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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
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

20 Meter Solar Sail Analysis and Correlation

This paper describes finite element analyses and correlation studies to predict deformations and vibration modes/frequencies of a 20-meter solar sail system developed by ATK Space Systems. Under the programmatic leadership of NASA Marshall Space Flight Center's In-Space Propulsion activity, the 20-meter solar sail program objectives were to verify the design, to assess structural responses of the sail system, to implement lessons learned from a previous 10-meter quadrant system analysis and test program, and to mature solar sail technology to a technology readiness level (TRL) of 5. For this 20 meter sail system, static and ground vibration tests were conducted in NASA Glenn Research Center's 100 meter diameter vacuum chamber at Plum Brook station. Prior to testing, a preliminary analysis was performed to evaluate test conditions and to determine sensor and actuator locations. After testing was completed, an analysis of each test configuration was performed. Post-test model refinements included updated properties to account for the mass of sensors, wiring, and other components used for testing. This paper describes the development of finite element models (FEM) for sail membranes and masts in each of four quadrants at both the component and system levels, as well as an optimization procedure for the static test/analyses correlation.

Taleghani, B. K.↗
DOE OSTI2021

The Simons Observatory: HoloSim-ML: machine learning applied to the efficient analysis of radio holography measurements of complex optical systems

Near-field radio holography is a common method for measuring and aligning mirror surfaces for millimeter and sub-millimeter telescopes. In instruments with more than a single mirror, degeneracies arise in the holography measurement, requiring multiple measurements and new fitting methods. We present HoloSim-ML, a Python code for beam simulation and analysis of radio holography data from complex optical systems. This code uses machine learning to efficiently determine the position of hundreds of mirror adjusters on multiple mirrors with few micron accuracy. We apply this approach to the example of the Simons Observatory 6m telescope.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1990

Sparse distributed memory overview

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

Raugh, Mike↗
NASA NTRS2010

Orion Flight Performance Design Trades

A significant portion of the Orion pre-PDR design effort has focused on balancing mass with performance. High level performance metrics include abort success rates, lunar surface coverage, landing accuracy and touchdown loads. These metrics may be converted to parameters that affect mass, such as ballast for stabilizing the abort vehicle, propellant to achieve increased lunar coverage or extended missions, or ballast to increase the lift-to-drag ratio to improve entry and landing performance. The Orion Flight Dynamics team was tasked to perform analyses to evaluate many of these trades. These analyses not only provide insight into the physics of each particular trade but, in aggregate, they illustrate the processes used by Orion to balance performance and mass margins, and thereby make design decisions. Lessons learned can be gleaned from a review of these studies which will be useful to other spacecraft system designers. These lessons fall into several categories, including: appropriate application of Monte Carlo analysis in design trades, managing margin in a highly mass-constrained environment, and the use of requirements to balance margin between subsystems and components. This paper provides a review of some of the trades and analyses conducted by the Flight Dynamics team, as well as systems engineering lessons learned.

Jackson, Mark C.↗
DOE OSTI2020

Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint

Frequency control from Photovoltaic (PV) plants has great potential to address the frequency response challenge of the power system with high renewable penetration. However, using model-based approaches to determine the optimal PV headroom reserve requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic one-day operation profile of the WECC system and demonstrates over 40% PV headroom saving compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Validation of Maui PSCAD Model: Motivation, Methodology, and Lessons Learned: Preprint

As power systems become increasingly dependent on inverter-based resources, the dominant physical and control interactions that determine system stability may no longer be accurately captured in positive sequence dynamic simulation tools; electromagnetic transient (EMT) simulations may be needed. This paper describes the process of creating and validating an EMT model of the entire Maui island transmission system in PSCAD software. The model is validated against the utility's PSSE model and against field data for an event that consisted of a single-phase fault followed by a generation trip. The methodology and lessons learned are summarized here.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada

A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a probability map. During the first year of this project we have encountered several issues particular to using geological and geophysical data sets with these tools. Through an illustrative example we develop a promising workflow for future use as more data become available and are analyzed.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.

14 SOLAR ENERGY↗
DOE OSTI2021

Planning for Resilient Power Distribution Systems using Risk-Based Quantification and Q-Learning

Grid hardening is one of the most effective approaches that reduce the component failures and restoration efforts thus increasing the resilience of the power systems against extreme events. However, hardening and upgrading the entire system is prohibitively expensive and hence the optimal design of a distribution network is challenging. This paper adopted a reinforcement learning algorithm to identify the optimal hardening strategy to enhance the resilience of power distribution systems. Adopting the Q-learning algorithm as the reinforcement learning technique, we found the sequential optimal action for hardening measures to enhance the grid's resilience for the given budget. To identify the optimal strategy through Q-learning, Conditional Value at Risk (CVaR) is used as a rewarding metric. A study on the IEEE 123-bus test feeder validate the effectiveness of the proposed model and show how to effectively allocate budget limited resources to plan a resilient power distribution network.

Paul, Shuva↗
DOE OSTI2021

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Coronagraphic Imaging of Debris Disks from a High Altitude Balloon Platform

Debris disks around nearby stars are tracers of the planet formation process, and they are a key element of our understanding of the formation and evolution of extrasolar planetary systems. With multi-color images of a significant number of disks, we can probe important questions: can we learn about planetary system evolution; what materials are the disks made of; and can they reveal the presence of planets? Most disks are known to exist only through their infrared flux excesses as measured by the Spitzer Space Telescope, and through images measaured by Herschel. The brightest, most extended disks have been imaged with HST, and a few, such as Fomalhaut, can be observed using ground-based telescopes. But the number of good images is still very small, and there are none of disks with densities as low as the disk associated with the asteroid belt and Edgeworth-Kuiper belt in our own Solar System. Direct imaging of disks is major observational challenge, demanding high angular resolution and extremely high dynamic range close to the parent star. The ultimate experiment requires a space-based platform, but demonstrating much of the needed technology, mitigating the technical risks of a space-based coronagraph, and performing valuable measurements of circumstellar debris disks, can be done from a high-altitude balloon platform. In this paper we present a balloon-borne telescope concept based on the Zodiac II design that could undertake compelling studies of a sample of debris disks.

Unwin, Stephen↗
NASA NTRS2012

Coronagraphic Imaging of Debris Disks from a High Altitude Balloon Platform

Debris disks around nearby stars are tracers of the planet formation process, and they are a key element of our understanding of the formation and evolution of extrasolar planetary systems. With multi-color images of a significant number of disks, we can probe important questions: can we learn about planetary system evolution; what materials are the disks made of; and can they reveal the presence of planets? Most disks are known to exist only through their infrared flux excesses as measured by the Spitzer Space Telescope, and through images measured by Herschel. The brightest, most extended disks have been imaged with HST, and a few, such as Fomalhaut, can be observed using ground-based telescopes. But the number of good images is still very small, and there are none of disks with densities as low as the disk associated with the asteroid belt and Edgeworth-Kuiper belt in our own Solar System. Direct imaging of disks is a major observational challenge, demanding high angular resolution and extremely high dynamic range close to the parent star. The ultimate experiment requires a space-based platform, but demonstrating much of the needed technology, mitigating the technical risks of a space-based coronagrap, and performing valuable measurements of circumstellar debris disks, can be done from a high-altitude balloon platform. In this paper we present a balloon-borne telescope experiment based on the Zodiac II design that would undertake compelling studies of a sample of debris disks.

debris disk↗
DOE OSTI2023

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems

This article develops a deep graph operator network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g., the power grid or traffic) with an underlying subgraph structure. Here, we build our DeepGraphONet by fusing the ability of graph neural networks to exploit spatially correlated graph information and deep operator networks to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different subgraph. Finally, empirical results on the transient stability prediction problem of power grids and traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF

In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP – part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP’s spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part’s coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Finally, our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Short Term Plasticity for Artificial Neural Networks

Achieving efficient learning for AI systems was identified as a major challenge in the DOE's recently released, AI for Science , report. The human brain is capable of efficient and low-powered learning. It is likely that implementing brain-like principles will lead to more efficient AI systems. In this LDRD, I aim to contribute to this goal by creating a foundation for implementing and studying a brain phenomenon termed short term plasticity (STP) in spiking artificial neural networks within Sandia. First, data collected by the Allen Institute for Brain Science (AIBS) was analyzed to see if STP could be classified into types using the data collected. Although the data was inadequate at the time, AIBS has updated their database and created models that could be utilized in the future. Second, I began creating a software package to assess the ability of a Boltzmann machine utilizing STP to sample from national security data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1986

KBGIS-II: A knowledge-based geographic information system

The architecture and working of a recently implemented Knowledge-Based Geographic Information System (KBGIS-II), designed to satisfy several general criteria for the GIS, is described. The system has four major functions including query-answering, learning and editing. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial object language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multilayered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is performing all its designated tasks successfully. Future reports will relate performance characteristics of the system.

Smith, Terence↗