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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Developing Nontraditional Partnerships to Disseminate the Space Science Story

NASA Space Science Days (NSSD) was established in 2004 to bring the story of the Mars Exploration Rovers (MER) to a community far removed from areas NASA traditionally serves. The original NSSD invited 400 5th and 8th graders from the Texas Rio Grande Valley area to the University of Texas Brownsville campus to participate in a one day Saturday event filled with information about MER with related hands on activities. Currently the program has grown to over 700 5th and 8th grade participants who are mentored by trained university students from several Texas universities and community colleges and growing to include universities from throughout the U.S. A collaboration between three major institutions: The NASA Johnson Space Center (JSC) Astromaterials Research and Exploration Science Directorate (ARES), the Society of Hispanic Professional Engineers/Advancing Hispanic Excellence in Technology Engineering, Math, and Science, (SHPE/AHETEMS), and the University of Texas at Brownsville (UTB) has been established to enable the dissemination of Solar System related educational materials throughout the U.S. Already in its 8th year, UTB has developed and tested a NSSD model that has successfully disseminated space science materials to students throughout South Texas Rio Grande Valley. With this newly formed collaboration this model s expansion will allow trained SHPE students and professionals to conduct events throughout its established nation-wide delivery systems. Each year a new NSSD site will be established through an application process solicited from SHPE student and professional chapters. Once a chapter is awarded, upper-level high school and university students will travel to NASA- JSC for a two day workshop where students learn about the current year s theme and are trained to present hands-on activities related to the theme. Additional training by ARES and UTB occurs one month before the new event in their own communities. Both local middle school teachers and mentors are trained locally. This allows the teachers time to prepare their students with the background material for NSSD and give the mentors time to decide which activity they feel comfortable with to lead. Several events using this process have already been successful and an increase in interest from SHPE chapters wanting to establish their NSSD event is growing.

Galindo, Charles↗
DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
DOE OSTI2026

Temperature‐Dependent Crystallization in Two‐Step Perovskite Deposition Revealed by In Situ GIWAXS and Machine Learning‐Guided Analysis

The performance and stability of perovskite solar cells are strongly governed by the crystallization behavior of their active layer. In two-step sequential deposition, early-stage film formation plays a decisive role in determining final phase purity and device quality. Guided by a data-driven analysis of nearly 39 000 devices in the FAIR perovskite database, we identified solvent-mediated quenching and thermal processing as key variables affecting power conversion efficiency (PCE), particularly in two-step fabrication. Here, to investigate these effects in real time, we designed and implemented a custom-built, temperature-controlled spin-coating system, enabling precise thermal modulation during precursor deposition. Using this platform, we performed in situ GIWAXS measurements to study the crystallization dynamics of FA 0.5 MA 0.5 PbI 3 films over a temperature range of 30°C–90°C. Our results reveal a non-monotonic relationship between spin-coating temperature and α-phase formation, governed by the interplay between precursor interdiffusion, PbI 2 crystallinity, and δ-phase suppression. The custom thermal control enabled us to isolate and quantify these competing effects during the earliest stages of film formation, providing mechanistic insight into how spin-coating temperature governs both phase purity and kinetic pathways in two-step perovskite systems. Temperature-dependent SEM and photovoltaic device measurements further demonstrate that early-stage crystallization pathways directly translate into differences in morphology, charge-transport continuity, and device performance. These findings inform targeted strategies for optimizing deposition protocols to balance rapid nucleation, phase stability, and device performance.

Saadawy, Ahmed [King Fahd University of Petroleum ↗
DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Automating Bayesian inference and design to quantify acoustic particle levitation

Self-propulsion of micro- and nanoparticles powered by ultrasound provides an attractive strategy for the remote manipulation of colloidal matter using biocompatible energy inputs. Quantitative understanding of particle motion and its dependence on size, shape, and composition requires accurate characterization of the acoustic field, which depends sensitively on the experimental setup. Here, we show how automated experiments based on Bayesian inference and design can accurately and efficiently characterize the acoustic field within resonant chambers used to propel acoustic nanomotors. Repeated cycles of observation, inference, and design (OID) are guided by a physical model that describes the rate at which levitating particles approach the nodal plane. Using video microscopy, we observe the relaxation of tracer particles to this plane following the application of the acoustic field. We use sequential Monte Carlo methods to infer model parameters such as the amplitude and frequency of the resonant chamber while accounting for particle-level measurement noise and population-level heterogeneity in the field. Guided by simulated outcomes, we select the optimal design for the next experiment as to maximize the information gain in the relevant parameters. We show how this iterative process serves to discriminate between competing hypotheses and efficiently converges to accurate parameter estimates using only few automated experiments. We discuss the need for model criticism to ensure the validity of the guiding model throughout automated cycles of observation, inference, and design. Furthermore, this work demonstrates how Bayesian methods can learn the parameters of nonlinear, hierarchical models used to describe video microscopy data of active colloids.

42 ENGINEERING↗
DOE OSTI2025

Neutron Tagging From Neutrino Interactions in DUNE-ND 2x2 Prototype

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino oscillation experiment that aims to measure whether CP is violated in the leptonic sector (if violated, how much) and unambiguously determine the neutrino mass ordering. DUNE consists of near and far detectors that rely on liquid argon time projection chamber (LArTPC) technology to observe neutrino interactions. The near detector (ND) will be placed in Fermilab, near the neutrino source, while the far detector (FD) will be deployed in Sanford Lab, 1.5 km deep underground, which is 1300 km away from the source. LArTPCs provide excellent particle identification and calorimetry; however, detecting neutrons is challenging, as they do not leave direct ionization signals in LArTPCs. Neutrons can carry away up to 25% of the neutrino energy, introducing a significant uncertainty in DUNE measurements. The DUNE near detector (ND) features a novel modular LArTPC with pixelated charge readout, which enhances event recons truction. The modular design enables precise correlation between ionization signals and light signals in a high-rate environment, improving the identification of delayed energy depositions from neutrons in neutrino interactions. We introduce a neutron tagging technique using the 2x2 Demonstrator, a small-scale prototype of the DUNE ND LArTPC. The analysis utilizes Monte Carlo simulations and deep-learning techniques to identify neutron-induced energy depositions and reconstruct low-energy activity.

Kufatty, Georgette [Florida State U.]↗
NASA NTRS1990

Spacecraft observations of the interaction of active comets with the solar wind

Six spacecraft encountered two comets during 1985 and 1986, obtaining a wealth of data relative to the plasma processes at work in the interaction of an active comet with the solar wind. A review of what space plasma scientists have learned from these data and of their interpretations is presented. The interaction process begins millions of kilometers from the nucleus where the solar wind first picks up cometary ions that slow down the wind and causes the interplanetary magnetic field to pile up and drape around the comet's ionosphere. Thus the geometry of a comet's plasma tail is defined by this draped field. At Halley the Giotto spacecraft noted a well-defined boundary separating the mixture of solar wind and cometary plasmas and the interplanetary magnetic field from the free-field, almost pure cometary plasma in the inner coma. Additional unexpected features in the inner coma were a flux of fast 'granddaughter' ions and high densities of negative ions. Further details covering pickup ions and plasma waves, mass loading, bow shock, energetic particles, the cometosheath, the ionopause, the plasma tail, and the field-free region are provided.

Neugebauer, M.↗
NASA NTRS1991

Hubble Space Telescope high speed photometer orbital verification

The purpose of this report is to provide a summary of the results of the HSP (High Speed Photometer) Orbital Verification (OV) tests and to report conclusions and lessons learned from the initial operations of the HSP. The HSP OV plan covered the activities through fine (phase 3) alignment. This report covers all activities (OV, SV, and SAO) from launch to the completion of phase 3 alignment. Those activities in this period that are not OV tests are described to the extent that they relate to OV activities.

Richards, Evan E.↗
NASA NTRS1999

STS-99 CEIT at the OPF High Bay-2, Endeavour, SRTM Mission

Live footage shows the crewmembers of STS-99, Commander Kevin R. Kregel, Pilot Dominic L. Pudwill Gorie, Mission Specialists Janet L. Kavandi, Janice E. Voss, Mamoru Mohri, and Gerhard P.J. Thiele, participating in Crew Equipment Interface Test (CEIT) activities at the Orbiter Processing Facility (OPF). The crew is shown checking out and learning about the equipment.

Source record↗
NASA NTRS2008

NASA's Evolutionary Xenon Thruster: The NEXT Ion Propulsion System for Solar System Exploration

This viewgraph presentation reviews NASA s Evolutionary Xenon Thruster (NEXT) Ion Propulsion system. The NEXT project is developing a solar electric ion propulsion system. The NEXT project is advancing the capability of ion propulsion to meet NASA robotic science mission needs. The NEXT system is planned to significantly improve performance over the state of the art electric propulsion systems, such as NASA Solar Electric Propulsion Technology Application Readiness (NSTAR). The status of NEXT development is reviewed, including information on the NEXT Thruster, the power processing unit, the propellant management system (PMS), the digital control interface unit, and the gimbal. Block diagrams NEXT system are presented. Also a review of the lessons learned from the Dawn and NSTAR systems is provided. In summary the NEXT project activities through 2007 have brought next-generation ion propulsion technology to a sufficient maturity level.

Pencil, Eric J.↗
NASA NTRS2012

Magnetic Testing, and Modeling, Simulation and Analysis for Space Applications

The Aerospace Corporation (Aerospace) and Lockheed Martin Space Systems (LMSS) participated with Jet Propulsion Laboratory (JPL) in the implementation of a magnetic cleanliness program of the NASA/JPL JUNO mission. The magnetic cleanliness program was applied from early flight system development up through system level environmental testing. The JUNO magnetic cleanliness program required setting-up a specialized magnetic test facility at Lockheed Martin Space Systems for testing the flight system and a testing program with facility for testing system parts and subsystems at JPL. The magnetic modeling, simulation and analysis capability was set up and performed by Aerospace to provide qualitative and quantitative magnetic assessments of the magnetic parts, components, and subsystems prior to or in lieu of magnetic tests. Because of the sensitive nature of the fields and particles scientific measurements being conducted by the JUNO space mission to Jupiter, the imposition of stringent magnetic control specifications required a magnetic control program to ensure that the spacecraft's science magnetometers and plasma wave search coil were not magnetically contaminated by flight system magnetic interferences. With Aerospace's magnetic modeling, simulation and analysis and JPL's system modeling and testing approach, and LMSS's test support, the project achieved a cost effective approach to achieving a magnetically clean spacecraft. This paper presents lessons learned from the JUNO magnetic testing approach and Aerospace's modeling, simulation and analysis activities used to solve problems such as remnant magnetization, performance of hard and soft magnetic materials within the targeted space system in applied external magnetic fields.

magnetics↗
NASA NTRS2017

NASA's Hybrid Reality Lab: One Giant Leap for Full Dive

This presentation demonstrates how NASA is using consumer VR headsets, game engine technology and NVIDIA's GPUs to create highly immersive future training systems augmented with extremely realistic haptic feedback, sound, additional sensory information, and how these can be used to improve the engineering workflow. Include in this presentation is an environment simulation of the ISS, where users can interact with virtual objects, handrails, and tracked physical objects while inside VR, integration of consumer VR headsets with the Active Response Gravity Offload System, and a space habitat architectural evaluation tool. Attendees will learn how the best elements of real and virtual worlds can be combined into a hybrid reality environment with tangible engineering and scientific applications.

Delgado, Francisco J.↗
NASA NTRS2018

Commissioning of NASA's 3rd Generation Tracking and Data Relay Satellites (TDRS KLM)

In the summer of 2017, the third and final spacecraft of the 3rd generation of the Tracking and Data Relay Satellites (TDRS) launched aboard an Atlas V rocket from Complex 41 on the Eastern Test Range. Finishing final testing and integration in the first quarter of 2018, the TDRS-M communication and navigation satellite completes a constellation that began service in the early 1980s. The 3rd generation of spacecraft, TDRS-K, L, and M, not only provided beneficial systems engineering lessons in handling anomalous Radio Frequency and Doppler interference as well as integrating new spacecraft into an aging ground support infrastructure, but also supplies NASA with a valuable test bed for new operational concepts and technologies useful in defining the future architecture of the NASA Space Network. This paper presents an overview of the TDRS-K, L, and M missions, including transfer orbit, Level 5 bus and payload testing, and finally NASA-led Level 6 testing, which includes active TDRS System (TDRSS) users. Highlights include relevant testing results, commissioning challenges, and lessons learned. The final discussion includes a brief overview of future NASA communication and navigation technologies and network architectures.

Data Relay↗
NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗
NASA NTRS2023

Automated Global-Scale Detection and Characterization of Anthropogenic Activity using Multi-Source Satellite-Based Remote Sensing Imagery

Satellite-based remote sensing imagery is an effective means for detecting objects and structures in support of many applications. However, detecting the spatial and temporal bounds of a specific activity in satellite imagery is inherently more complex and research in this area is nascent. One reason for this is that describing an activity implies defining both spatial and temporal bounds and while activity is inherently continuous in nature, the geospatial (imagery) time series for any particular swath of ground provided by satellite imagery is relatively sparse and discrete in comparison. The IARPA Space-Based Machine Automated Recognition Technique (SMART)1 program is the first large-scale research program to target advancing the state of the art for automatically detecting, characterizing, and monitoring large-scale anthropogenic activity in global, multispectral satellite imagery. The program has two primary research objectives: 1) the “harmonization” of multiple imagery sources and 2) automated reasoning at scale to detect, characterize, and monitor activities of interest. This paper provides details on the goals, dataset, metrics, and lessons learned of the IARPA SMART program. By releasing the annotated dataset, the program aims to foster additional research in this area by the community at large.

Hirsh R Goldberg↗
NASA NTRS1994

Suited for spacewalking: Teacher's guide with activities for physical and life science

Space walking has captured the imagination of generations of children and adults since science-fiction authors first placed their characters on the Moon. This publication is an activity guide for teachers interested in using the intense interest many children have in space exploration as a launching point for exciting hands-on learning opportunities. The guide begins with brief discussions of the space environment, the history of space walking, the Space Shuttle spacesuit, and working in space. These are followed by a series of activities that enable children to explore the space environment as well as the science and technology behind the functions of spacesuits. The activities are not rated for specific grade levels because they can be adapted for students of many ages. The chart on curriculum application at the back of the book is designed to help teachers incorporate activities into various subject areas.

Vogt, Gregory L.↗
DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗