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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018

The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment −PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.

forecasting↗
NASA NTRS2010

Performance Testing of Molten Regolith Electrolysis with Transfer of Molten Material for the Production of Oxygen and Metals on the Moon

Previously, we have demonstrated the production of oxygen by electrolysis of molten regolith simulants at temperatures near 1600 C. Using an inert anode and suitable cathode, direct electrolysis (no supporting electrolyte) of the molten silicate is carried out, resulting in the production of molten metallic products at the cathode and oxygen gas at the anode. Initial direct measurements of current efficiency have confirmed that the process offer potential advantages of high oxygen production rates in a smaller footprint facility landed on the moon, with a minimum of consumables brought from Earth. We now report the results of a scale-up effort toward the goal of achieving production rates equivalent to 1 metric ton O2/year, a benchmark established for the support of a lunar base. We previously reported on the electrochemical behavior of the molten electrolyte as dependent on anode material, sweep rate and electrolyte composition in batches of 20-200g and at currents of less than 0.5 A. In this paper, we present the results of experiments performed at currents up to 10 Amperes) and in larger volumes of regolith simulant (500 g - 1 kg) for longer durations of electrolysis. The technical development of critical design components is described, including: inert anodes capable of passing continuous currents of several Amperes, container materials selection, direct gas analysis capability to determine the gas components co-evolving with oxygen. To allow a continuous process, a system has been designed and tested to enable the withdrawal of cathodically-reduced molten metals and spent molten oxide electrolyte. The performance of the withdrawal system is presented and critiqued. The design of the electrolytic cell and the configuration of the furnace were supported by modeling the thermal environment of the system in an effort to realize a balance between external heating and internal joule heating. We will discuss the impact these simulations and experimental findings have on the design of a suitable prototype for lunar applications

Sibille, Laurent↗
NASA NTRS1987

The performance of multicomputer interconnection networks

The interdependency of nodes and multicomputer interconnection networks is examined using simple calculations based on the asymptotic properties of queueing networks. Methods are described for choosing interconnection networks that fit individual classes of applications. It is also shown how analytic models can be extended to benchmark existing interconnection networks.

Reed, Daniel A.↗
NASA NTRS2010

Cavitation, Flow Structure and Turbulence in the Tip Region of a Rotor Blade

Objectives: Measure the flow structure and turbulence within a Naval, axial waterjet pump. Create a database for benchmarking and validation of parallel computational efforts. Address flow and turbulence modeling issues that are unique to this complex environment. Measure and model flow phenomena affecting cavitation within the pump and its effect on pump performance. This presentation focuses on cavitation phenomena and associated flow structure in the tip region of a rotor blade.

Wu, H.↗
NASA NTRS2005

Capillary Driven Flows Along Differentially Wetted Interior Corners

Closed-form analytic solutions useful for the design of capillary flows in a variety of containers possessing interior corners were recently collected and reviewed. Low-g drop tower and aircraft experiments performed at NASA to date show excellent agreement between theory and experiment for perfectly wetting fluids. The analytical expressions are general in terms of contact angle, but do not account for variations in contact angle between the various surfaces within the system. Such conditions may be desirable for capillary containment or to compute the behavior of capillary corner flows in containers consisting of different materials with widely varying wetting characteristics. A simple coordinate rotation is employed to recast the governing system of equations for flows in containers with interior corners with differing contact angles on the faces of the corner. The result is that a large number of capillary driven corner flows may be predicted with only slightly modified geometric functions dependent on corner angle and the two (or more) contact angles of the system. A numerical solution is employed to verify the new problem formulation. The benchmarked computations support the use of the existing theoretical approach to geometries with variable wettability. Simple experiments to confirm the theoretical findings are recommended. Favorable agreement between such experiments and the present theory may argue well for the extension of the analytic results to predict fluid performance in future large length scale capillary fluid systems for spacecraft as well as for small scale capillary systems on Earth.

Golliher, Eric L.↗
NASA NTRS1988

Full configuration interaction benchmark calculations for TiH

Full configuration interaction (FCI) results for the 3F and 5F states of Ti and the 4Phi and 2Delta states of TiH are presented. While the coupled pair functional (CPF) or modified CPF approaches are found to work well for the 4Phi state of TiH, they do not perform as well for the 2Delta state. Although for mu, the CASSCF/MRCI methods do well for the 2Delta state, when the active space is expanded outside the normal valence definition, the dipole moment is only brought into agreement with the FCI results by natural orbital iterations.

Bauschlicher, Charles W., Jr.↗
NASA NTRS2011

Teledyne Energy Systems, Inc., Proton Exchange Member (PEM) Fuel Cell Engineering Model Powerplant. Test Report: Initial Benchmark Tests in the Original Orientation

Proton Exchange Membrane (PEM) fuel cell technology is the leading candidate to replace the alkaline fuel cell technology, currently used on the Shuttle, for future space missions. During a 5-yr development program, a PEM fuel cell powerplant was developed. This report details the initial performance evaluation test results of the powerplant.

Loyselle, Patricia↗
NASA NTRS1995

Metrics and Benchmarks for Visualization

What is a "good" visualization? How can the quality of a visualization be measured? How can one tell whether one visualization is "better" than another? I claim that the true quality of a visualization can only be measured in the context of a particular purpose. The same image generated from the same data may be excellent for one purpose and abysmal for another. A good measure of visualization quality will correspond to the performance of users in accomplishing the intended purpose, so the "gold standard" is user testing. As a user of visualization software (or at least a consultant to such users) I don't expect visualization software to have been tested in this way for every possible use. In fact, scientific visualization (as distinct from more "production oriented" uses of visualization) will continually encounter new data, new questions and new purposes; user testing can never keep up. User need software they can trust, and advice on appropriate visualizations of particular purposes. Considering the following four processes, and their impact on visualization trustworthiness, reveals important work needed to create worthwhile metrics and benchmarks for visualization. These four processes are (1) complete system testing (user-in-loop), (2) software testing, (3) software design and (4) information dissemination. Additional information is contained in the original extended abstract.

Uselton, Samuel P.↗
NASA NTRS1997

Application of the Discontinuous Galerkin Method to Acoustic Scatter Problems

The application of the quadrature-free form of the discontinuous Galerkin method to two problems from Category 1 of the Second Computational Aeroacoustics Workshop on Benchmark problems is presented. The method and boundary conditions relevant to this work are described followed by two test problems, both of which involve the scattering of an acoustic wave off a cylinder. The numerical test performed to evaluate mesh-resolution requirements and boundary-condition effectiveness are also described.

Atkins, H. L.↗
NASA NTRS2010

On Acoustic Source Specification for Rotor-Stator Interaction Noise Prediction

This paper describes the use of measured source data to assess the effects of acoustic source specification on rotor-stator interaction noise predictions. Specifically, the acoustic propagation and radiation portions of a recently developed coupled computational approach are used to predict tonal rotor-stator interaction noise from a benchmark configuration. In addition to the use of full measured data, randomization of source mode relative phases is also considered for specification of the acoustic source within the computational approach. Comparisons with sideline noise measurements are performed to investigate the effects of various source descriptions on both inlet and exhaust predictions. The inclusion of additional modal source content is shown to have a much greater influence on the inlet results. Reasonable agreement between predicted and measured levels is achieved for the inlet, as well as the exhaust when shear layer effects are taken into account. For the number of trials considered, phase randomized predictions follow statistical distributions similar to those found in previous statistical source investigations. The shape of the predicted directivity pattern relative to measurements also improved with phase randomization, having predicted levels generally within one standard deviation of the measured levels.

Nark, Douglas M.↗
NASA NTRS1988

A system for evaluating Ada implementations using synthesized benchmarks

A system is described that allows project designers to evaluate the suitability of an Ada implementation for a particular application using tailored benchmarks. Specification of the load that an application is expected to generate is written in an Ada-like notation. An Ada program is then synthesized that generates the specified load. Studies of the way in which performance varies with various parameters of the application can be performed by the system automatically.

Knight, John C.↗
NASA NTRS1990

Improvement of pump tubes for gas guns and shock tube drivers

In a pump tube, a gas is mechanically compressed, producing very high pressures and sound speeds. The intensely heated gas produced in such a tube can be used to drive light gas guns and shock tubes. Three concepts are presented that have the potential to allow substantial reductions in the size and mass of the pump tube to be achieved. The first concept involves the use of one or more diaphragms in the pump tube, thus replacing a single compression process by multiple, successive compressions. The second concept involves a radical reduction in the length-to-diameter ratio of the pump tube and the pump tube piston. The third concept involves shock heating of the working gas by high explosives in a cyclindrical geometry reusable device. Preliminary design analyses are performed on all three concepts and they appear to be quite feasible. Reductions in the length and mass of the pump tube by factors up to about 11 and about 7, respectively, are predicted, relative to a benchmark conventional pump tube.

Bogdanoff, D. W.↗
NASA NTRS2001

Particle Engulfment and Pushing (PEP): Past Micro-Gravity Experiments and Future Experimental Plan on the International Space Station (ISS)

The interaction of an insoluble particle with a growing solid-liquid interface (SLI) has been a subject of investigation for the four decades. For a metallurgist or a material scientist understanding the fundamental physics of such an interaction is relevant for applications that include distribution of reinforcement particles in metal matrix composites, inclusion management in castings, and distribution of Y2Ba1Cu1O5 (211) precipitates (flux pinning sites) in Y1Ba2Cu3O7 (123) superconducting crystals. The same physics is also applicable to other areas including geological applications (frost heaving in soils) and preservation of biological cells. Experimentally this interaction can be quantified in terms of a critical growth velocity, Vcr, of the SLI below which particles are pushed ahead of the advancing interface, and above which the particles are engulfed. Past experimental evidence suggests that this Vcr is an inverse function of the particle radius, R. In order to isolate the fundamental physics that governs such a relationship it is necessary to minimize natural convection at the SLI that is inherent in ground based experiments. Hence for the purpose of producing benchmark data (Vcr vs. R) PEP is a natural candidate for micro-gravity experimentation. Accordingly, experiments with pure Al containing a dispersion of ZrO2 particles and an organic analogue, succinonitrile (SCN) containing polystyrene particles have been performed on the LMS and USMP-4 mission respectively. In this paper we will summarize the experimental data that was obtained during these two micro-gravity missions and show that the results differ compared to terrestrial experiments. We will also discuss the basic elements of our analytical and numerical model and present a comparison of the predictions of these models against micro-gravity experimental data. Finally. we will discuss our future experimental plan that includes the ISS glovebox and MSRRl.

Sen, Subhayu↗
NASA NTRSDate not supplied

Hardware-Efficient Quantum Optimization Layered Algorithms and Experiments

Quantum optimization algorithms, such as QAOA, that implement parametrized stochastic optimization solvers attempt to identify low-energy solutions of Ising systems by exploiting available quantum effects in noisy-intermediate scale machines. Engineering a well-performing parametrized quantum optimization circuit is indeed an exercise in balancing the trade-off between expressivity and implementation complexity. We show that, for MaxCut QAOA circuits defined on native hardware topology (Rigetti’s Aspen Quantum Processors), error-mitigation techniques recover simulated features of the noiseless theory. Moreover, we explore a design space for QAOA-like ansatze that perform well in theory as well as in hardware for fully-connected problems. We also discuss how efficient coherence and entanglement detection methods that could be coupled with quantum optimization experiments require only linear overhead in benchmarking time.

quantum computing↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗
NASA NTRS2020

Data Accountability and Uncertainty Analysis for the Mars Science Laboratory

This paper presents machine learning-based approaches to automate and optimize the detection of volume loss for the downlink process of telemetry data from the Mars Curiosity Rover. The Curiosity observes volume loss and data corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the data flow. To resolve this issue, we created a data pipeline to accumulate data from various data sources in the downlink process and detect where the data is missed. In this paper, we benchmarked different methodologies based on the accuracy and excitability of them to identify whether a downlink data that is received to the ground system is complete or incomplete. Our results show that machine learning methods can improve the performance of the GDSA by 55% while the user can diagnose why data is missed and provide an explanation for the data accountability problem.

Chowdhury, Ameera↗
NASA NTRS1994

Inducer analysis/pump model development

Current design of high performance turbopumps for rocket engines requires effective and robust analytical tools to provide design information in a productive manner. The main goal of this study was to develop a robust and effective computational fluid dynamics (CFD) pump model for general turbopump design and analysis applications. A finite difference Navier-Stokes flow solver, FDNS, which includes an extended k-epsilon turbulence model and appropriate moving zonal interface boundary conditions, was developed to analyze turbulent flows in turbomachinery devices. In the present study, three key components of the turbopump, the inducer, impeller, and diffuser, were investigated by the proposed pump model, and the numerical results were benchmarked by the experimental data provided by Rocketdyne. For the numerical calculation of inducer flows with tip clearance, the turbulence model and grid spacing are very important. Meanwhile, the development of the cross-stream secondary flow, generated by curved blade passage and the flow through tip leakage, has a strong effect on the inducer flow. Hence, the prediction of the inducer performance critically depends on whether the numerical scheme of the pump model can simulate the secondary flow pattern accurately or not. The impeller and diffuser, however, are dominated by pressure-driven flows such that the effects of turbulence model and grid spacing (except near leading and trailing edges of blades) are less sensitive. The present CFD pump model has been proved to be an efficient and robust analytical tool for pump design due to its very compact numerical structure (requiring small memory), fast turnaround computing time, and versatility for different geometries.

Cheng, Gary C.↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗