SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The recent gamma ray and neutron observations made by the SMM Gamma Ray Spectrometer are reviewed. The implication these observations hold for understanding particle acceleration in solar flares are discussed. The data require that both electrons and ions must be accelerated together to relativistic energies and interact with matter in a time scale of seconds.
An investigation was conducted by the National Aeronautics and Space Administration, Ames Research Center, and the Naval Air Development Center, Aviation Medical Acceleration Laboratory, to study the effects of acceleration on pilot performance and to obtain some meaningful data for use in establishing tolerance to acceleration levels. The flight simulator used in the study was the Johnsville centrifuge operated as a closed loop system. The pilot was required to perform a control task in various sustained acceleration fields typical of those that Might be encountered by a pilot flying an entry vehicle in which he is seated in a forward-facing position. A special restraint system was developed and designed to increase the pilot's tolerance to these accelerations. The results of this study demonstrated that a well-trained subject, such as a test pilot, can adequately carry out a control task during moderately high accelerations for prolonged periods of time. The maximum levels of acceleration tolerated were approximately 6 times that of gravity for approximately 6 minutes, and varied slightly with the acceleration direction. The tolerance runs were in each case terminated by the subject. In all but two instances, the cause was extreme fatigue. On two occasions the subject terminated the run when he "grayed out." Although there were subjective and objective findings involving the visual and cardiovascular systems, the respiratory system yielded the more critical limiting factors. It would appear that these limiting factors were less severe during the "eyeballs-out" accelerations when compared with the "eyeballs-in" accelerations. These findings are explained on the basis of the influence that the inertial forces of acceleration have on the mechanics of respiration. A condensed version of this report was presented at the Annual Meeting of the Aerospace Medical Association, Miami Beach, May 5-11, 1960, in a paper entitled "Ability of Pilots to Perform a Control Task in Various Sustained Acceleration Fields."
As Earth observation data volume, velocity, and variety continues to accelerate, so do the challenges of working with those data. The computation, storage, and network transfer requirements for multi-petabyte datasets demand solutions that allow users to process, store, and move fewer bytes while still finding the valuable insights they seek. Our cloud-based data transformation framework, Harmony, tackles these challenges by providing consistent, standards-based interface to multiple disparate backend processors collocated with NASA EOSDIS data. It allows data scientists to perform data processing and harmonization activities across many datasets through familiar tooling. Simultaneously, it provides a clear, consistent interface for data providers to expose new or customized backend services for their data. This talk will discuss Harmony, its architecture and interface, and the path forward for cloud-based NASA EOSDIS data harmonization services.
As demand for digital services accelerates, the energy and environmental footprint of data centers faces increasing scrutiny. While hyperscale cloud facilities have driven efficiency gains, small and midsize U.S. data centers remain a critical yet underexamined segment with significant untapped potential for energy savings. This study leverages data from the Commercial Buildings Energy Consumption Survey (CBECS) to analyze trends in server stocks, computing customers, cooling system adoption and efficiency, and geospatial distribution from 2012 to 2018. Findings reveal a sharp decline in small and midsize data centers, from 1.764 million to 1.398 million, with server counts dropping from 5.177 million to 4.262 million—aligning with the broader shift toward cloud computing. More than 40 % of servers in small data centers and 55 % in midsize data centers are housed in office buildings, and over half of all servers are concentrated in climate zones 5A (cold), 3A (mixed-humid), and 4A (mixed-humid), with the highest densities in metropolitan hubs. While direct expansion units remain the dominant cooling system, a clear transition toward more energy-efficient solutions, particularly air economizers, is evident. By integrating server and cooling system distributions, we estimate Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) for U.S. data centers by size and year. Results show that midsize data centers are more energy-efficient but more water-intensive due to the widespread use of water-cooled chillers. These findings highlight the trade-offs in cooling system selection and provide a critical foundation for policies aimed at enhancing efficiency in an evolving data center landscape.
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.
The purpose of this report is to begin the development of a maximally efficient process for licensing and deploying new materials in Advanced Non-Light-Water Reactors (ANLWRs). Some new materials that are to be used in some new plants are seen as possibly introducing risks, because our understanding of those new materials’ behavior in the conditions generated by some novel plant designs is less complete than our understanding of the behavior of materials with long use histories in existing designs. In these cases, an approved code/standard or a code case to support the use of these materials in the novel design’s safety case may not exist for the regulator to utilize as part of the licensing determination. This circumstance creates the potential for an extremely long licensing process for new designs using new materials. The present strategy is to show how to manage these risks proactively, in such a way as to permit licensing decisions to be made in a timely manner, based on this risk management process. The present report outlines the gaps in the current codes to support deployment and use of novel materials and begins the development of the necessary risk management framework that is focused on the subject materials issues; it is based on risk-informed in-service surveillance practices, carried out in such a way as to compensate for current limitations in our state of knowledge. This development will enable licensing and deployment of the subject materials, conditional on the proactive surveillance process to be established. While this report is occasioned by limitations in our knowledge of certain materials issues that may arise in advanced designs, in-service surveillance is always done in order to compensate for a lack of knowledge: if we knew that components were not already failed and not trending toward failure, we would not perform surveillance, even in current-generation plants (except that prescriptive requirements would force us to do so). What is different about the surveillance program discussed here is that the issues are newer and the relevant experience base is less complete, so the surveillance presently contemplated may need to measure new things and/or measure them more often than has been traditional for surveillance coupons. The present report is devoted to the risk management framework and applies American Society of Mechanical Engineers Boiler and Pressure Vessel Code Section XI, Division [1] to establish the structure of a protocol for carrying out the necessary surveillance. These documents are generic: they do not tell us how often to surveille, or what to surveille, or what to measure, but rather how to determine those things, given certain technical inputs. The Regulatory Development R&D Program [2] is currently developing the companion supporting technical basis for the materials surveillance technology that, when completed and validated, can be used by owner/operator and NRC to implement a materials degradation management program for ANLWRs. This report also outlines salient points of discussion, positive potential outcomes, and potential concerns from industry and the USNRC. These aspects of the report intend to inform future work to develop a proposed technical process for adoption by the industry and endorsement by the USNRC to allow developers to propose a risk informed and conservative approach for the use of materials where operating experience/data and codes and standards may not exist for use of a novel material in an operating reactor environment. Additionally, such a technology could be leveraged to potentially reduce part of the upfront materials data requirements from ongoing long-term materials testing so that early action on license application could be undertaken by NRC, in parallel with the continuation of long-term data collection. This could accelerate the schedule for a first-of-a-kind ANLWR deployment or a nth-of-a-kind new materials insertion for established ANLWR designs.
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.
Screen test vs life test data and infrared measurements to determine acceleration factor for metal film resistors
Tensor algebra operations represent an important class of algorithms used across many applications, including machine learning, scientific computing, and data analytics. As a result, the efficient generation of custom accelerators for tensor operations has received increased attention. Previous efforts have produced automated tools enabling users to prototype and explore optimized accelerators. However, little effort has been focused on the host-accelerator interaction in these tools. Efficient use of hardware accelerators requires knowledge about the accelerator's capabilities (operations, data formats, and opcode support), the host CPU microarchitecture (e.g., memory hierarchy), the host-accelerator interface, and the application's features (which code regions should be mapped onto an accelerator). Manually rewriting the original applications to facilitate improved custom accelerator mapping is an error-prone and time-consuming endeavor. To cope with this, we propose AXI4MLIR, a new framework to automatically generate and optimize the communication between the host CPU and arbitrary accelerators that implement linear algebra algorithms. AXI4MLIR extends the MLIR compiler framework to automatically generate efficient host-accelerator driver code for accelerators with AXI-based interfaces. Our compiler extensions enable automatic driver code generation while carefully considering the host's memory hierarchy and target accelerator features. To demonstrate the flexibility and utility of AXI4MLIR, we test it with diverse use cases that include different types of accelerators, tiling scenarios, and dataflow schemes. We compare our experimental results to manual implementations of host-accelerator driver code and find that our approach can reduce CPU cache references by 56% and deliver up to a 1.65x speedup.
On-line time sharing data processing and computer analysis techniques, and acceleration effects on cardiovascular and respiratory systems
Owing to the complexity of coupling mechanical and electrical solvers for finite element analysis (FEA) of mechanical abuse of a battery, the simulation should be performed by incorporating a local short circuit model activated based on the status of battery elements. However, the approach should also consider various other conditions of the battery simulated, such as the initial state of charge (SOC), for precise simulations of the internal short. This study proposes an approach developed with a mechanical model and single-particle battery model, along with a reaction kinetics model for thermal abuse using LS-DYNA. Several key parameters were selected, such as the activation criteria, kinetic abuse and element integration points during the internal short circuit induced by the indenter. The impact of these key parameters in the FEA on thermal runaway response for various initial SOCs from 25 to 100 % were analyzed. The FEA results obtained were compared against experimental results with various parameters such as activation criterion of internal short, kinetic abuse based on accelerating rate calorimetry (ARC) data. Finally, the root causes of the discrepancy between the FEA and experimental results are discussed regarding integration point what solver can make difference during calculation.
The existing photofission product yield data sets are often based on nuclear models, and limited experimental measurements of these fission product yields have been performed. These experiments resulted in measured cumulative fission product yields (CFPYs) for 238 U and 232 Th for the bremsstrahlung X-ray endpoint energies of 8, 14, and 20 MeV. The half-lives of the reported fission products range from 1.07–40.8 s. This work is motivated by a demonstrated need for novel isotope production methods for nuclear forensics and improved nuclear data to support the nonproliferation community. A high-purity germanium detector and a pneumatic transfer system were employed for measurements of short-lived fission products, collecting data between cycles of accelerator irradiation and measurement.
The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.
Techno-economic analysis (TEA) and life cycle assessment (LCA) were conducted for an integrated system designed for the production of volatile fatty acid (VFA) from food waste. The TEA estimated a production cost of $\$$3.12/kg VFA, and the LCA predicted negative greenhouse gas (GHG) emissions of -0.4 kg CO 2 e/kg VFA, driven primarily by diverting organic waste from landfills and avoiding methane emissions while producing valuable chemical products. Hotspot analysis showed arrested methanogenesis (AM) fermentation as the largest contributor to costs (37%) and environmental burden (47%), driven by high sodium hydroxide (NaOH) consumption. Distillation and microbial electrosynthesis (MES) units were the next-largest environmental contributors (28% and 18%). Major cost drivers also included residuals management (biosolids and wastewater) and the equipment and operating costs for AM, MES, and sonication pretreatment units. Although the new integrated system is environmentally benign, its costs and environmental impacts can be further reduced by integrating alternative energy sources, minimizing chemical and energy inputs through process optimization, and improving efficiency. In conclusion, this work highlighted the viability of waste-derived VFA production and provided a clear, data-driven strategy to accelerate the commercialization of waste valorization technology.
High-resolution environmental monitoring is necessary to record, understand, and predict biogeochemical and ecological changes particularly in coastal systems but brings significant challenges in processing and making rapidly available the resulting data. The COMPASS-FME project established a network of coastal observational sites across the Chesapeake Bay and western Lake Erie regions extensively instrumented with soil, vegetation, and weather sensors logging data every 15 min. Our data processing framework, written in R and completely open source, prioritizes rapid model-experiment iteration and makes biogeochemical data rapidly available for quality assurance/quality control, analysis, and model ingestion. This pipeline is distinguished by a standardized and modular approach to data curation, extensive metadata and documentation, and its high performance. These attributes combine to make biogeochemical data rapidly accessible across COMPASS-FME and the broader community. Flexible, powerful, and reproducible approaches to handling high-volume environmental data are crucial for accelerating biogeosciences research.
Abstract There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young’s modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.
Low-exergy waste heat, which constitutes the majority of industrial-scale thermal losses, remains largely unrecoverable with conventional technologies. Thermoelectrics offer a solid-state solution for converting this hard-to-access energy into electricity, making them attractive for decentralized power generation and sensor applications. High-entropy materials (HEMs) have gained traction as a strategy for better-performing thermoelectrics, but the mechanisms driving their benefits require further exploration. This article highlights key insights for heat and electronic transport in HEMs. For heat transport, we argue that reduced, and often ultralow, lattice thermal conductivity in HEMs—with respect to ordered counterparts—can be taken for granted, emerging naturally as a fifth core effect of high-entropy systems. While band convergence is often considered beneficial for electronic transport, its impact depends strongly on the electronic structure. We summarize the scenarios where it can be detrimental to thermoelectric performance. These insights motivate strategies that align seamlessly with advancements in artificial intelligence and data-driven approaches, helping accelerate the discovery of next-generation thermoelectric materials.