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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 NTRS2001

The Conference Proceedings of the 2001 Air Transport Research Society (ATRS) of the WCTR Society

The ATRS held its 5th Annual conference at the City University of Hong Kong Campus in July 2001. The conference was a success with nearly 140 participants including 70 presenters. Titles that comprise Volume 2 include: Intelligent Airport Gate Assignment System; A Study on the Effects of the Personality Compatibility to the Job Performance; ITS/CVO Application for Air cargo Transportation in Korea; An Airport as a Logistics and Economic Hub: The Case of Incheon International Airport; The Impact Of Aviation Safety over the Consumer's Behavior; The Integration of China and Taiwan Air Networks for Direct Air Cargo Services; Quality perception and carrier choice in Civil Aviation; Future Trends in Business Travel Decision Making; Cooperation Among German Airports in Europe; Inbound and Outbound Air Passenger Traffic Forecasting between the United States and Selected Asian countries; An Evaluation of Alternative Facilities for Airport Redevelopment using Fuzzy Linguistic Approach; Economic Analysis of Airline Alliances; The Aviation Cooperation between the two Koreas Preparing for the Reunification of the Peninsula; and A Study on the Air Transport Cooperation in Northeast Asia between China, Japan and Korea.

Lee, Yeong-Heok↗

DOE OSTI2021

Key Learnings from Hydraulic Fracturing Test Site - 2 (HFTS-2), Delaware Basin

Hydraulic fractures (HF) play a dominant role in oil and gas production from unconventional reservoirs, fracture characteristics, geometry, and extent are generally not well known and are typically inferred from various monitoring methods or subsurface models. The Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Wolfcamp formation of the Permian (Delaware) Basin. HFTS-2, a unique joint industry program (JIP) that brings together government, academia, and industry, focused on field data acquisition and interpretation. The project has built a comprehensive HF research dataset that includes microseismic (MS), 1,500 ft of core, diagnostic formation injection tests (DFIT), fluid characterization, advanced logs, geochemistry, downhole gauges, varying completions, and permanent fiber optic (FO) cable in vertical and horizontal wells. These data were integrated and used to characterize HF geometry/dimensions and resulting depletion profiles, gain insights for completion design, and achieve a better understanding of parent well depletion effects on child well performance. The analysis and integration of the comprehensive HFTS-2 diagnostics dataset provide insights for optimal well placement and completions and improves understanding about the effects of parent well depletion. Furthermore, this dataset can be used to blind test and develop subsurface workflows for unconventional resources. Key learnings from HFTS-2 and the new technologies tested can easily be transferred to other unconventional plays.

58 GEOSCIENCES↗

DOE OSTI2023

Effect of printing parameters on 3D-printed carbon fiber-reinforced polymer composites under magnetic field control

Magnetic field controlled (MFC) 3D printing is a promising method for realizing the intelligent control of the structural performance of carbon fiber-reinforced plastic (CFRP) composites by adjusting the fiber orientation during the printing process. However, few studies have focused on the influence of the printing parameters on the magnetic control of the fiber direction. Therefore, in this study, the mechanical properties and internal fiber distributions of MFC 3D-printed samples of CFRP composites with 10 wt% fiber content, which were printed at different speeds and layer thicknesses, were investigated. Subsequently, the effects of the fiber content on the mechanical properties and fiber distribution of the MFC 3D-printed samples were studied. Furthermore, the origin of fiber fracture was evaluated through observation and analysis of the fracture section using scanning electron microscopy. The fiber orientation could be well adjusted under the action of a magnetic field and adjustment of printing parameters, thereby improving the tensile strength, young's modulus significantly with slightly decrease of fracture elongation. The recommended parameter combinations (printing speed and layer thickness) were 40 mm/s and 0.2 mm for 10 wt% fiber content CFRP composites.

36 MATERIALS SCIENCE↗

NASA NTRS2000

ISS Microgravity Environment Monitoring System (MEMS): System Design - Part 1

The Principal Investigator Microgravity Services project at the NASA Glenn Research center supports Principal Investigators of the microgravity science community as they evaluate the effects of acceleration on their experiments. The Principal Investigator Microgravity Services' primary responsibility is to support NASA sponsored investigators in the area of acceleration data analysis, interpretation and the monitoring of the microgravity environment on-board various carriers. The microgravity environment is a rich and very complex dynamic one. It is subject to quasi-steady accelerations, higher frequency acceleration, and transient disturbances. With the advent of the International Space Station operation, a significant amount of data is expected to be downlinked and processed for both the space station microgravity environment characterization (verification) and scientific experiments. Therefore, to help principal investigator teams monitor the microgravity environment on-board the International Space Station in order to avoid negative impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services project is currently developing an artificial intelligence monitoring system, which will notify the principal investigator teams in near real time of any change in the microgravity environment susceptible to affect their experiments.

Jules, Kenol↗

DOE OSTI2024

Analysis of Automated Transit Network Systems with Battery-Electric Vehicles in Automated Mobility Districts: Preprint

The paper provides an overview of the insights and findings of the National Renewable Energy Laboratory's (NREL) ongoing research on the implementation of automated mobility districts (AMDs). AMD is a term coined by NREL to describe a geographically defined district or major activity center located in a dense urban setting with mobility applications provided by automated/autonomous vehicle (AV) systems spanning internal circulation and first-mile/last-mile connections to regional transportation hubs. Research over the past five years has focused on understanding the evolution of AMDs, beginning with demonstrations of automated shuttles prior to the pandemic, to more integrated on-demand mobility systems currently in initial stages of deployment. Initial insights and findings from earlier studies include the need for designation of a "jurisdiction having authority," a clear vision of a complete system to provide end-to-end mobility services, and the requisite intelligent infrastructure to complement AV technology. NREL's most recent Phase III research investigates station boarding/alighting (curb) issues, the full electrification of fleets, and the need for a systems engineering methodology (SEM) to properly analyze the complexities resulting from the convergence of automation, on-demand mobility, and electrification of the transit systems within the AMD. The paper reviews the findings of AMD research conducted during Phase I, II, and III, with special emphasis on Phase III results with respect to a descriptive example of the proposed SEM when a "digital twin" analytical model is used to simulate the transport fleet's battery-electric vehicle miles of travel and associated duty cycles through a rigorous analytical assessment with a comprehensive modeling process.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

The water use of data center workloads: A review and assessment of key determinants

The global importance of data center water use is increasing with the rapid growth of digitalization and artificial intelligence. This study analyzes the factors influencing workload-level water use, measured in liters consumed per workload, to guide water-saving strategies in data centers. Our findings reveal workload-level water use variations exceeding 10,000-fold, driven by over 1000-fold differences in water consumption per kilowatt hour of server electricity consumed and approximately 10-fold differences in server workload efficiency. Key determinants are ranked as server efficiency, electrical grid water consumption factors, server utilization, cooling system type, infrastructure efficiency, climate zone, inactive server percentage, and server refresh cycle. Notably, there is no single recipe for minimizing water use; instead, optimal outcomes depend on tailored combinations of these factors. This analysis addresses critical knowledge gaps by identifying the determinants of data center water use and exploring their achievable minima under diverse site-specific constraints.

Data centers↗

NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗

DOE OSTI2022

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗

DOE OSTI2022

Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis

The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation. The plant reload optimization framework development project aims to build an artificial intelligence, i.e., Genetic Algorithm (GA), based reactor core designing tool taking into account reactor safety and fuel performance analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1977

A stand-alone interactive graphics finite element modeling system

The finite element modeling (FEM) system described here is an interactive graphics system designed for use with an intelligent terminal. It offers an economical alternative to the conventional methods (hand compilation and remote terminals) of 2 and 3-dimensional model generation. Using local computing and local storage, this FEM system allows the operator to create and display 3-dimensional models entirely off-line from the host computer. This eliminates costly on-line computer and host-processing time, and makes the user more effective by greatly reducing computer response time. The system allows model generation by down-loading node and element data from the host computer or by using the system's model generation operations for digitizing and automatic creation. The system then generates completed model information in the desired analysis package's bulk data format and transmits it to the host computer for finite element analysis.

Gingerich, J. Z.↗

NASA NTRS2002

Reusable Autonomy

Currently, spacecraft ground systems have a well defined and somewhat standard architecture and operations concept. Based on domain analysis studies of various control centers conducted over the years it is clear that ground systems have core capabilities and functionality that are common across all ground systems. This observation alone supports the realization of reuse. Additionally, spacecraft ground systems are increasing in their ability to do things autonomously. They are being engineered using advanced expert systems technology to provide automated support for operators. A clearer understanding of the possible roles of agent technology is advancing the prospects of greater autonomy for these systems. Many of their functional and management tasks are or could be supported by applied agent technology, the dynamics of the ground system's infrastructure could be monitored by agents, there are intelligent agent-based approaches to user-interfaces, etc. The premise of this paper is that the concepts associated with software reuse, applicable in consideration of classically-engineered ground systems, can be updated to address their application in highly agent-based realizations of future ground systems. As a somewhat simplified example consider the following situation, involving human agents in a ground system context. Let Group A of controllers be working on Mission X. They are responsible for the command, control and health and safety of the Mission X spacecraft. Let us suppose that mission X successfully completes it mission and is turned off. Group A could be dispersed or perhaps move to another Mission Y. In this case there would be reuse of the human agents from Mission X to Mission Y. The Group A agents perform their well-understood functions in a somewhat but related context. There will be a learning or familiarization process that the group A agents go through to make the new context, determined by the new Mission Y, understood. This simplified scenario highlights some of the major issues that need to be addressed when considering the situation where Group A is composed of software-based agents (not their human counterparts) and they migrate from one mission support system to another. This paper will address: - definition of an agent architecture appropriate to support reuse; - identification of non-mission-specific agent capabilities required; - appropriate knowledge representation schemes for mission-specific knowledge; - agent interface with mission-specific knowledge (a type of Learning); development of a fully-operational group of cooperative software agents for ground system support; architecture and operation of a repository of reusable agents that could be the source of intelligent components for realizing an autonomous (or nearly autonomous) agent-based ground system, and an agent-based approach to repository management and operation (an intelligent interface for human use of the repository in a ground-system development activity).

Truszkowski, Walt↗

DOE OSTI2022

Frontiers in computing for artificial intelligence

An emerging diversity of computational platforms offers many different approaches to adopting the paradigm of artificial intelligence to the study of electron-ion collisions. Here we review several leading candidates in this computational frontier and their workflows for experimental applications of artificial intelligence that may impact the future Electron-Ion Collider. We discuss the motivation for exploring novel methods to solve artificial intelligence and machine learning problems including with customized devices, quantum simulation, and heterogeneous computing systems. Furthermore, these technologies offer promising approaches to address some of the leading concerns of future computing that may impact the Electron-Ion Collider but they will require further development and testing in order to support future planning efforts.

detector design and construction technologies and ↗

DOE OSTI2022

Development of Plant Reload Optimization Framework Capabilities for Core Design and Fuel Performance Analysis

The United States (U.S.) nuclear industry faces a challenge in maintaining required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects of light water reactor (LWR) nuclear power plant (NPP) operations. Safety can become more economical by using a risk-informed ecosystem, such as the one being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program promotes a wide range of research and development activities with the goal of maximizing both the safety and economic efficiency of NPPs through improved scientific understanding, especially given many plants are now considering second license renewals. The RISA Pathway has two main goals: (1) deploy methodologies and technologies that better represent safety margins and cost and safety factors and (2) develop advanced applications that enable cost-effective plant operation. The Plant Reload Optimization Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses, and also uses artificial intelligence to support optimization of core design solutions. This report summarizes Fiscal Year 2022 (FY-22) activity in platform capability developments in RAVEN. This platform performs simulations using industry codes for core design (i.e., PARCS) and fuel performance (i.e., TRANSURANUS) which will allow expansion of the capabilities to include advanced fuel designs such as accident-tolerant fuel (ATF)s with high burnup.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1995

Wide-bandwidth high-resolution search for extraterrestrial intelligence

Research was accomplished during the third year of the grant on: BETA architecture, an FFT array, a feature extractor, the Pentium array and workstation, and a radio astronomy spectrometer. The BETA (this SETI project) system architecture has been evolving generally in the direction of greater robustness against terrestrial interference. The new design adds a powerful state-memory feature, multiple simultaneous thresholds, and the ability to integrate multiple spectra in a flexible state-machine architecture. The FFT array is reported with regards to its hardware verification, array production, and control. The feature extractor is responsible for maintaining a moving baseline, recognizing large spectral peaks, following the progress of previously identified interesting spectral regions, and blocking signals from regions previously identified as containing interference. The Pentium array consists of 21 Pentium-based PC motherboards, each with 16 MByte of RAM and an Ethernet interface. Each motherboard receives and processes the data from a feature extractor/correlator board set, passing on the results of a first analysis to the central Unix workstation (through which each is also booted). The radio astronomy spectrometer is a technological spinoff from SETI work. It is proposed to be a combined spectrometer and power-accumulator, for use at Arecibo Observatory to search for neutral hydrogen emission from condensations of neutral hydrogen at high redshift (z = 5).

Horowitz, Paul↗

DOE OSTI2026

A roadmap toward scaling, reasoning and self-evolving foundation models for nuclear and particle physics

Foundation models have revolutionized artificial intelligence, with Large Language Models demonstrating unprecedented capabilities in multimodal understanding, reasoning and tool use. Nuclear and particle physics stands at a critical juncture where similar transformative potential awaits realization. The field generates exabytes of experimental data, exascale simulations, and decades of theoretical insights — yet these remain largely disconnected from modern Artifical Intelligence (AI) capabilities, with most physics AI applications confined to narrow, task-specific models that suffer from domain shifting when applied to real experimental data. We present a roadmap for FM4NPP (Foundation Model for Nuclear and Particle Physics), systematically scaling from current proof-of-concept models to trillion-parameter architectures capable of autonomous discovery. Our approach advances three critical frontiers: unified data infrastructure integrating detector data, scientific knowledge and computational tools across global facilities; multi-facility foundation models enabling cross-experiment knowledge transfer and accelerated discovery; and agentic AI capabilities for reasoning and autonomous tool use. The resulting self-evolving FM4NPP will transform physics research by converting time-intensive data analysis, theory derivation and computational bottlenecks into rapid AI–human collaborative discovery. This paradigm shift promises to fundamentally accelerate scientific progress in nuclear and particle physics, enabling researchers to focus on high-level insights while AI handles routine analysis and explores vast parameter spaces beyond human capacity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1991

Intelligent Robotic Systems Study (IRSS), phase 3

This phase of the Intelligent Robotic Systems Study (IRSS) examines some basic dynamics and control issues for a space manipulator attached to its worksite through a compliant base. One example of this scenario is depicted, which is a simplified, planar representation of the Flight Telerobotic Servicer (FTS) Development Test Flight 2 (DTF-2) experiment. The system consists of 4 major components: (1) dual FTS arms to perform dextrous tasks; (2) the main body to house power and electronics; (3) an Attachment Stabilization and Positioning Subsystem (ASPS) to provide coarse positioning and stabilization of the arms, and (4) the Worksite Attachment Mechanism (WAM) which anchors the system to its worksite, such as a Space Station truss node or Shuttle bay platform. The analysis is limited to the DTF-2 scenario. The goal is to understand the basic interaction dynamics between the arm, the positioner and/or stabilizer, and the worksite. The dynamics and controls simulation model are described. Analysis and simulation results are presented.

Source record↗

NASA NTRS2009

Real-Time Detection of Dust Devils from Pressure Readings

A method for real-time detection of dust devils at a given location is based on identifying the abrupt, temporary decreases in atmospheric pressure that are characteristic of dust devils as they travel through that location. The method was conceived for use in a study of dust devils on the Martian surface, where bandwidth limitations encourage the transmission of only those blocks of data that are most likely to contain information about features of interest, such as dust devils. The method, which is a form of intelligent data compression, could readily be adapted to use for the same purpose in scientific investigation of dust devils on Earth. In this method, the readings of an atmospheric- pressure sensor are repeatedly digitized, recorded, and processed by an algorithm that looks for extreme deviations from a continually updated model of the current pressure environment. The question in formulating the algorithm is how to model current normal observations and what minimum magnitude deviation can be considered sufficiently anomalous as to indicate the presence of a dust devil. There is no single, simple answer to this question: any answer necessarily entails a compromise between false detections and misses. For the original Mars application, the answer was sought through analysis of sliding time windows of digitized pressure readings. Windows of 5-, 10-, and 15-minute durations were considered. The windows were advanced in increments of 30 seconds. Increments of other sizes can also be used, but computational cost increases as the increment decreases and analysis is performed more frequently. Pressure models were defined using a polynomial fit to the data within the windows. For example, the figure depicts pressure readings from a 10-minute window wherein the model was defined by a third-degree polynomial fit to the readings and dust devils were identified as negative deviations larger than both 3 standard deviations (from the mean) and 0.05 mbar in magnitude. An algorithm embodying the detection scheme of this example was found to yield a miss rate of just 8 percent and a false-detection rate of 57 percent when evaluated on historical pressure-sensor data collected by the Mars Pathfinder lander. Since dust devils occur infrequently over the course of a mission, prioritizing observations that contain successful detections could greatly conserve bandwidth allocated to a given mission. This technique can be used on future Mars landers and rovers, such as Mars Phoenix and the Mars Science Laboratory.

Wagstaff, Kiri↗

NASA NTRS2012

Digital Architecture for a Trace Gas Sensor Platform

A digital architecture has been implemented for a trace gas sensor platform, as a companion to standard analog control electronics, which accommodates optical absorption whose fractional absorbance equivalent would result in excess error if assumed to be linear. In cases where the absorption (1-transmission) is not equivalent to the fractional absorbance within a few percent error, it is necessary to accommodate the actual measured absorption while reporting the measured concentration of a target analyte with reasonable accuracy. This requires incorporation of programmable intelligence into the sensor platform so that flexible interpretation of the acquired data may be accomplished. Several different digital component architectures were tested and implemented. Commercial off-the-shelf digital electronics including data acquisition cards (DAQs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), and microcontrollers have been used to achieve the desired outcome. The most completely integrated architecture achieved during the project used the CPLD along with a microcontroller. The CPLD provides the initial digital demodulation of the raw sensor signal, and then communicates over a parallel communications interface with a microcontroller. The microcontroller analyzes the digital signal from the CPLD, and applies a non-linear correction obtained through extensive data analysis at the various relevant EVA operating pressures. The microcontroller then presents the quantitatively accurate carbon dioxide partial pressure regardless of optical density. This technique could extend the linear dynamic range of typical absorption spectrometers, particularly those whose low end noise equivalent absorbance is below one-part-in-100,000. In the EVA application, it allows introduction of a path-length-enhancing architecture whose optical interference effects are well understood and quantified without sacrificing the dynamic range that allows quantitative detection at the higher carbon dioxide partial pressures. The digital components are compact and allow reasonably complete integration with separately developed analog control electronics without sacrificing size, mass, or power draw.

Gonzales, Paula↗