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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Material Studies Related to the Use of NaK Heat Exchangers Coupled to Stirling Heater Heads

NASA has been supporting design studies and technology development that could provide power to an outpost on the Moon, Mars, or an asteroid. Technology development efforts have included fabrication and evaluation of components used in a Stirling engine power conversion system. Destructive material evaluation was performed on a NaK shell heat exchanger that was developed by the NASA Glenn Research Center (GRC) and integrated with a commercial 1 kWe Stirling convertor from Sunpower Incorporated. The NaK Stirling test demonstrated Stirling convertor electrical power generation using a pumped liquid metal heat source under thermal conditions that represent the heat exchanger liquid metal loop in a Fission Power Systems (FPS) reactor. The convertors were operated for a total test time of 66 hr at a maximum temperature of 823 K. After the test was completed and NaK removed, the heat exchanger assembly was sectioned to evaluate any material interactions with the flowing liquid metal. Several dissimilar-metal braze joint options, crucial for the heat exchanger transfer path, were also investigated. A comprehensive investigation was completed and lessons learned for future heat exchanger development efforts are discussed.

Locci, Ivan E.↗
NASA NTRSDate not supplied

The PACE-MAPP Algorithm: Simultaneous Aerosol and Ocean Products From Combined Polarimeter and Shortwave Infrared Measurements

PACE-MAPP collaborative algorithm project - Produce accurate aerosol optical and microphysical properties and ocean properties - Use a coupled atmosphere-ocean vector radiative transfer (VRT) model - Use accurate but fast Mie/SS/T-matrix LUTs - Use scientific machine learning to speed-up retrievals by 1000x (PACE-MAPP Neural Network) - PACE-MAPP is a multi-instrument polarimeter algorithm for SPEXone, HARP2, OCI shortwave infrared channels

Snorre Alfred Moen Stamnes↗
NASA NTRS2004

Microgravity Spray Cooling Research for High Powered Laser Applications

An extremely powerful laser is being developed at Goddard Space Flight Center for use on a satellite. This laser has several potential applications. One application is to use it for upper atmosphere weather research. In this case, the laser would reflect off aerosols in the upper atmosphere and bounce back to the satellite, where the aerosol velocities could be calculated and thus the upper atmosphere weather patterns could be monitored. A second application would be for the US. Air Force, which wants to use the laser strategically as a weapon for satellite defense. The Air Force fears that in the coming years as more and more nations gain limited space capabilities that American satellites may become targets, and the laser could protect the satellites. Regardless of the ultimate application, however, a critical step along the way to putting the laser in space is finding a way to efficiently cool it. While operating the laser becomes very hot and must be cooled to prevent overheating. On earth, this is accomplished by simply running cool tap water over the laser to keep it cool. But on a satellite, this is too inefficient. This would require too much water mass to be practical. Instead, we are investigating spray cooling as a means to cool the laser in microgravity. Spray cooling requires much less volume of fluid, and thus could be suitable for use on a satellite. We have inherited a 2.2 second Drop Tower rig to conduct our research with. In our experiments, water is pressurized with a compressed air tank and sprayed through a nozzle onto our test plate. We can vary the pressure applied to the water and the temperature of the plate before an experiment trial. The whole process takes place in simulated microgravity in the 2.2 second Drop Tower, and a high speed video camera records the spray as it hits the plate. We have made much progress in the past few weeks on these experiments. The rig originally did not have the capability to heat the test plate, but I did some heat transfer calculations and picked out a heater to order for the rig. I learned QBasic programming language to change the operating code for our drops, allowing us to rapidly cycle the spray nozzle open and closed to study the effects. We have derived an equation for flow rate vs. pressure for our experiment. We have recorded several videos of drops at different pressures, some with heated test plate and some without, and have noticed substantial differences in the liquid behavior. I have also changed the computer program to write a file with temperature vs. time profiles for the test plate, and once the necessary thermocouple comes in (it was ordered last week), we will have temperature profiles to accompany the videos. Once we have these temperature profiles to go with the videos, we will be able to see how the temperature is affected by the spray at different pressures, and how the spray changes its behavior once as the plate changes from hot to cool. With quantitative temperature data, we can then mathematically model the heat transfer from the plate to the cooling spray. Finally, we can look at the differences between trials in microgravity and those in normal earth gravity.

Zivich, Chad P.↗
NASA NTRS1995

Emergence of relations and the essence of learning: a review of Sidman's Equivalence relations and behavior: a research story. Book review

The author reviews and comments on the book Equivalence relations and behavior: a research story by Murray Sidman. Sidman's book reports his research about equivalence relations and competencies in children with mental retardation and how it relates to behavior. Sidman used the idea of stimulus-stimulus relations among features of the environment to develop his theories about equivalence relations. Experimental work with children and animals demonstrated their ability to use equivalence relations to learn new tasks. The subject received feedback and reinforcement for specific choices made during training, then was presented with new choices during testing. Results of the tests indicate that subjects were able to establish relations and retrieve them in different situations.

NASA Discipline Space Human Factors↗
NASA NTRS2022

pyCRTM: A Python Interface for the Community Radiative Transfer Model

The Community Radiative Transfer Model (CRTM) is a powerful and versatile scalar radiative transfer model for satellite data assimilation and remote sensing applications. It is implemented as an object-oriented Fortran library, enabling flexible code development and optimal runtime performance on clusters. The downsides of the Fortran interface are a steep learning curve for students and the reduced productivity of users that is typical for static compiled languages, in contrast to dynamic interpreted languages like Python. pyCRTM is a new software framework that directly interfaces the CRTM Fortran data structures and procedures in Python, leveraging both the simplicity and ease of use of Python syntax as well as the flexibility arising from the vast contemporary Python ecosystem. The goal of pyCRTM is to lower the barrier of entry for university students to learn and use the CRTM and to boost the productivity of researchers seeking to create new methods in radiative transfer and data assimilation, or seeking to apply the CRTM to study atmospheric phenomena without having to go through the pre-existing complexity of the CRTM Fortran interface.

Python↗
NASA NTRSDate not supplied

Fast Machine Learning Lidar Surrogate Simulator: Pristine Clear Sky

The simulations of lidar signals and retrievals rely on a range of optic-physical models, such as radiative transfer models, particle scattering and absorption models, along with the output data from atmospheric physical models. Integrating these different models to represent signals of a lidar system is computationally expensive, and performing backward retrievals can be complex and ambiguous. However, with the advantages of Machine Learning, there is a new potential for building effective lidar signal database linked to corresponding atmospheric profiles. For this project, we are developing a fast pre-trained neural network as the lidar surrogate simulator using simulated data for a CALIPSO-like lidar (355 nm, 532nm, and 1064nm), and a CO2 differential absorption lidar (DIAL) near 1571nm. Specifically, we utilize a long short-term memory (LSTM) model to map the relationships between atmospheric profiles (pressure, temperature, air density and CO2 mixing ratio) and lidar signals. This approach allows us to build machine learning based simulators that can reconstruct lidar signals at specific bands from MERRA reanalysis data, and perform retrievals of atmospheric profiles using lidar signals at various wavelengths. As a first step, the results show the potential of this method to establish a foundational model for sensor signals. This model offers the promise of enabling both accurate predictions and rapid retrievals, providing a more efficient approach to signal processing and analysis.

Shan Zeng↗
DOE OSTI2024

Abstract for CRADA between National Energy Technology Laboratory and Shell International Exploration & Production, Inc

Introducing CO₂ into geothermal systems as a working fluid in reservoirs can enhance geothermal conductivity, production, and pressure maintenance. A cross-disciplinary interaction of geothermal reservoir stimulation and CO₂ utilization satisfies renewable energy demands and operations that support sustainable energy infrastructure. Challenges to implementing CO₂-stimulated geothermal enhancement (CS-GE) include (1) accurately characterizing and imaging CO₂-stimulated geothermal reservoirs; (2) quantitatively inferring CS-GE evolution under current and future engineered conditions for cost effective operations; and (3) monitoring resources by improving observational methods to advance the understanding of complex geothermal systems for sweep efficiency and, ultimately, cost effectiveness. NETL and Shell will collaborate under this CRADA to develop software to image key features, including CO₂-stimulated fracture imaging, CO₂ fluid sweep imaging and heat transfer and exchange imaging by leveraging available datasets and applying advanced Artificial Intelligence/Machine Learning (AI/ML), multi-level data analytics and data/information fusion to better understand the comprehensive mechanisms of CO₂-stimulated geothermal systems.

15 GEOTHERMAL ENERGY↗
NASA NTRS1996

The Study of the Relationship between Probabilistic Design and Axiomatic Design Methodology

This program report is the final report covering all the work done on this project. The goal of this project is technology transfer of methodologies to improve design process. The specific objectives are: 1. To learn and understand the Probabilistic design analysis using NESSUS. 2. To assign Design Projects to either undergraduate or graduate students on the application of NESSUS. 3. To integrate the application of NESSUS into some selected senior level courses in Civil and Mechanical Engineering curricula. 4. To develop courseware in Probabilistic Design methodology to be included in a graduate level Design Methodology course. 5. To study the relationship between the Probabilistic design methodology and Axiomatic design methodology.

Onwubiko, Chinyere↗
NASA NTRS1976

Offsetting Gravity

The Coastal Center and the NASA Biomedical Team are working together to adapt a suspension device that simulates weightlessness. Attempts to approximate weightlessness have led astronauts underwater and into harnesses that suspend all or part of their weight. One such device, built at Langley Research Center, is being transferred to rehabilitation work. It can help a person walk and re-learn muscular coordination following a stroke. Hospitals could use it to lift handicapped persons.

Source record↗
NASA NTRS2011

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

Tang, Liang↗
NASA NTRS2011

Eta Carinae: An Observational Testbed for 3-D Interacting Wind Modeling

Eta Car, with its very massive interacting winds, provides shocked arc-like structures dense enough to trace in forbidden emission lines out to 0.7" (1700 AU). As the massive binary is in a very elliptical orbit (e approx. 0.9), the spatial and velocity structures of these winds change over the 5.54 year period. We can tract ionization structures by several forbidden emission lines. With the addition of radiative transfer on a time-step frame-by-frame basis, we are learning much new information on the ballistic structures, and may gain insight on how molecules and dust might form in these very massive systems.

Gull, Theodore↗
DOE OSTI2025

Tensorized Interior Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator

Building energy simulation is a critical tool for developing and testing advanced control strategies, such as Reinforcement Learning (RL), to provide demand flexibility and affordable energy costs. The recently introduced Smart Buildings Control Suite (sbsim) provides a lightweight, scalable, and data-calibrated simulation environment based on a 2D finite-difference model. However, the initial model primarily focused on conductive and convective heat transfer, neglecting the significant impact of long-wave radiative heat exchange between interior surfaces. This paper presents a significant extension to the sbsim framework by incorporating a physically-grounded model for interior radiative heat transfer. Our primary contribution is the development and integration of a fully tensorized radiative heat transfer module, which preserves the computational efficiency and scalability of the original simulator. This was achieved by developing a pipeline for view factor calculation, including an algorithm to identify directly seeing surfaces within complex floor plans, and formulating the net radiation equations for efficient execution on modern hardware accelerators. We validate the numerical accuracy of our tensorized implementation by comparing its results against a traditional iterative approach, demonstrating identical outcomes. This enhancement increases the physical fidelity of sbsim, enabling more accurate training of RL agents for building energy optimization.

Ham, Sang woo↗
DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗
DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗
NASA NTRS1992

A radial basis function neurocomputer implemented with analog VLSI circuits

An electronic neurocomputer which implements a radial basis function neural network (RBFNN) is described. The RBFNN is a network that utilizes a radial basis function as the transfer function. The key advantages of RBFNNs over existing neural network architectures include reduced learning time and the ease of VLSI implementation. This neurocomputer is based on an analog/digital hybrid design and has been constructed with both custom analog VLSI circuits and a commercially available digital signal processor. The hybrid architecture is selected because it offers high computational performance while compensating for analog inaccuracies, and it features the ability to model large problems.

Watkins, Steven S.↗
NASA NTRS1993

An ASIC memory buffer controller for a high speed disk system

The need for large capacity, high speed mass memory storage devices has become increasingly evident at NASA during the past decade. High performance mass storage systems are crucial to present and future NASA systems. Spaceborne data storage system requirements have grown in response to the increasing amounts of data generated and processed by orbiting scientific experiments. Predictions indicate increases in the volume of data by orders of magnitude during the next decade. Current predictions are for storage capacities on the order of terabits (Tb), with data rates exceeding one gigabit per second (Gbps). As part of the design effort for a state of the art mass storage system, NASA Langley has designed a 144 CMOS ASIC to support high speed data transfers. This paper discusses the system architecture, ASIC design and some of the lessons learned in the development process.

Hodson, Robert F.↗
NASA NTRS2000

Modal Survey Test of the SOTV 2X3 Meter Off-Axis Inflatable Concentrator

NASA's Marshall Space Flight Center has had several projects involving inflatable space structures. Projects in solar thermal propulsion have had the most involvement, primarily inflatable concentrators. A flight project called Shooting Star Experiment initiated the first detailed design, analysis and testing effort involving an inflatable concentrator that supported a Fresnel lens. The lens was to concentrate the sun's rays to provide an extremely large heat transfer for an experimental solar propulsion engine. Since the conclusion of this experiment, research and development activities for solar propulsion at Marshall Space Flight Center have continued both in the solar propulsion engine technology as well as inflatable space structures. Experience gained in conducting modal survey tests of inflatable structures for the Shooting Star Experiment has been used by dynamic test engineers at Marshall Space Flight Center to conduct a modal survey test on a Solar Orbital Transfer Vehicle (SOTV) off-axis inflatable concentrator. This paper describes how both previously learned test methods and new test methods that address the unique test requirements for inflatable structures were used. Effects of the inherent nonlinear response of the inflatable concentrator on test methods and test results are noted as well. Nine analytical mode shapes were successfully correlated to test mode shapes. The paper concludes with several "lessons learned" applicable to future dynamics testing and shows how Marshall Space Flight Center has utilized traditional and new methods for modal survey testing of inflatable space structures.

Engberg, Robert C.↗