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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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NASA NTRS2020

Mars 2020 Maxon Commercial Motor Development from COTS to Flight Qualified Motors, Gearbox, and Detent Brake: Issues Overcome and Lessons Learned

Building on previous collaborations, maxon and the Jet Propulsion Laboratory (JPL) established a partnership to modify Ø20mm & Ø32mm Commerical-Off-The-Shelf (COTS) BrushLess Direct Current (BLDC) flat motors and a Ø22mm planetary gearbox.The commercial design was modified to meet the requirements for the Mars2020 rover, a Class B [1], Martian surface environment, interplanetary mission, whilst maintain as much of the industrial heritage as possible. Despite numerous problems encountered during the development, these were all overcome and qualification of the design and acceptance testing for the 10 Flight Model (FM) actuators that were installed on the Mars2020 rover was completed on-time for the rover assembly schedule.

Loschiavo, Michael↗

DOE OSTI2020

Implementing Mixed Reality Tools to Support Mission Delivery at Hanford - 20520

Mission Support Alliance's (MSA) Public Works and Information Systems organizations have been working with Microsoft Corporation to develop and implement mixed reality operational solutions using their HoloLens technology. This collaborative effort is intended to drive innovative solutions and significantly improve MSA's efficiency in performing work at the Hanford Site. HoloLens, a mixed reality tool, is a commercial off-the-shelf technology that combines a head-mounted viewing screen with applications to help people and organizations learn, communicate, and collaborate more effectively through the use of mixed reality. https://microsoft.com/en-us/hololens MSA's initial pilot successfully demonstrated its ability to use mixed reality utilizing HoloLens' advanced features in a variety of ways, illustrating the potential efficiency gains previously stated as goals of this pilot. These achievements include the development of proprietary technology that allows synchronization between a HoloLens device and a mobile device or alternate global positioning system (GPS) device. This synchronization enables the HoloLens device to access real time GPS location data anywhere on the Hanford Site. This real time location data, combined with MSA's improved Hanford Geographical Information System (GIS) data, enables the mixed reality pilot application to identify underground utility systems as well as related utility attributes. This ability will aid utility workers in several areas, including excavation activities, the placement of large cranes, identifying interactions and conflicts with future underground utility placements and, eventually, recognizing and de-conflicting tank waste transfer system valve alignments. The HoloLens mixed reality application is also able to provide full or small scale holographic images of a facility's digital twin. This feature will allow MSA's Water and Sewer Utilities team to train and validate procedures associated with the new water treatment facility virtually, before it is constructed. This reduces the amount of time it takes to complete the training and procedure validation, and accelerates the overall construction schedule. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2013

International R&M/Safety Cooperation Lessons Learned Between NASA and JAXA

Presented are a number of important experiences gained and lessons learned from the collaboration of the National Aeronautics and Space Administration (NASA) and the Japanese Aerospace Exploration Agency (JAXA) on the CoNNeCT (Communications, Navigation, and Networking re-Configurable Testbed) project. Both space agencies worked on the CoNNeCT Project to design, assemble, test, integrate, and launch a communications testbed facility mounted onto the International Space Station (ISS) truss. At the 2012 RAMS, two papers about CoNNeCT were presented: one on Ground Support Equipment Reliability & System Safety, and the other one on combined application of System Safety & Reliability for the flight system. In addition to the logistics challenges present when two organizations are on the opposite side of the world, there is also a language barrier. The language barrier encompasses not only the different alphabet, it encompasses the social interactions; these were addressed by techniques presented in the paper. The differences in interpretation and application of Spaceflight Requirements will be discussed in this paper. Although many, but definitely not all, of JAXA's Spaceflight Requirements were inspired by NASA, there were significant and critically important differences in how they were interpreted and applied. This paper intends to summarize which practices worked and which did not for an international collaborative effort so that future missions may benefit from our experiences. The CoNNeCT flight system has been successfully assembled, integrated, tested, shipped, launched and installed on the ISS without incident. This demonstrates that the steps taken to facilitate international understanding, communication, and coordination were successful and warrant discussion as lessons learned.

Fernandez, Rene↗

NASA NTRS2002

Virtual Collaborative Environments for System of Systems Engineering and Applications for ISAT

This paper describes an system of systems or metasystems approach and models developed to help prepare engineering organizations for distributed engineering environments. These changes in engineering enterprises include competition in increasingly global environments; new partnering opportunities caused by advances in information and communication technologies, and virtual collaboration issues associated with dispersed teams. To help address challenges and needs in this environment, a framework is proposed that can be customized and adapted for NASA to assist in improved engineering activities conducted in distributed, enhanced engineering environments. The approach is designed to prepare engineers for such distributed collaborative environments by learning and applying e-engineering methods and tools to a real-world engineering development scenario. The approach consists of two phases: an e-engineering basics phase and e-engineering application phase. The e-engineering basics phase addresses skills required for e-engineering. The e-engineering application phase applies these skills in a distributed collaborative environment to system development projects.

Dryer, David A.↗

DOE OSTI2026

DP-TwoLevel: two-stage gradient subspace learning for differentially private federated learning

Federated learning (FL) enables collaborative model training across distributed data sources without sharing raw data, but faces fundamental challenges in communication efficiency and privacy. Differentially private (DP) training mitigates information leakage but introduces noise that degrades model performance, especially in high-dimensional settings. We propose DP-TwoLevel, a hierarchical gradient projection method that improves utility under fixed DP constraints by exploiting low-dimensional structure in model updates. Our approach learns a two-level PCA-based representation of gradients and applies DP noise in a reduced-dimensional subspace, thereby lowering the effective noise magnitude while preserving dominant signal components. We evaluate the method across three datasets (MNIST, Fashion-MNIST, CIFAR-10) and three privacy regimes (ϵ∈0.5, 1.0, 2.0). Across nine experimental settings, DP-TwoLevel consistently outperforms DP-FedAvg, achieving an average accuracy improvement of 9.44%, with larger gains observed in lower ϵ(higher-noise) regimes (up to +22.31%). We further analyze scalability across models ranging from 100K to 1.49M parameters and identify a variance-based success criterion: performance remains strong when the projection preserves more than 75% of gradient variance, degrades in a marginal regime (65–75%), and fails below this threshold. Our results demonstrate that structure-aware dimensionality reduction can significantly improve the privacy–utility tradeoff in FL without modifying formal privacy guarantees. We also provide empirical evidence of scaling limitations for global projections and motivate per-layer extensions for larger models.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗

NASA NTRS2005

The APPL "Learning Map"

Root Learning, a learning consulting organization with a background in strategic planning, recognizes the knowledge gap that frequently exists between a leadership team and the rest of an organization. Team members supposedly working toward the same goal don't always have the same vision as to where the organization is headed, and they may not understand how the piece they are accountable for fits into the big picture. To address these complex problems, Root Learning utilizes the age-old tools of sarcasm, metaphor and graphics (much in the same way that ASK uses a traditional storytelling format.) The company is best known for creating "Learning Maps" like this one: humorous drawings based on the inner workings of an organization. Their purpose is to put complex topics on the table, to stimulate discussion, and to ultimately give team members a common vision of where the organization is going and what role they personally play in getting there. APPL knows how effective it is to incorporate new and engaging techniques into its knowledge sharing programs. By collaborating with Root Learning, we were able to expand the knowledge of the organization and add one more of these techniques to our repertoire.

Source record↗

DOE OSTI2017

PV Systems Reliability and Outreach

The project purpose was to collaborate with the Electric Power Research Institute (EPRI) to reach out to the photovoltaic (PV) industry to cooperate, collaborate and facilitate establishment and access to PV plant data for use in the Photovoltaic Reliability Operations and Maintenance (PVROM) database. Sandia National Laboratories (SNL) and EPRI conducted this outreach to expand and accelerate the implementation of PVROM by gathering more data for analyzing trends, with a goal of accelerating the adoption of PV systems as a primary power generation source in the United States. SNL and EPRI analyzed the data from the PVROM database to assess industry trends in operations and maintenance (O&M) and develop guidelines for the industry. More specifically, this project was intended to leverage SNL’s expertise in PV plant reliability data collection and analysis, with data sharing partners that own or operate PV systems who wanted to learn about rigorous reliabilitybased data collection to track issues over time and improve O&M activities. The partnership also worked to host multiple workshops as part of the SNL/EPRI PV Systems Symposium to gather industry feedback on O&M process improvement and data standards, and share insights learned from the collaboration between SNL and EPRI in the area of PV system reliability. Additional outreach included multiple ‘expert elicitation’ interviews led by EPRI to gather best practices and paths forward for improving/standardizing PV plant O&M and asset management practices. Technical accomplishments included further development of the PVROM database process, sharing best practices for reliability data collection and improving industry understanding of existing O&M practices, important knowledge, and new and novel techniques for benefitting EPRI utility partners and the broader PV industry. Improved reliability at a PV power plant leads to a lower levelized cost of energy (LCOE), which is reflected most directly in its operating expenses. Electric utilities that utilize PV as a larger share of plants they own and operate are interested in having a high degree of availability or ‘uptime’, similar to other power generation sources in their service area. Having low cost, reliable PV generation can lead to fewer costs that need to be recovered by the utility over the lifetime of the power plant, and less regulatory uncertainty when considering bulk transmission and distribution system reliability. The public and taxpayers benefit by the electric utility passing through lower electricity costs.

14 SOLAR ENERGY↗

DOE OSTI2025

Dynamical Sketching for Enhanced Communication Efficiency in Federated Learning

Federated learning (FL) has revolutionized distributed machine learning by enabling collaborative model training without sharing local data. However, communication efficiency and privacy guarantees remain significant challenges. This paper introduces a dynamic sketching mechanism in FL, optimizing the trade-off between communication efficiency and model accuracy. By dynamically selecting the sketch matrix size, our approach adapts to the evolving characteristics of the data and the model, ensuring optimal performance across diverse scenarios. We leverage Bayesian optimization to systematically tune the sketch parameters, achieving an effective balance between resource efficiency and model performance. Experimental results on the MNIST dataset using a convolutional neural network (CNN) architecture validate the proposed method's efficiency and scalability. Our dynamic sketching approach significantly outperforms fixed-size sketching techniques, achieving higher compression ratios (up to 62x) and providing better privacy guarantees while maintaining high model accuracy. These findings highlight the robustness and versatility of our approach and make it a valuable solution for privacy-preserving, communication-efficient federated learning.

Afrose, Sharmin [ORNL]↗

NASA NTRS2005

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented.

Goetz, Scott J.↗

DOE OSTI2025

Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx

Facilitating large-scale, cross-institutional collaboration in biomedical machine learning (ML) projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. Specifically designed for this purpose, this work introduces APPFLx - a low-code, easy-to-use FL framework that enables easy setup, configuration, and running of FL experiments. APPFLx removes administrative boundaries of research organizations and healthcare systems while providing secure end-to-end communication, privacy-preserving functionality, and identity management. Furthermore, it is completely agnostic to the underlying computational infrastructure of participating clients, allowing an instantaneous deployment of this framework into existing computing infrastructures. Experimentally, the utility of APPFLx is demonstrated in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. Here, ML models were securely trained across heterogeneous computing resources, including a combination of on-premise high-performance computing and cloud computing facilities. By securely unlocking data from multiple sources for training without directly sharing it, these FL models enhance generalizability and performance compared to centralized training models while ensuring data remains protected. In conclusion, APPFLx demonstrated itself as an easy-to-use framework for accelerating biomedical studies across organizations and healthcare systems on large datasets while maintaining the protection of private medical data.

Biomedical Research↗

DOE OSTI2017

Multi-actor involvement for integrating ecosystem services in strategic environmental assessment of spatial plans

Integrating an ecosystem services (ES) approach into Strategic Environmental Assessment (SEA) of spatial plans potentially enhances the consideration of the value of nature in decision making and policy processes. However, there is increasing concern about the institutional context and a lack of a common understanding of SEA and ecosystem services for adopting them as an integrated framework. This paper addresses this concern by analysing the current understanding and network relations in a multi-actor arrangement as a first step towards a successful integration of ES in SEA and spatial planning. Our analysis focuses on a case study in Chile, where we administered a questionnaire survey to some of the main actors involved in the spatial planning process. The questionnaire focused on issues such as network relations among actors and on conceptual understanding, perceptions and challenges for integrating ES in SEA and spatial planning, knowledge on methodological approaches, and the connections and gaps in the science-policy interface. Our findings suggest that a common understanding of SEA and especially of ES in a context of multiple actors is still at an initial stage in Chile. Additionally, the lack of institutional guidelines and methodological support is considered the main challenge for integration. We conclude that preconditions exist in Chile for integrating ES in SEA for spatial planning, but they strongly depend on appropriate governance schemes that promote a close science-policy interaction, as well as collaborative work and learning. - Highlights: • Linking ecosystem services in SEA is an effective framework for sustainability. • Multi-actor understanding and networks in ecosystem services and SEA were analyzed. • Understanding of SEA and especially of ES is still in an initial stage in Chile. • A lack of institutional guidelines is one of the key challenges for this link.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.

Bayesian inference↗

DOE OSTI2025

Position-Enhanced Gradient Attack (PEGA) on Medical Language Models

Federated Learning (FL) enables collaborative training of language models on sensitive clinical notes without sharing the data. However, this paradigm is vulnerable to gradient inversion attacks that can reconstruct private data from shared gradients. We find that state-of-the-art attacks are less effective in the medical domain, failing to overcome the unique challenges posed by its specialized vocabulary and unstructured format. To address this, we introduce the Position-Enhanced Gradient Attack (PEGA), a novel attack that makes gradients position-aware by optimizing token and position embeddings simultaneously. PEGA employs two key innovations: a periodic sorting of positional embeddings to resolve token order ambiguity and a late-stage embedding replacement strategy to correct hard-to-recover critical tokens. To evaluate the leakage of sensitive data more directly, we also propose the Unified PHI-Recall (UPHI), a new metric measuring the recovery of Protected Health Information. Experiments on the MIMIC-III dataset show that PEGA significantly outperforms leading attacks like TAG and LAMP, particularly in its ability to reconstruct identifiable patient information, exposing a more severe and nuanced privacy risk in federated medical NLP.

Xu, Nuo [University of Minnesota]↗

DOE OSTI2019

The Gulf Nuclear Energy Infrastructure Institute (GNEII): Origins, Objectives, and Operations—A Joint Report

The Gulf Nuclear Energy Infrastructure Institute (GNEII—pronounced "genie") seeks to develop expertise among future leaders of Gulf-region nuclear power programs in global standards, norms and best practices in nuclear energy programs. More specifically, the institute aims to contribute to the enhancement of nuclear security, safety, and safeguards (the so-called nuclear "3S") by providing an avenue for regional nuclear interaction, technical collaboration, lessons-learned discussions, and best-practices sharing. It is a multidisciplinary human capacity development institute offering education, research and technical services to support responsible nuclear energy programs in the Gulf and Middle East regions. In this Joint Report, Chapter 2 discusses GNEII's origins (including drivers, milestones, and design principles), Chapter 3 discusses GNEII's objectives (including goals, mission, and vision), Chapter 4 discusses GNEII's operations (including education, research, and technical service pillars), Chapter 5 discusses major insights and next steps, and Chapter 6 provides a list of publications offering additional depictions and details of GNEII's evolution. Though only one piece of a multi-faceted, multi-national effort to develop human infrastructure needs for nascent nuclear energy programs, GNEII offers a model that addresses the socio-technical attributes of nuclear 3S that can be replicated globally.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Data Archive and Portal (DAP) Platform for Solid Phase Processing Technologies

The scale and speed of data generated by modern scientific experiments have constantly challenged the research community to store, curate, manage and optimally use it to drive scientific discoveries. In this work, we have developed a data archive and portal (DAP) platform including analytics capabilities to collect, curate, and manage data and metadata stream for solid phase processing (SPP) techniques. We successfully hosted around ~347K files of data related to processing parameters, microscopic images, and spectroscopic data related to solid phase processing. The DAP platform for SPP will establish an enduring capability to support machine learning and grow collaboration at the intersection of materials science and data science.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument

Abstract The Dark Energy Spectroscopic Instrument (DESI) embarked on an ambitious 5 yr survey in 2021 May to explore the nature of dark energy with spectroscopic measurements of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the baryon acoustic oscillation method to measure distances from the nearby universe to beyond redshift z > 3.5, and employ redshift space distortions to measure the growth of structure and probe potential modifications to general relativity. We describe the significant instrumentation we developed to conduct the DESI survey. This includes: a wide-field, 3.°2 diameter prime-focus corrector; a focal plane system with 5020 fiber positioners on the 0.812 m diameter, aspheric focal surface; 10 continuous, high-efficiency fiber cable bundles that connect the focal plane to the spectrographs; and 10 identical spectrographs. Each spectrograph employs a pair of dichroics to split the light into three channels that together record the light from 360–980 nm with a spectral resolution that ranges from 2000–5000. We describe the science requirements, their connection to the technical requirements, the management of the project, and interfaces between subsystems. DESI was installed at the 4 m Mayall Telescope at Kitt Peak National Observatory and has achieved all of its performance goals. Some performance highlights include an rms positioner accuracy of better than 0.″1 and a median signal-to-noise ratio of 7 of the [O ii ] doublet at 8 × 10 −17 erg s −1 cm −2 in 1000 s for galaxies at z = 1.4–1.6. We conclude with additional highlights from the on-sky validation and commissioning, key successes, and lessons learned.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2020

Vision for Cross-Center MSBE Collaboration on the Gateway Program

Model-Based Systems Engineering (MBSE) can be a challenge when there is only one modeler and one model involved. For the Gateway Program, due to its unique acquisition approach, the modeling efforts involve multiple NASA centers with each developing their own models. Every additional model to be integrated compounds the difficulties, necessitating stronger ontologies and explicitly defined interfaces between models. To help facilitate this integration, a vision of collaboration between centers is in its beginning stages. This vision includes looking at models as systems themselves and developing their own use cases, requirements and interfaces between each of them. The goal of this presentation is to share the Gateway Program's cross-center vision for model collaboration, the lessons learned in developing and implementing that vision for the various system engineering products needed to satisfy life cycle review criteria and how treating models as systems helped in these efforts.

Crane, Jeremiah↗

DOE OSTI2024

Offshore Wind to Hydrogen - Modeling, Analysis, Testing and International Collaboration Work

This project explores electrolytic hydrogen production from an offshore wind turbine by: Modeling: Simulate an offshore wind turbine and generate power output profiles; Analysis: Analyze offshore conditions and determine operational modes of the stack; Testing: Perform hardware testing to evaluate dynamic characteristics; and International Collaboration Work: Share learnings and publish. Potential Outcomes: Accelerate development of an integrated, in-turbine offshore wind hydrogen system; and Support two DOE Energy Earthshots - Hydrogen and Floating Offshore Wind.

electrolyzer↗