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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Development of a high Reynolds number quiet tunnel for transition research

High noise levels in conventional supersonic and hypersonic wind tunnels prevent further advances in transition research. Recent data confirm previous results that transition is dominated by tunnel noise when the rms pressure intensities exceed about 1%. High facility noise levels also dominate fluctuating pressure loads under fully turbulent boundary layers. Recent data on the power spectra of surface pressures indicate that the basic structure of turbulent boundary layers may be modified by high facility noise levels. Experimental data for current techniques to control and reduce noise levels in supersonic and hypersonic wind tunnels by laminarization of nozzle wall boundary layers and by noise radiation shields are presented. These results and possible effects of Taylor-Gortler vortices observed in a nozzle wall boundary layer are used to predict the limits of quiet performance for a proposed 20-in. quiet tunnel.

Beckwith, I. E.↗
NASA NTRS1977

Error rate information in attention allocation pilot models

The Northrop urgency decision pilot model was used in a command tracking task to compare the optimized performance of multiaxis attention allocation pilot models whose urgency functions were (1) based on tracking error alone, and (2) based on both tracking error and error rate. A matrix of system dynamics and command inputs was employed, to create both symmetric and asymmetric two axis compensatory tracking tasks. All tasks were single loop on each axis. Analysis showed that a model that allocates control attention through nonlinear urgency functions using only error information could not achieve performance of the full model whose attention shifting algorithm included both error and error rate terms. Subsequent to this analysis, tracking performance predictions for the full model were verified by piloted flight simulation. Complete model and simulation data are presented.

Faulkner, W. H.↗
NASA NTRS1984

Computational modeling of jet induced mixing of cryogenic propellants in low-G

The SOLA-ECLIPSE Code is being developed to enable computational prediction of jet induced mixing in cryogenic propellant tanks in a low-gravity environment. Velocity fields, predicted for scale model tanks, are presented which compare favorably with the available experimental data. A full scale liquid hydrogen tank for a typical Orbit Transfer Vehicle is analyzed with the conclusion that coupling an axial mixing jet with a thermodynamic vent system appears tobe a viable concept for the control of tank pressure. Previously announced in STAR as N84-25000

Hochstein, J. I.↗
NASA NTRS1984

Report of the Panel on Aerodynamics

Progress in aerodynamics over the past 50 years has been evidenced by the development of increasingly sophisticated and efficient flight vehicles throughout the flight spectrum. Advances have generally arisen in an evolutionary manner from experience gained in wind tunnel testing, flight testing, and improvements in analytical and computational capabilities. As a result of this evolutionary development, both military and commercial vehicles operate at a relatively high efficiency level. This observation plus the fact that airplanes have not changed appreciably in outward appearance over recent years has led some skeptics to conclude incorrectly that aerodynamics is a mature technology, with little to be gained from further developments in the field. It is of interest to note that progress in aerodynamics has occurred without a thorough understanding of the fundamental physics of flow, turbulence, vortex dynamics, and separated flow, for example. The present understanding of transition, turbulence, and boundary layer separation is actually very limited. However, these fundamental flow phenomena provide the key to reducing the viscous drag of aircraft. Drag reduction provides the greatest potential for increased flight efficiency from the standpoint of both saving energy and maximizing performance. Recent advances have led to innovative concepts for reducing turbulent friction drag by modifying the turbulent structure within the boundary layer. Further advances in this basic area should lead to methods for reducing skin friction drag significantly. The current challenges for military aircraft open entirely new fields of investigation for the aerodynamicist. The ability through very high speed information processing technology to totally integrate the flight and propulsion controls can permit an aircraft to fly with "complete abandon," avoiding departure, buffet, and other undesirable characteristics. To utilize these new control concepts, complex aerodynamic phenomena will have to be understood, predicted, and controlled. Current requirements for military aircraft include configuration optimization through a widened envelope from subsonic to supersonic and from low to high angles of attack. This task is further complicated by requirements for control of observables. These challenging new designs do not have the luxury of a large experimental data base from which to optimize for various parameter combinations. Consequently, there exists a strong need for better techniques, both experimental and computational, to permit design optimization in a complete sense.

Bradley, Richard G.↗
NASA NTRS2017

Tank System Integrated Model: A Cryogenic Tank Performance Prediction Program

Accurate predictions of the thermodynamic state of the cryogenic propellants, pressurization rate, and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning for future space exploration missions. This Technical Memorandum (TM) presents the analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, mixing, and condensation on the tank wall. This TM also includes comparisons of TankSIM program predictions with the test data andexamples of multiphase mission calculations.

Bolshinskiy, L. G.↗
NASA NTRSDate not supplied

Packed Bed Reactor Experiment (PBRE)-2: Pressure Drop Measurements in Microgravity

Single and two-phase flow through porous media are encountered on ground and in space in numerous applications related to life support systems, fuel cells, chemical/materials processing and transporting nutrients to plants. These systems operate differently in the microgravity environment encountered in space travel because the density differences no longer cause the phases to separate or “drain” under the body force of gravity. In the absence of gravity, the interfacial or capillary forces play a more significant role in determining the operational variables such as phase distribution, liquid holdup, and pressure drop especially when the liquid inertia and viscous forces are at minimum. Lower liquid flow rates in air-water two-phase flows represent the case for most of the space processes that depend on two-phase flows in porous media. The Packed Bed Reactor Experiment (PBRE) was developed and flown on the International Space Station (ISS) by NASA to extend a series of fundamental studies of gas-liquid flows through porous media. The goal of the this series of flight experiments (PBRE-1 and PBRE-2) is to better understand the hydrodynamics of two phase flows in porous media in flow regimes dominated by inertia and viscous effects and understanding the role of the interfacial forces and their role in determining the flow dynamics. The flight experiments were preceded by a series of low gravity experiments performed on the low gravity aircraft. These experiments led to the development of a semi-empirical two-phase pressure drop and flow pattern transition in support of a number of reactor beds planned for water reclamation processes onboard of the international space station. The PBRE flight experiment was designed to deliver a wide range of tightly controlled gas (nitrogen) and liquid (water) flows to one of two interchangeable test sections differing only in the type of internal packing material. In this paper, we describe the PBRE-2 experiment and present preliminary low gravity data on pressure drop and compare with semi-empirical model pressure drop predictions. This work is supported by the National Aeronautics and Space Administration (NASA) under grant no. 80NSSC20K0830

Suratman Number↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
NASA NTRS1976

Environmental parameters of the Tennessee River in Alabama. 2: Physical, chemical, and biological parameters

Physical, chemical and biological water quality data from five sites in the Tennessee River, two in Guntersville Reservoir and three in Wheeler Reservoir were correlated with climatological data for three annual cycles. Two of the annual cycles are for the years prior to the Browns Ferry Nuclear Power Plant operations and one is for the first 14 months of Plant operations. A comparison of the results of the annual cycles indicates that two distinct physical conditions in the reservoirs occur, one during the warm months when the reservoirs are at capacity and one during the colder winter months when the reservoirs have been drawn-down for water storage during the rainy months and for weed control. The wide variations of physical and chemical parameters to which the biological organisms are subjected on an annual basis control the biological organisms and their population levels. A comparison of the parameters of the site below the Power plant indicates that the heated effluent from the plant operating with two of the three reactors has not had any effect on the organisms at this site. Recommendations given include the development of prediction mathematical models (statistical analysis) for the physical and chemical parameters under specific climatological conditions which affect biological organisms. Tabulated data of chemical analysis of water and organism populations studied is given.

Rosing, L. M.↗
DOE OSTI2026

Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models

This research introduces a comprehensive framework for creating and deploying a digital twin platform for continuous monitoring and predictive maintenance within industrial settings. Through utilizing advanced technologies, including Unreal Engine 5, Unity 3D, the Message Queue Telemetry Transport protocol, Random Forest machine learning algorithms, and Large Language Models (LLMs), we establish a platform that digitally reproduces physical equipment and translates digital controls into real-world actions. This facilitates preventive maintenance approaches and improves operational effectiveness. The digital twin platform gathers sensor data from operational equipment, analyzes it using machine learning, and delivers practical insights to prevent potential malfunctions and enhance equipment performance. Furthermore, the incorporation of a web portal enables efficient monitoring and access to historical data, educational materials, and equipment status information. Preliminary findings indicate that digital twins can transform industrial equipment management and maintenance methodologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A hierarchical gray-box dynamic modeling methodology for direct-expansion cooling systems to support control stability analysis; Méthodologie de modélisation dynamique hiérarchique de type boîte grise pour les systèmes de refroidissement à détente directe afin de soutenir l'analyse de stabilité de la commande

In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.

42 ENGINEERING↗
NASA NTRS2003

Test Data Analysis of a Spray Bar Zero-Gravity Liquid Hydrogen Vent System for Upper Stages

To support development of a zero-gravity pressure control capability for liquid hydrogen (LH2), a series of thermodynamic venting system (TVS) tests was conducted in 1996 and 1998 using the Marshall Space Flight Center (MSFC) multipurpose hydrogen test bed (MHTB). These tests were performed with ambient heat leaks =20 and 50 W for tank fill levels of 90%, 50%, and 25%. TVS performance testing revealed that the spray bar was highly effective in providing tank pressure control within a 7-kPa band (131-138 Wa), and complete destratification of the liquid and the ullage was achieved with all test conditions. Seven of the MHTB tests were correlated with the TVS performance analytical model. The tests were selected to encompass the range of tank fill levels, ambient heat leaks, operational modes, and ullage pressurants. The TVS model predicted ullage pressure and temperature and bulk liquid saturation pressure and temperature obtained from the TVS model were compared with the test data. During extended self-pressurization periods, following tank lockup, the model predicted faster pressure rise rates than were measured. However, once the system entered the cyclic mixing/venting operational mode, the modeled and measured data were quite similar.

Hedayat, A.↗
NASA NTRS2009

Validation of an Acoustic Impedance Prediction Model for Skewed Resonators

An impedance prediction model was validated experimentally to determine the composite impedance of a series of high-aspect ratio slot resonators incorporating channel skew and sharp bends. Such structures are useful for packaging acoustic liners into constrained spaces for turbofan noise control applications. A formulation of the Zwikker-Kosten Transmission Line (ZKTL) model, incorporating the Richards correction for rectangular channels, is used to calculate the composite normalized impedance of a series of six multi-slot resonator arrays with constant channel length. Experimentally, acoustic data was acquired in the NASA Langley Normal Incidence Tube over the frequency range of 500 to 3500 Hz at 120 and 140 dB OASPL. Normalized impedance was reduced using the Two-Microphone Method for the various combinations of channel skew and sharp 90o and 180o bends. Results show that the presence of skew and/or sharp bends does not significantly alter the impedance of a slot resonator as compared to a straight resonator of the same total channel length. ZKTL predicts the impedance of such resonators very well over the frequency range of interest. The model can be used to design arrays of slot resonators that can be packaged into complex geometries heretofore unsuitable for effective acoustic treatment.

Howerton, Brian M.↗
DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗
DOE OSTI2020

Battery Equivalent Model for Residential HVAC

Flexible loads, especially heating, ventilation, and air-conditioning (HVAC) systems can be used to provide a battery-like service to the power grid by varying their demand up and down over a baseline. Recent work has reported that the round-trip efficiency for providing grid services with HVAC systems can be close to 1. Inspired by this promising result, in this work, we rigorously develop a battery equivalent model (BEM) for residential HVAC systems. Relying only on typically available data, the proposed BEM will bridge the observability and controllability gaps between utility companies and customers. The proposed BEM will enable the utility companies to manage the flexible HVAC devices as easy as charging/discharging the conventional energy storage devices. Validated physics-based models are used to train the BEM, whose prediction accuracy is illustrated through a simulation study.

Dong, Jin↗
DOE OSTI2023

On-Line Prediction of Resistant Force During Soil–Tool Interaction

For off-road vehicles such as excavators and wheel loaders, a large portion of energy is consumed to overcome the soil resistant force in the digging process. For optimal control of the digging tool, a high-fidelity model of the soil–tool interaction force is important to reduce energy consumption. Here, in this paper, an on-line soil resistant force prediction method is proposed. In this method, a hybrid model, which combines a physical model and a data-driven model, is used for the force prediction. In addition, the parameters of the hybrid model can be updated on-line based on real-time data. Comparisons with experimental data demonstrate that the proposed prediction method has an average error of around 12.7%.

42 ENGINEERING↗
DOE OSTI2022

Benchmark Specification for FFTF LOFWOS Test #13

The Fast Flux Test Facility (FFTF) at the Hanford site in Washington was designed by the Westinghouse Electric Corporation for the U.S. Department of Energy. FFTF was a 400 MW thermal, oxide-fueled, liquid sodium cooled test reactor, built to assist development and testing of advanced fuels and materials for fast breeder reactors. After reaching criticality in 1980, FFTF operated until 1992, providing the U.S. Department of Energy (DOE) with the means to test fuels, materials, and other components in a fast neutron flux environment. In July 1986, a series of unprotected transients (with the plant protection system intentionally disabled) were performed in FFTF as part of the passive safety demonstration program. Among these were thirteen loss of flow without scram (LOFWOS) tests. The goals of this program included confirming the liquid metal reactor safety margins, providing data for computer code validation, and demonstrating the inherent and passive safety benefits of specific design features. The test defined in this benchmark is LOFWOS Test #13, which was initiated at 50% power and 100% flow with the pump pony motors turned off. This benchmark specification is intended to support collaborative efforts within international partnerships on the validation of simulation tools and models in the area of Sodium-cooled Fast Reactor (SFR) safety. Validated tools and models are needed to evaluate SFR inherent safety characteristics and assess the impact of passive design features in response to accident initiators. Comparisons with experimental data and the results of safety analyses from other groups create unique opportunities to improve predictive capabilities of computational codes and methods for SFR modeling and simulation. The conditions of the LOFWOS test along with the feedback from FFTF’s limited free bow core restraint system and the novel passive reactivity control Gas-Expansion Modules (GEMs) pose a very challenging and uniquely valuable benchmark exercise.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 1

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024). Three distinct rounds of FSP experiments were performed by the experimental team, producing replicate samples utilizing across different nominal processing conditions (Condition IDs) listed in Table 1. The starting material on which FSP was applied was commercially available unprocessed stainless-steel type 316L material. Chosen processing conditions were very diverse, and some were intentionally chosen to produce defects. Several samples experienced tool breakage during experimentation, so a full set of three replicates was not produced for every nominal processing condition.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 2

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗