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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 991 records · Page 55

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NASA NTRS1974

MSFC Skylab experimenter's reference

The methods and techniques for experiment development and integration that evolved during the Skylab Program are described to facilitate transferring this experience to experimenters in future manned space programs. Management responsibilities and the sequential process of experiment evolution from initial concept through definition, development, integration, operation and postflight analysis are outlined in the main text and amplified, as appropriate, in appendixes. Emphasis is placed on specific lessons learned on Skylab that are worthy of consideration by future programs.

Source record↗

NASA NTRS1981

Why surface-truth field study is needed in remote-sensing instruction

Especially designed field studies are needed in remote sensing technology transfer courses regardless of the field work provided by the students'/trainees' major disciplines because the remote sensing discipline has unique emphases and needs. Modification of existng schedules to include field work provides the equivalent of extending the duration of the program with the added benefit of enhancing learning achievements per actual program day. The process of surface truth field instruction, levels of student capabilities and stages in the development of surface truth field studies are discussed.

Wake, W. H.↗

NASA NTRS2009

Science Operations for the 2008 NASA Lunar Analog Field Test at Black Point Lava Flow, Arizona

Surface science operations on the Moon will require merging lessons from Apollo with new operation concepts that exploit the Constellation Lunar Architecture. Prototypes of lunar vehicles and robots are already under development and will change the way we conduct science operations compared to Apollo. To prepare for future surface operations on the Moon, NASA, along with several supporting agencies and institutions, conducted a high-fidelity lunar mission simulation with prototypes of the small pressurized rover (SPR) and unpressurized rover (UPR) (Fig. 1) at Black Point lava flow (Fig. 2), 40 km north of Flagstaff, Arizona from Oct. 19-31, 2008. This field test was primarily intended to evaluate and compare the surface mobility afforded by unpressurized and pressurized rovers, the latter critically depending on the innovative suit-port concept for efficient egress and ingress. The UPR vehicle transports two astronauts who remain in their EVA suits at all times, whereas the SPR concept enables astronauts to remain in a pressurized shirt-sleeve environment during long translations and while making contextual observations and enables rapid (less than or equal to 10 minutes) transfer to and from the surface via suit-ports. A team of field geologists provided realistic science scenarios for the simulations and served as crew members, field observers, and operators of a science backroom. Here, we present a description of the science team s operations and lessons learned.

Garry W. D.↗

NASA NTRS2011

Temporary Urine and Brine Stowage System (TUBSS) Materials Selection and Testing

Storing wastewater in the event of a system anomaly is a necessity for closed loop water recovery systems. The temporary urine and brine stowage system (TUBSS) is an assembly used to store and transfer pre-treated urine (PTU) and brine for processing or disposal at a later date. This paper describes the selection and testing of several candidate materials from both a chemical and material strength standpoint. In addition, this paper will provide results of testing as well as lessons learned from the project, culminating in the successful launch of the hardware.

Carrigan, Caitlin↗

DOE OSTI2023

CASTLE: Conflict Analysis Strategy Testing Laboratory Environment v.1.0.0

SAND2024-01743O The Conflict Analysis Strategy Testing Laboratory Environment (CASTLE) is a software framework that enables and simplifies building a novel, turn-based strategy game in which it can define its own rules, maps, pieces, and interactions. The software is for novice to experienced programmers with some knowledge of Unity3D, a tool used in game production. CASTLE includes a library of common game mechanics used for strategic wargames and traditional board games, such as cards, tokens, dice, and grid maps. It follows design principles popularized by the video game industry and uses singletons for managing portions of the code. CASTLE builds on Unity's component-based design and can respond to engine events during execution. Among the numerous user-friendly features: Build games quickly and cost-effectively Network in real-time and apply data to new games developed on the framework Host multiple participants online Connect rule- or machine learning-based agents to a CASTLE game to serve as opponents or to simulate games Collect data collection from players and in-game behaviors Create a survey to gather demographics or opinions from players Store data locally or save it to an external database through Representational State Transfer (REST) functions CASTLE, which was prototyped using Microsoft Azure, is also designed for easily distributing online games using popular cloud services. The multiplayer functionality includes an agent interface, allowing developers to construct AI players that can substitute for humans in any of the games. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fabian, Nathan↗

DOE OSTI2024

Concept Study of Robotic Camera-Based Foreign Object Detection for EV Wireless Charging

Wireless charging of an electric vehicle (EV) is an emerging charging technology promising convenient, autonomous, and highly efficient EV charging without requiring heavy gauge cables. However, due to the strong electromagnetic field created by this process that surrounds the wireless charger, the presence of foreign objects can detrimentally interact with it, thus affecting wireless power transfer (WPT) performance or leading to harmful and unwanted safety risks. This paper presents the results for a concept study on a robotic camera-based foreign object detection (FOD) system, as a supplement to the industry-existing overlapped FOD coil array method, for EV wireless charging. A Raspberry PI 4 control board and compatible Raspberry PI Camera Module 2 are used to implement camera-based object detection. The FOD program was developed using a state-of-the-art deep learning object detection model with the OpenCV and Pytorch library and is compatible with camera module hardware. A dry-run test with Raspberry PI and a camera module was conducted and the preliminary FOD function was verified. The feasibility assessment is also validated by comparing the performance of five existing state-of-the-art deep learning object detection models for vehicles, animals, persons, and metals subsets, respectively. Satisfactory performance on the benchmark datasets is observed by the tests, but further improvements are needed in future work when detecting small-sized metallic objects. A programable robotic car is also under development as ongoing work for carrying the Raspberry PI and camera module while moving for the maintenance process.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2002

Training, transfer, and retention of three-dimensional spatial memory in virtual environments

Human orientation requires one to remember and visualize spatial arrangements of landmarks from different perspectives. Astronauts have reported difficulties remembering relationships between environmental landmarks when imagined in arbitrary 3D orientations. The present study investigated the effects of strategy training on humans' 1) ability to infer their orientation from landmarks presented ahead and below, 2) performance when subsequently learning a different array, and 3) retention of configurational knowledge over time. On the first experiment day, 24 subjects were tested in a virtual cubic chamber in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures, they had to memorize the spatial relationships among the six pictures around them and learn to predict the direction to a specific picture when facing any view direction, and in any roll orientation. Half of the subjects ("strategy group") were taught methods for remembering picture groupings, while the remainder received no such training ("control group"). After learning one picture array, the procedure was repeated in a second. Accuracy (% correct) and response time learning curves were measured. Performance for the second array and configurational memory of both arrays were also retested 1, 7, and 30 days later. Results showed that subjects "learned how to learn" this generic 3D spatial memory task regardless of their relative orientation to the environment, that ability and configurational knowledge was retained for at least a month, that figure rotation ability and field independence correlate with performance, and that teaching subjects specific strategies in advance significantly improves performance. Training astronauts to perform a similar generic 3D spatial memory task, and suggesting strategies in advance, may help them orient in three dimensions.

Non-NASA Center↗

DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

BioSentinel: Leading the Way for Deep Space CubeSat Missions

Flagship science missions are not alone in Deep Space thanks to BioSentinel, a 6U spacecraft launched on Artemis-1. BioSentinel is one of the longest operating CubeSats beyond cislunar space. The subsystems and COTS components of the BioSentinel bus are a template for future deep space missions, and the lessons learned from over a year of operations will enable improved performance for the next missions. BioSentinel achieved its unprecedented performance for an SLS secondary payload due to preparation, planning, and a robust design. Pre-launch antenna and interface testing with both DSN and ESA confirmed command and data pathways and allowed for operational flexibility in the critical early hours post-deployment. Mission Operations simulations prior to launch identified potential risks and primed operators to respond in flight, preparing the team to react quickly to successfully detumble the spacecraft and enter a power-positive state. The spacecraft would not have survived without the inclusion of the trailblazing 3D-printed composite cold gas propulsion system. The non-standard tank geometry enabled efficient use of the limited space available in the CubeSat, as well as the capability to detumble the spacecraft and manage momentum, while providing sufficient margin to execute potential delta-V maneuvers. The Iris radio has operated for over 18 months with no significant issues. Initial Iris performance estimates have been accurate throughout the mission. BioSentinel continues to collect data on thermal conditions and to validate our performance models with real-world knowledge. We have received exemplary support from our DSN partners. Following the conclusion of the primary science mission, the Linear Energy Transfer (LET) Spectrometer continued to collect solar and galactic radiation data from its location in heliocentric orbit. The free space dataset offered by the BioSentinel LET is a valuable source of data for model validation and future mission planning. As the spacecraft travels farther from Earth it is poised to provide longitudinally distributed measurements of solar particle events during solar maximum. The lessons learned from BioSentinel suggest key areas to enhance performance. The ability to upload modified flight software can increase the stability of memory management. Additional heaters in the propulsion system design have already proven successful on the Starling mission. Streamlining mission operations can reduce costs, increase data return, and better utilize DSN time. Enhancements such as these will facilitate reliable, long-duration deep space exploration using the proven BioSentinel 6U CubeSat bus.

BioSentinel↗

DOE OSTI2023

Data-driven prediction of α IIb β 3 integrin activation paths using manifold learning and deep generative modeling

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of αI I b β3 integrin to learn a low-dimensional embedding of the configurational phase space. We then train deep generative models to learn an inverse mapping between the low-dimensional embedding and high-dimensional molecular space and use these models to interpolate the molecular configurations constituting the activation pathways between the experimentally characterized states. Furthermore, this work furnishes plausible predictions of integrin activation pathways and reports a generic and transferable multi-scale technique to predict transition pathways for biomolecular systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Machine learning-based optimization of air-cooled heat sinks

Machine learning-based models using Artificial Neural Network (ANN) and greedy search algorithm are used to optimize air-cooled parallel plate-finned heat sinks (PPFHSs) subjected to laminar flow over an extensive range of design parameters. Here, the thermal and hydraulic performances of PPFHSs are represented by heat transfer coefficient (h) and pressure drop (ΔP), respectively. Optimization objectives for PPFHS designs can vary from industry to industry depending on their design priorities. The present study proposes a novel and generalized optimization method that defines practical optimization objectives and provides an accurate optimization process to design effective PPFHSs for a wide range of industrial applications with different design requirements. Three optimization objectives are presented in this study: (i) the largest h PΔ, (ii) the largest h within a specified maximum allowed flow rate, and (iii) the lowest weight that maximizes h for operation within the maximum allowed flow rate. While the shortcoming of the first objective is demonstrated, the other two objectives are found to be suitable for designing effective heat sinks (HSs) across different applications. Results suggest a promising trend from the third objective to develop HSs with ~ 37-68% lower weight, 80-85% reduced ΔP, and negligible penalty in h compared with optimized HSs obtained from the second objective. However, since the third objective leads to HSs with thinner fins, structural analysis should be performed to ensure reliable operation of the HSs.

42 ENGINEERING↗

DOE OSTI2022

Deep modeling of plasma and neutral fluctuations from gas puff turbulence imaging

The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develop and demonstrate a technique to translate brightness measurements of HeI line radiation into local plasma fluctuations via a novel integrated deep learning framework that combines neutral transport physics and collisional radiative theory for the 3 3 D - 2 3 P transition in atomic helium with unbounded correlation constraints between the electron density and temperature. The tenets for experimental validity are reviewed, illustrating that this turbulence analysis for ionized gases is transferable to both magnetized and unmagnetized environments with arbitrary geometries. Based on fast camera data on the Alcator C-Mod tokamak, we present the first two-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density, revealing shadowing effects in a fusion plasma using a single spectral line.

47 OTHER INSTRUMENTATION↗

NASA NTRS1976

NASA philosophy concerning space stations as operations centers for construction and maintenance of large orbiting energy systems

Future United States plans for manned space-flight activities are summarized, emphasizing the long-term goals of achieving permanent occupancy and limited self-sufficiency in space. NASA-sponsored studies of earth-orbiting Space Station concepts are reviewed along with lessons learned from the Skylab missions. Descriptions are presented of the Space Transportation System, the Space Construction Base, and the concept of space industrialization (the processing and manufacturing of goods in space). Future plans for communications satellites, solar-power satellites, terrestrial observations from space stations, and manned orbital-transfer vehicles are discussed.

Freitag, R. F.↗

NASA NTRS1992

Technology transfer in the NASA Ames Advanced Life Support Division

This paper summarizes a representative set of technology transfer activities which are currently underway in the Advanced Life Support Division of the Ames Research Center. Five specific NASA-funded research or technology development projects are synopsized that are resulting in transfer of technology in one or more of four main 'arenas:' (1) intra-NASA, (2) intra-Federal, (3) NASA - aerospace industry, and (4) aerospace industry - broader economy. Each project is summarized as a case history, specific issues are identified, and recommendations are formulated based on the lessons learned as a result of each project.

Connell, Kathleen↗

NASA NTRS1997

NASA Virtual Conferences and Instruction Over the Internet

Distance learning is not new. Since the time that radio has embellished our culture distance learning has taken on may forms. With the onset of television, video tape and satellite link ups the world of multimedia has taken a presence in our remote learning environment. Now in the information age new models for bring the best education to people through out the world is in its early stages. Recent "Information Age" technological developments have made key advancements to distance learning through the greater bandwidths now available over the Internet and a broader communications infrastructure that extends to classrooms throughout the country and the world. Further, new software compression technology allows audio and video to be communicated over the Internet much more efficiently. Larger amounts of data can be transferred to remote sites at less cost. The purpose of this paper is to demonstrate the use of state-of-art technology in the educational community. The focus will be on virtual conferences, virtual instruction and remote education. The techniques herein have been developed by NASA and the University of North Dakota(UND) through the use of existing software and hardware purchased in the United States. NASA has awarded UND a grant for continued research in this area based on their pioneering effort to date. NASA has been conducting "Virtual Conferences" from Ames Research Center in order to make unique educational opportunities available to participants across the country and internationally. Through the use of this technical approach, hundreds of teachers have been able to attend events where physical or financial barriers traditionally prevented their attendance. This technique is currently being adopted by industry due to its scaleable merit.

Leon, Mark↗

NASA NTRS2023

Trajectory Design and Early Mission Operations for the Lunar IceCube Mission

The Lunar IceCube (LIC) mission, a Next Space Technologies for Exploration Partnerships (NSTEP) program selection, was launched as a rideshare onboard Artemis-I on November 16th 2022, and deployed into a high energy lunar flyby trajectory. The final destination of the mission was a polar elliptical lunar orbit from which it could conduct spectroscopy observations of the lunar surface; however, a near rectilinear halo orbit (NRHO) was planned to be used as a staging orbit that divided the lunar transfer and low-thrust spiral phases of the mission. This paper presents the process used to design the LIC transfer trajectory from the high-energy deployment state to a 9:2 synodic resonance NRHO. Additional analyses performed, to assess the critical deployment to lunar flyby phase of the trajectory and to generate recovery trajectories following a loss of contact with the spacecraft, are described as well. Lessons learned from working on the LIC mission are presented to inform the design of similar future CubeSat missions.

Robert E Pritchett↗

DOE OSTI2024

Efficient Parallelization of Irregular Applications on GPU Architectures

With the enlarging computation capacity of general Graphics Processing Units (GPUs), leveraging GPUs to accelerate parallel applications has become a critical topic in academia and industry. However, a wide range of irregular applications with the computation-/memory-intensive nature cannot easily achieve high GPU utilization. The challenges mainly involve the following aspects: first, data dependence leads to coarse-grained kernel and inefficient parallelism; second, heavy GPU memory usage may cause frequent memory evictions and extra overhead of I/O; third, specific computation patterns produce memory redundancies; last, workload balance and data reusability conjunctly benefit the overall performance, but there may exist a dynamic trade-off between them. Targeting these challenges, this dissertation proposes multiple optimizations to accelerate two real-world applications: many-body correlation functions to simulate nuclear physics in a large-scale scientific system; the other is the eALS-based matrix factorization recommendation system. To accelerate the calculations of many-body correlation functions, this dissertation presents three frameworks in GPU memory management and multi-GPU scheduling. Firstly, an optimized systematic GPU memory management framework, MemHC, utilizes a series of new memory reduction designs in GPU memory allocation, CPU/GPU communications, and GPU memory oversubscription. Secondly, an enhanced multi-GPU scheduling framework, MICCO, particularly by taking both data dimension (e.g., data reuse and data eviction) and computation dimension into account. MICCO designs a heuristic scheduling algorithm and a machine learning-based regression model to generate the optimal settings of a proposed new concept to manage the trade-off. Thirdly, a locality-aware multi-GPU scheduling framework. This scheduler leverages pipeline batch generation with a looking-ahead strategy by building local dependency graphs for memory transfer reduction and better data reuse, achieving up to 79.92% memory cost reduction and 1.67x speedup. To parallelize the eALS-based recommendation system, this dissertation proposes an efficient CPU/GPU heterogeneous recommendation system, HEALS. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. To mitigate the data dependence, HEALS presents a CPU/GPU collaboration model for both task parallelism and data parallelism with multiple kernel computation optimizations. In summary, this dissertation efficiently accelerates two typical irregular applications on GPUs by building four frameworks, including CPU/GPU collaboration, GPU memory management, and multi-GPU scheduling.

Wang, Qihan↗

DOE OSTI2025

Machine Learning-Assisted Recovery of Delicate Kinetic Information from Transient Reactor Experiments

Identifying active sites and their roles in chemical reaction steps remains a vital challenge in heterogeneous catalysis. Transient experiments offer a unique way to probe active sites and distinguish subtle kinetic features. Although physics-based analysis methods may be well-developed, they can be highly susceptible to experimental noise, and smoothing methods may erase or even distort important features; a smooth curve is not always the best curve. We demonstrate a new workflow for the direct interpretation of intrinsic kinetic information from exit flux curves measured in transient reactor experiments. This workflow contains three artificial neural networks (ANNs), including a noise reducer, a concentration predictor, and a rate predictor to analyze experimental data, followed by the virtual TAP (VTAP) physics-based reactor model and density functional theory (DFT) calculations of adsorption energies on specific sites. We use this workflow to analyze the data from experiments titrating Pt/Al 2 O 3 and Pt/SiO 2 catalysts with carbon monoxide (CO) in the temporal analysis of products (TAP) reactor. Our workflow separates the time-evolving chemical reaction and mass transfer information contained in the TAP pulse response. The existence of strong- and weak-binding sites on the Pt/Al 2 O 3 catalyst is observed in the catalyst titration experiment in the transient reactor. The structures of the strong- and weak-binding sites are then identified by using DFT calculations. We find that the Pt/SiO 2 catalyst has only strong-binding sites, which aligns with the inactive support effect of SiO 2 . We demonstrate how machine learning methods provide unique insights with high-resolution data analysis that cannot be achieved by using state-of-the-art physics-based methods.

Adsorption↗