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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 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRS2008

James Webb Space Telescope (JWST) Project Status for the AIAA Working Group on Space Simulation

This brief presentation discusses developments in the design of the James Webb Space Telescope (JWST). The JWST master schedule is provided and its system architecture is described. Instruments include the Near Infrared Camaera, Near Infrared Spectrograph, Mid-Infrared Instrument, and the Fine Guidance Sensor. Compared to other telescopes the JWST has a 2.7 longer wavelength than the Hubble Telescope (HST), 38 times better sensitivity at K band that the HST NICMOS, and 8-24 times better angular resolution than the Spitzer Telescope. among other features. Engineering or verification models for all telescope instruments are being assembled and tested in the current year. Currently, good progress continues to be made on critical path items and the JWST is on track for a June 2013 launch.

Sabelhaus, Phil↗

NASA NTRS2017

Dynamic Analysis for a Geared Turbofan Engine with Variable Area Fan Nozzle

Aggressive design goals have been set for future aero-propulsion systems with regards to fuel economy, noise, and emissions. To meet these challenging goals, advanced propulsion concepts are being explored and current operating margins are being re-evaluated to find additional concessions that can be made. One advanced propulsion concept being evaluated is a geared turbofan with a variable area fan nozzle (VAFN), developed by NASA. This engine features a small core, a fan driven by the low pressure turbine through a reduction gearbox, and a shape memory alloy (SMA)-actuated VAFN. The VAFN is designed to allow both a small exit area for efficient operation at cruise, while being able to open wider at high power conditions to reduce backpressure on the fan and ensure a safe level of stall margin is maintained. The VAFN is actuated via a SMA-based system instead of a conventional system to decrease overall weight of the system, however, SMA-based actuators respond relatively slowly, which introduces dynamic issues that are investigated in this work. This paper describes both a control system designed specifically for issues associated with SMAs, and dynamic analysis of the geared turbofan VAFN with the SMA actuators. Also, some future recommendations are provided for this type of propulsion system.

N+3↗

NASA NTRS2017

High-Fidelity Multidisciplinary Design Optimization of Aircraft Configurations

To evaluate new airframe technologies we need design tools based on high-fidelity models that consider multidisciplinary interactions early in the design process. The overarching goal of this NRA is to develop tools that enable high-fidelity multidisciplinary design optimization of aircraft configurations, and to apply these tools to the design of high aspect ratio flexible wings. We develop a geometry engine that is capable of quickly generating conventional and unconventional aircraft configurations including the internal structure. This geometry engine features adjoint derivative computation for efficient gradient-based optimization. We also added overset capability to a computational fluid dynamics solver, complete with an adjoint implementation and semiautomatic mesh generation. We also developed an approach to constraining buffet and started the development of an approach for constraining utter. On the applications side, we developed a new common high-fidelity model for aeroelastic studies of high aspect ratio wings. We performed optimal design trade-o s between fuel burn and aircraft weight for metal, conventional composite, and carbon nanotube composite wings. We also assessed a continuous morphing trailing edge technology applied to high aspect ratio wings. This research resulted in the publication of 26 manuscripts so far, and the developed methodologies were used in two other NRAs. 1

Martins, Joaquim R. R. A.↗

NASA NTRS2018

Earth Science Deep Learning: Applications and Lessons Learned

Deep Learning: A subfield of machine learning; Algorithms inspired by function of the brain; Scales with amount of training data; Powerful tool without the need for feature engineering; Suitable for Earth Science applications. Deep Learning for Earth science at MSFC (Marshall Space Flight Center): Phenomena identification; Hurricane intensity (wind speed) estimation; Severe storm (hailstorm) detection; Transverse bands detection; Entity extraction for knowledge graph creation; Ephemeral water detection.

Labeled Data↗

NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗

NASA NTRS2022

Remote sensing-based vegetation and soil moisture constraints reduce irrigation estimation uncertainty

Understanding the human water footprint and its impact on the hydrological cycle is essential to inform water management under climate change. Despite efforts in estimating irrigation water withdrawals in earth system models, uncertainties and discrepancies exist within and across modeling systems conditioned by model structure, irrigation parameterization, and the choice of input datasets. Achieving model reliability could be much more challenging for data-sparse regions, given limited access to ground truth for parameterization and validation. Here, we demonstrate the potential of utilizing remotely sensed vegetation and soil moisture observations in constraining irrigation estimation in the Noah-MP land surface model. Results indicate that the two constraints together can effectively reduce model sensitivity to the choice of irrigation parameterization by 7%–43%. It also improves the characterization of the spatial patterns of irrigation and its impact on evapotranspiration and surface soil moisture by correcting for vegetation conditions and irrigation timing. This study highlights the importance of utilizing remotely sensed soil moisture and vegetation measurements in detecting irrigation signals and correcting for vegetation growth. Integrating the two remote sensing datasets into the model provides an effective and less feature engineered approach to constraining the uncertainty of irrigation modeling. Such strategies can be potentially transferred to other modeling systems and applied to regions across the globe.

Wanshu Nie↗

NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗

DOE OSTI2022

Matrix Multiply Performance of GPUs on Exascale-class HPE/Cray Systems

The computation of dense matrix-matrix products (GEMMs) is central to many modeling and simulation workloads as well as AI/ML deep learning campaigns. In fact, millions of dollars are spent annually on computing GEMMs, and large model training demands are increasing exponentially. Specialized processors such as GPUs are designed to perform well for these operations. However, the performance of GEMMs on GPUs can exhibit complex behaviors depending on many factors, making it challenging to optimize the performance of GEMMs on these processors. In this study we undertake an examination of GEMM performance on several leading GPU models taken from product lines of GPUs to be deployed in forthcoming exascale computing systems. We show results to illustrate the many factors that can affect performance of GEMMs on GPUs. We then present data collected from a large number of test runs for an example GEMM operation to show the dependence behaviors of GEMM rate on matrix dimensions. Finally, we show results from machine learning-based performance models using novel feature engineering methods to fit the measured performance, providing a potential basis for GEMM performance tuning and autotuning methods for GPUs. Recommendations are also given for how to achieve high GEMM performance on modern GPUs.

Melesse Vergara, Veronica↗

DOE OSTI2025

Tandem Predictions for HPC Jobs: Preprint

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1973

The Space Shuttle Main Engine and its maintenance features

The Space Shuttle Main Engine (SSME) is a reusable, high-performance rocket engine being developed to satisfy the performance, life, reliability, and operational requirements of the Space Shuttle Orbiter. The design includes simple, low-cost maintenance features resulting from a viable maintainability program dedicated to minimizing engine cost per flight.

Wheelock, V. J.↗

NASA NTRS1977

Space Shuttle Main Engine

Significant features of the Space Shuttle Main Engine (SSME) include a staged combustion power cycle, high area ratio nozzle expansion, throttling capability, and a computer operated control system. These main design features are discussed along with development test results.

Thompson, J. R., Jr.↗

NASA NTRS1980

Space Shuttle Main Engine

Significant features of the Space Shuttle Main Engine (SSME) include a staged combustion power cycle developing chamber pressure in excess of 3000 psia, high area ratio nozzle expansion, throttling capability, and a computer-operated engine control system. This paper examines the current status of the SSME with attention given to engine performance, system characteristics, and test results. A comparison of the SSME development and certification programs with engines successfully used in the Saturn Program is presented.

Thompson, J. R., Jr.↗

NASA NTRS1984

Dynamic property studies of Sterling engines

A description is given of the results of dynamic property tests that were carried out using a trial produced prototype of a 50 KW Sterling engine. The features of the engine are shown graphically. A high thermal efficiency is found in the low rotation region.

Tani, Y.↗

NASA NTRS1992

A survey of instabilities within centrifugal pumps and concepts for improving the flow range of pumps in rocket engines

Design features and concepts that have primary influence on the stable operating flow range of propellant-feed centrifugal turbopumps in a rocket engine are discussed. One of the throttling limitations of a pump-fed rocket engine is the stable operating range of the pump. Several varieties of pump hydraulic instabilities are mentioned. Some pump design criteria are summarized and a qualitative correlation of key parameters to pump stall and surge are referenced. Some of the design criteria were taken from the literature on high pressure ratio centrifugal compressors. Therefore, these have yet to be validated for extending the stable operating flow range of high-head pumps. Casing treatment devices, dynamic fluid-damping plenums, backflow-stabilizing vanes and flow-reinjection techniques are summarized. A planned program was undertaken at LeRC to validate these concepts. Technologies developed by this program will be available for the design of turbopumps for advanced space rocket engines for use by NASA in future space missions where throttling is essential.

Veres, Joseph P.↗

NASA NTRS1979

Advanced General Aviation Turbine Engine (GATE) study

The small engine technology requirements suitable for general aviation service in the 1987 to 1988 time frame were defined. The market analysis showed potential United States engines sales of 31,500 per year providing that the turbine engine sales price approaches current reciprocating engine prices. An optimum engine design was prepared for four categories of fixed wing aircraft and for rotary wing applications. A common core approach was derived from the optimum engines that maximizes engine commonality over the power spectrum with a projected price competitive with reciprocating piston engines. The advanced technology features reduced engine cost, approximately 50 percent compared with current technology.

Smith, R.↗

NASA NTRS1980

AiResearch QCGAT engine, airplane, and nacelle design features

The quiet, clean, general aviation turbofan engine and nacelle system was designed and tested. The engine utilized the core of the AiResearch model TFE731-3 engine and incorporated several unique noise- and emissions-reduction features. Components that were successfully adapted to this core include the fan, gearbox, combustor, low-pressure turbine, and associated structure. A highly versatile workhorse nacelle incorporating interchangeable acoustic and hardwall duct liners, showed that large-engine attenuation technology could be applied to small propulsion engines. The application of the mixer compound nozzle demonstrated both performance and noise advantages on the engine. Major performance, emissions, and noise goals were demonstrated.

Heldenbrand, R. W.↗

NASA NTRS1978

Gas turbine engine with convertible accessories

Drive means for connecting a gas turbine engine to its accessories are so constructed as to allow the accessories to be selectively positioned to any one of several predetermined circumferential positions about the perimeter of the engine. This feature permits convenient mounting of the same engine upon vehicles demanding radically different engine mounting arrangements.

Sargisson, D. F.↗

NASA NTRS1980

Orbit transfer vehicle engine study, phase A extension. Volume 1: Executive summary

A 1980 state-of-the-art advanced expander engine was configured that delivers near-optimum payload-performance and exhibits the simplicity reliability, and life characteristics required of the OTV mission. Engine optimization studies indicate that the performance of the conventional expander cycle engine can be improved substantially through combustion chamber surface area enhancement, thermal enhancement, turbine regeneration, and power cycle efficiency enhancement procedures. The 20K lb. thrust staged combustion engine can be adapted to operate at extended periods in the 1 to 2K lb. thrust regime. The study results indicate that long-life, high performnce operation in the low thrust mode can be achieved with minimum modifications (kitting) of the engine. Although the main contribution to mission success and crew safety stems from propulsion system redundancy, significant reliability can be gained from engine component redundancy, specific engine design features ensuring safety and reliability, including an engine health monitoring system, and from a total program approach to safety and reliability, such as that presently applied to the space shuttle.

Source record↗