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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Finite Element Modeling of Extravehicular Mobility Units for Use With Human Body Models – Motivation, Major Challenges, Use Cases and Preliminary Work

Finite element modeling of pressurized spacesuits and implementation with human body models offers many advantages over physical experimentation, but also presents significant challenges. A model of pressurized spacesuit softgoods was developed, integrated with an existing hardgoods model, and fitted to a human body model. Two representative loading scenarios were simulated: dynamic external suit loading and internal occupant-driven loading, to serve as proof-of-concept for the modeling techniques employed. The modeled interactions behaved as intended and illustrate that finite element models of pressurized spacesuits can be used in conjunction with human body models to assess the biomechanical behavior. Further model development and experimental validation are needed.

Finite Element↗
NASA NTRS2024

Finite Element Modeling of Extravehicular Mobility Units for Use With Human Body Models – Motivation, Major Challenges, Use Cases and Preliminary Work

Finite element modeling of pressurized spacesuits and implementation with human body models offers many advantages over physical experimentation, but also presents significant challenges. A model of pressurized spacesuit softgoods was developed, integrated with an existing hardgoods model, and fitted to a human body model. Two representative loading scenarios were simulated: dynamic external suit loading and internal occupant-driven loading, to serve as proof-of-concept for the modeling techniques employed. The modeled interactions behaved as intended and illustrate that finite element models of pressurized spacesuits can be used in conjunction with human body models to assess the biomechanical behavior. Further model development and experimental validation are needed.

Finite Element↗
DOE OSTI2022

Pumped Storage Hydropower Augmented with Pressurized Air: The Ground-Level Integrated Diverse Energy Storage (GLIDES) System — GLIDES System Configurations and Use Cases

Energy storage is essential for cost-effective integration of variable renewable energy sources to support a low-carbon grid. It is also a key enabler of a modern grid infrastructure for demand management. However, several main challenges remain for different kind of energy storage technologies in grid scale deployment. Currently, the largest source of utility-scale storage and long-duration storage in the US is pumped storage hydropower (PSH). Prospect of growth in conventional PSH faces challenges that have limited its deployment over the last three decades, including high capital costs and long deployment timelines. Batteries have high energy densities and are the primary technology of choice for small-scale energy storage. Compressed air energy storage (CAES) is another large-scale energy storage technology, but there are few plants deployed worldwide. They suffer from their low round trip efficiency (RTE) due to the use of high-pressure air compressors. To address some of the challenges associated with these various storage technologies, the Ground-Level Integrated Diverse Energy Storage (GLIDES) is a modular PSH technology that was invented in 2015 at Oak Ridge National Laboratory. It utilizes gas compression to store electric energy. GLIDES stores energy by compressing gas using a liquid piston in high-pressure vessels. In doing so the vessels act as the upper reservoir in conventional PSH. Initially, the vessels are filled with gas to a prescribed pressure. To store energy, GLIDES uses a hydraulic piston pump to pump water into the pressurized vessels. As the water volume increases inside the vessels, water acts as a hydraulic piston compressing the gas on top of it. This process can be thought of as pumping water from the lower reservoir to the higher reservoir in PSH, increasing the water head. To dispatch the stored energy, the high-head water in the vessel is discharge through a high head Pelton hydraulic turbine that is connected to an electric generator. Employing high-pressure vessels enables GLIDES to reach water heads ~10-80 times higher than conventional PSH, achieving ~40 times higher energy densities, and overcomes the geographic limitation of conventional PSH. Although its energy density is much lower than that of batteries, GLIDES holds the potential advantages of having long service life, ease of system integration and being less hazardous over batteries. GLIDES prospective scalability could make it suitable for wide range of applications from behind the meter storage in buildings to grid-scale storage. It also makes it suitable for installations in densely populated urban areas where energy storage is most needed and real estate is limited. Over the last 5 years, work has focused on increasing GLIDES’ energy density, decreasing its initial capital cost of the system, and increasing its revenue potential. Several designs were developed and prototyped to verify and demonstrate the improvement in energy density. The latest prototype achieved energy density of 1.21 kWh/m 3 . Our analysis showed that it could achieve up to 1.7 kWh/m 3 with a mixture of air and carbon dioxide as the gas being compressed.

13 HYDRO ENERGY↗
NASA NTRS2023

Exploration Medical Capability Clinical Decision Support Use Cases for CDSS Test Bed

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data, and crew time), limited options for evacuation, and those associated with delayed or constrained communications. Each of these challenges necessitates greater degrees of crew autonomy as our distance from Earth increases. Specifically, as communication delays intensify - and evacuation capability diminishes the further we explore space - the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will become key to mission continuation and success. This need will be especially true should a crewmember become ill or injured wherein treatment and disposition “in-situ” ultimately falls to the crew itself to determine. To augment the requisite knowledge, skills, and abilities (KSAs) of a time-constrained exploration mission crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution. A CDSS would facilitate, guide, and inform Earth-independent medical operations while assisting crewmembers through various clinical presentations. The CDSS would allow crewmembers to take advantage of pre-mission training tied to the in-flight/in-mission use of pre-planned protocols that offer both a range of diagnostic options and “just-in-time” (refamiliarization) training and assistance. CDSS will expand such capabilities by improving the utility and effectiveness of various available diagnostic, treatment, and health maintenance tools, techniques, and measures.

ExMC↗
DOE OSTI2023

Flexible visualization of a 3rd party Intrusion Prevention (Security) tool: A use case with the ELK stack

A difficult aspect of cyber security is the ability to achieve automated real time intrusion prevention across various sets of systems. To this extent, several companies are offering comprehensive solutions that leverage an "accuracy of scale" and moving much of the intelligence and detection on the Cloud, relying on an ever-growing set of data and analytics to increase decision accuracy. Often, they provide tools to visualize the decision workflows in attack prevention (as well as tune the algorithm) but those solutions are not always practical as companies see the problem as "global" that is, from a unified Cyber-security standpoint. However, a key to a successful Cyber-security program is transparency and trust: from an experimental team viewpoint, this specifically means having the ability to immediately see what and from where, who has been blocked and being able to inform the community in case of a revoked access without the need for filing a "ticket" (that may eventually be answered) – in other words, rapid response to their user-base is essential but solutions targeting "sub-groups" in an organization are not often available. We have come up with a versatile solution leveraging the ELK stack (Elasticsearch, Logstash, & Kibana) and an IPS (Intrusion Prevention System) based WAF (Web Application Firewall) from Signal Sciences. Signal Science allows the streaming of detailed logs in a Logstash format suitable for custom solutions for visualization. By combining these two tools, we have strengthened our security posture and enabled individual experiments to monitor their own traffic. Specifically, the IPS WAF provides unique data such as country of origin, protocol, response code, source IP, and paths accessed. In this contribution, we will show how we engineered a visualization solution so experiment groups could access a dashboard with predefined graphs but also, where they can create individual customizable dashboards used to display blocked traffic and troubleshoot latency issues. We will discuss the details and procedures for developing and configuring these tools and how it benefits cyber security postures across our scientific based environment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Dynamics-Based Peptide–MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen–major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Finally, our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗
DOE OSTI2021

Exploring OpenSNAPI Use Cases and Evolving Requirements [Slides]

Emerging system architectures are rapidly transforming in order to meet shifting requirements. Motivated by expanding data volumes, energy efficiency concerns, and the omnipresent need to improve performance, architectures are increasingly adopting a data-centric approach. At the core of this concept is the goal of minimizing data motion and instead processing data in-situ to the greatest degree possible. Therefore, data-centric designs, in contrast to conventional CPU-centric models, typically distribute compute capabilities throughout the architecture. As part of this paradigm shift, a novel class of devices known as data processing units (DPUs), alongside CPUs and GPUs, are quickly forming a third pillar of data-centric systems. These devices, which include smart network adapters and switches, seek to offload computation on data at the network edge as well as in-flight within the network fabric. The Open Smart Network API (OpenSNAPI) project seeks to develop a unified API for DPU devices. In our previous talks, we introduced the OpenSNAPI project and detailed our investigations regarding the viability of offloading compute intensive kernels to BlueField DPUs. In contrast, in this talk we detail our efforts to offload application-level file I/O to the DPU. We also discuss plans and early efforts to explore in-network compute capabilities. Finally, we describe our observations with respect to the evolving design of OpenSNAPI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multi-Mechanism Flood Hazard Assessment: Example Use Case Studies

Multi-mechanism flood (MMF) events are caused by the combined effects of more than one flooding mechanism. Although floods can result from the occurrence of individual flood mechanisms, they can (and often do) result from multiple flooding mechanisms. MMF events may be more severe than single mechanism events, or they may differ in characteristics. To facilitate comprehensive risk-informed decision-making to protect against and mitigate the effects of flood events, understanding the hazard contributions from MMFs is important. Nevertheless, conventional probabilistic flood hazard assessment approaches typically focus on individual flood hazard mechanisms. This report is part of a research project funded by the US Nuclear Regulatory Commission (NRC) intended to assist NRC in developing the technical basis for guidance on developing probabilistic estimates of flood hazards for combinations of flood mechanisms. Specifically, the purpose of this report is to document two case studies to illustrate approaches for quantifying MMF hazards for inland and coastal flooding scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

5G Energy FRAME: The Design and Implementation of Data, Model, and Use Case (Year 2 Report)

This report summarizes the Year 2 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research (ASCR) Program. In this report, newest 5G equipment testing results are presented, along with two 5G-enabled AI/ML examples for grid applications; in addition, the work flow of grid edge, cloud, and High Performance Computing (HPC) platform is introduced, to support and interface the cross-domain simulation for power system transmission, distribution, and communication networks. Last but not least, the outlook for Year 3 work and the overarching impact of 5G Energy FRAME work to a multitude of stakeholders are provided. Additional 5G performance data now is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Evaluation of Use Cases and Types of Databases for Hosting Wind Power Data

The goal of the National Wind Power Production Data Dashboard project is to develop a publicly available platform to model, process, and share wind power with uncertainty quantification for the current and future onshore and offshore wind plants across the continental United States. As of 2021, more than 70,000 utility-scale wind turbines have been installed across the United States and this number is expected to grow in the next few years. A large-scale wind power production database is needed for stakeholders (policy makers, operators, and researchers) to access easily for their decision-making. Currently, meeting that need is a challenge. Most wind power operators and system operators focus on regions of their own interest, and the data are usually inaccessible to the public.

17 WIND ENERGY↗