position information, and averaging data across a line of microphones in flight tests. For example, a 100° F to 200° F difference in jet exhaust temperature is enough to explain the differences between flight, model scale, and prediction comparisons.

Henderson, Brenda↗
NASA NTRS2019

Increasing Pilot’s Understanding of Future Automation State – an Evaluation of an Automation State and Trajectory Prediction System

A pilot in the loop flight simulation study was conducted at NASA Langley Research Center to evaluate a trajectory prediction system. The trajectory prediction system computes a five-minute prediction of the lateral and vertical path of the aircraft given the current and intent state of the automation. The prediction is shown as a graphical representation so the pilots can form an accurate mental model of the future state. Otherwise, many automation changes and triggers are hidden from the flight crew or need to be consolidated to understand if a change will occur and the exact timing of the change. Varying dynamic conditions like deceleration can obscure the future trajectory and the ability to meet constraints, especially in the vertical dimension. Current flight deck indications of flight path assume constant conditions and do not adequately support the flight crew to make correct judgments regarding constraints. The study was conducted using ten commercial airline crews from multiple airlines, paired by airline to minimize procedural effects. Scenarios spanned a range of conditions that provided evaluation in a realistic environment with complex traffic and weather conditions. In particular, scenarios probed automation state and loss of state awareness. The technology was evaluated and contrasted with current state-of-the-art flight deck capabilities modeled from the Boeing 787. Objective and subjective data were collected from aircraft parameters, questionnaires, audio/video recordings, head/eye tracking data, and observations. This paper details findings about the trajectory prediction system including recommendations about further study.

Etherington, Timothy J.↗
NASA NTRSDate not supplied

Predicting Quadcopter Noise With The Lattice-Boltzmann Method

From small delivery Unmanned Aircraft Systems (UAS) to short-haul building-to-building aircraft, urban air mobility is gaining traction as a new approach to tackle the problem of transportation in densely populated areas. NASA’s vision for vertical lift vehicles is to capitalize on and improve unique capabilities to greatly benefit the United States’ growing civil flight requirements. This vision is embodied in the Revolutionary Vertical Lift Technology (RVLT)project [1]. Beyond safety, one of the chief concerns of communities where drones are becoming more popular is the noise they generate. Noise will undoubtedly be one of the major obstacles to public acceptance of any new urban air mobility technology. The ability to predict the acoustics of new conceptual aircraft with multiple rotors and complex fuselages is critical to enable the creation of quieter designs. The objective of this research is to build up a better physical understanding of the noise generated by a typical quadcopter drone and what it takes to predict it from first principles using computational fluid dynamics (CFD) with the Lattice-Boltzmann method (LBM). The specific goals are to establish best practices to predict multi-rotor and vehicle interaction noise with LBM, validate these predictions by comparing to wind tunnel measurements, and assess the computational cost necessary to obtain accurate predictions.

Francois Cadieux↗
NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRS2020

Machine Learning-Based Predictive Analytics for Aircraft Engine Conceptual Design

Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.

deep-learning↗
NASA NTRSDate not supplied

Jet Noise Prediction for Chevron Nozzle Flows with Wall-Modeled Large-Eddy Simulation

This paper presents results from ongoing research on jet noise prediction with wall-modeled large eddy simulations (WMLES) performed with the LAVA computational framework. In particular the focus of this study is on mixing enhancements from a single stream chevron nozzle at Reynolds number of 1×10(exp 6). Although the concept of chevron nozzles to reduce jet noise is not new, our understanding of its impact on the overall noise is still not well understood. As a first step towards predicting noise reduction due to mixing enhancement concepts from first principles with WMLES, we simulate the noise generated by a single stream chevron nozzle SMC001 as well as its equivalent axisymmetric round jet nozzle SMC000. Detailed comparisons are made with a dedicated experiment conducted at NASA’s Glenn Research Center and good agreement was achieved. Two different approaches to introduce a turbulent boundary layer were compared but show no major impact on the results. This is especially important given future work were multi-stream nozzles are considered and extended costs of resolving the internal BL would have a bigger cost impact. A permeable Ffowcs Williams Hawkings (FWH) surface enclosing the jet is used to predict far-field noise from the simulated flow-field and excellent comparison to microphone array measurements is achieved within the resolved frequency band. Sensitivity of far-field noise predictions to grid resolution is systematically documented. Near-field comparisons to PIV data shows great agreement for both velocity and normal stresses, however a systematic TKE overshoot at the nozzle exit is seen in the shear-layer. The paper shows a cost reduction of an order of magnitude compared to an earlier study of this configuration due to algorithmic and software improvements and demonstrates that WMLES can be used as a cost-competitive approach for jet noise predictions.

CST↗
NASA NTRS2021

Predicting near-saturated hydraulic conductivity in urban soils

Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (K(n)). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban K(n), 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban K(n), with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between K(n) and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems.

Jinshi Jian↗
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRSDate not supplied

Numerical Prediction of Heat Transfer Coefficients during Xenon Tank Fill in Microgravity

NASA’s Gateway will serve as an orbital outpost to enable sustained human lunar exploration and facilitate access to destinations beyond the Moon. To maintain sustainable deep space missions, on-orbit propellant transfer and refueling must be realized. One of Gateway’s critical components, the Power and Propulsion Element (PPE), will use solar electric propulsion to perform attitude control maneuvers and orbit transfers. Xenon will be used as the propellant for the PPE and stored in a composite overwrapped pressure vessel (COPV). During the propellant refueling process, xenon will compress causing the fluid and tank walls to warm. There is concern that the bond between the tank liner and composite may degrade at elevated temperatures, along with the possibility of the tank exceeding design pressure. While on-orbit testing is being proposed, numerical models are being used to predict the thermal response and reduce risk during a refueling operation in microgravity where natural convection is diminished. Accurate numerical models may be used to inform decisions for follow-on testing, or design and operation of refueling architectures. Challenges of modeling xenon tank fill in microgravity include non-ideal gas behavior, operating near the fluid’s critical point, and lack of experimental flight data. This study presents a computational fluid dynamics (CFD) model with conjugate heat transfer that is used to predict averaged heat transfer coefficients during tank filling. At the anticipated injection flow rates, the CFD results showed that forced convection dominates in microgravity. Initial results from a multi-node Thermal Desktop model that assumed a purely conducting fluid (Nu=1) over-predicted the CFD fluid temperature and pressure, while a similar model allowing for natural convection under-predicted. The heat transfer coefficients predicted by CFD were implemented into the multi-node model, and the COPV thermal response from the two computational approaches were compared with good agreement.

Heat Transfer↗