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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

RANGERS: Methodology for Design and Performance Assessment of Engineered Barrier Systems in a Salt Repository for HLW/SNF: Synthesis Report

Salt formations have long been recognized as a highly favorable host rock for the final disposal of high-level radioactive waste (HLW) in deep geological repositories. Their unique properties, including exceptional impermeability, self-healing capabilities, and thermal conductivity, make them a reliable natural barrier for the deep disposal of radioactive waste. This report focuses on the development and application of a methodology for assessing the integrity and per formance of the Engineered Barrier System (EBS) within salt-based repositories, a critical component of the multi-barrier system ensuring safe radioactive waste disposal.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2017

EVA Human Health and Performance Benchmarking Study Overview and Development of a Microgravity Protocol

The primary objective of this study is to develop a protocol to reliably characterize human health and performance metrics for individuals working inside various EVA suits under realistic spaceflight conditions. Expected results and methodologies developed during this study will provide the baseline benchmarking data and protocols with which future EVA suits and suit configurations (e.g., varied pressure, mass, center of gravity [CG]) and different test subject populations (e.g., deconditioned crewmembers) may be reliably assessed and compared. Results may also be used, in conjunction with subsequent testing, to inform fitness-for-duty standards, as well as design requirements and operations concepts for future EVA suits and other exploration systems.

Norcross, Jason↗
NASA NTRS1992

Proof test methodology for composites

The special requirements for proof test of composites are identified based on the underlying failure process of composites. Two proof test methods are developed to eliminate the inevitable weak fiber sites without also causing flaw clustering which weakens the post-proof-test composite. Significant reliability enhancement by these proof test methods has been experimentally demonstrated for composite strength and composite life in tension. This basic proof test methodology is relevant to the certification and acceptance of critical composite structures. It can also be applied to the manufacturing process development to achieve zero-reject for very large composite structures.

Wu, Edward M.↗
NASA NTRS2001

Space Shuttle RTOS Bayesian Network

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores. Using a prioritization of measures from the decision-maker, trade-offs between the scores are used to rank order the available set of RTOS candidates.

Morris, A. Terry↗
NASA NTRS2004

COTS Ceramic Chip Capacitors: An Evaluation of the Parts and Assurance Methodologies

Commercial-Off-The-Shelf (COTS) multilayer ceramic chip capacitors (MLCCs) are continually evolving to reduce physical size and increase volumetric efficiency. Designers of high reliability aerospace and military systems are attracted to these attributes of COTS MLCCs and would like to take advantage of them while maintaining the high standards for long-term reliable operation they are accustomed io when selecting military qualified established reliability (MIL-ER) MLCCs. However, MIL-ER MLCCs are not available in the full range of small chip sizes with high capacitance as found in today's COTS MLCCs. The objectives for this evaluation were to assess the long-term performance of small case size COTS MLCCs and to identify effective, lower-cost product assurance methodologies. Fifteen (15) lots of COTS X7R dielectric MLCCs from four (4) different manufacturers and two (2) MIL-ER BX dielectric MLCCs from two (2) of the same manufacturers were evaluated. Both 0805 and 0402 chip sizes were included. Several voltage ratings were tested ranging from a high of 50 volts to a low of 6.3 volts. The evaluation consisted of a comprehensive screening and qualification test program based upon MIL-PRF-55681 (i.e., voltage conditioning, thermal shock, moisture resistance, 2000-hour life test, etc.). In addition, several lot characterization tests were performed including Destructive Physical Analysis (DPA), Highly Accelerated Life Test (HALT) and Dielectric Voltage Breakdown Strength. The data analysis included a comparison of the 2000-hour life test results (used as a metric for long-term performance) relative to the screening and characterization test results. Results of this analysis indicate that the long-term life performance of COTS MLCCs is variable -- some lots perform well, some lots perform poorly. DPA and HALT were found to be promising lot characterization tests to identify substandard COTS MLCC lots prior to conducting more expensive screening and qualification tests. The results indicate that lot- specific screening and qualification are still recommended for high reliability applications. One significant and concerning observation is that MIL- type voltage conditioning (100 hours at twice rated voltage, 125 C) was not an effective screen in removing infant mortality parts for the particular lots of COTS MLCCs evaluated.

Brusse, Jay A.↗
NASA NTRS1975

Systems cost performance analysis (study 2.3). Volume 1: Executive summary

A methodology for developing balanced designs of spacecraft subsystems which interrelates cost, performance, safety, and schedule considerations was developed. The methodology consists of a two-step process: selection of all hardware designs which satisfy the given performance and safety requirements; and estimating the cost and schedule required to design, build, and operate each spacecraft design. Aggregate equations were written to describe the performance, safety (reliability), cost, and schedule required for one type of stabilization and control subsystem in terms of the equipment used. The methodology was applied to unmanned, automated spacecraft subsystems, and the resulting model was implemented as a digital computer program.

Source record↗
DOE OSTI2025

Automated model generation and parameter estimation of building energy models using an ontology-based framework

This study presents a methodology for automated model generation and parameter estimation of building energy models using semantic modeling and Bayesian estimation. Semantic modeling techniques are used to represent the system components and their interactions, facilitating the automatic generation of a simulation model from dynamic component models. The proposed approach is applied to a case study of a ventilation system where a simulation model is generated, calibrated, and assessed through different performance metrics. These metrics demonstrate the accuracy and reliability of both model point estimates and probabilistic prediction intervals across all model outputs. Overall, the proposed methodology offers a systematic and automated approach to model development and calibration in building energy systems, with potential applications in building performance analysis, monitoring, and optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Software life cycle methodologies and environments

Products of this project will significantly improve the quality and productivity of Space Station Freedom Program software processes by: improving software reliability and safety; and broadening the range of problems that can be solved with computational solutions. Projects brings in Computer Aided Software Engineering (CASE) technology for: Environments such as Engineering Script Language/Parts Composition System (ESL/PCS) application generator, Intelligent User Interface for cost avoidance in setting up operational computer runs, Framework programmable platform for defining process and software development work flow control, Process for bringing CASE technology into an organization's culture, and CLIPS/CLIPS Ada language for developing expert systems; and methodologies such as Method for developing fault tolerant, distributed systems and a method for developing systems for common sense reasoning and for solving expert systems problems when only approximate truths are known.

Fridge, Ernest↗
NASA NTRS1992

Probabilistic design of advanced composite structure

Advanced composite technology offers potentials for sizable improvements in many areas: weight savings, maintainability, durability, and reliability. However, there are a number of inhibitors to these improvements. One of the biggest inhibitors is the imposition of traditional metallic approaches to design of composite structure. This is especially detrimental in composites because new materials technology demands new design approaches. Of particular importance are the decisions made regarding structural criteria. Significant changes cannot be implemented without careful consideration and exploration. This new approach is to implement changes on a controlled, verifiable basis. Probabilistic design is the methodology and the process to accomplish this. Its foundation is to base design criteria and objectives on reliability targets instead of arbitrary factors carried over from metallic structural history. The background is discussed of probabilistic design and the results are presented of a side-by-side comparison to generic aircraft structure designed the 'old' way and the 'new'. Activities are also defined that need to be undertaken to evolve available approaches to probabilistic design followed by summary and recommendations.

Gray, P. M.↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion

Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.

Laser Powder Bed Fusion↗
DOE OSTI2025

Exploring the interplay between distributed wind generators and solar photovoltaic systems

This study investigates the spatial and temporal dynamics of wind and solar energy generation across the continental United States, focusing on energy availability, reliability, variability, and cooperation. Using data from the National Renewable Energy Laboratory, we analyze the performance of wind turbines and photovoltaic systems, revealing distinct patterns in energy production and reliability. The classification of wind and solar zones based on energy availability and reliability provides valuable insights for renewable energy planning and grid integration strategies. Overall, this methodology contributes to understanding wind and solar interactions, which will lead to informing effective renewable energy deployment and grid integration efforts.

14 SOLAR ENERGY↗
DOE OSTI2025

Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Ellipsoidal Fitting Methodology for Defect Clusters in Gallium Arsenide

In assessing the initial spatial distribution of defects from neutron or heavy ion irradiation, it is useful to have a reliable, automated, and fast-running tool to evaluate characteristic metrics such as the number of sub-clusters or the overall cluster volume. The latter metric, for instance, can be utilized to estimate a reference neutron fluence level at which inter-cluster interaction effects begin to become significant. This paper details a methodology to fit an arbitrarily complex defect map with a set of ellipsoids (one per identified sub-cluster) in which the constituent defects of a sub-cluster are determined using fuzzy degree-of-membership analysis. Specifically, a parameterized model is developed for point defects in gallium arsenide. Cluster volume calculations based on the model are compared against convex hull and single- ellipsoid representations. Results show that the parameterized sub-cluster model begins to deviate from the two reference models at a recoil energy of about 100 keV in GaAs, with the convex hull and single-ellipsoid representations increasingly overestimating the volume thereafter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Space shuttle program: Shuttle Avionics Integration Laboratory. Volume 7: Logistics management plan

The logistics management plan for the shuttle avionics integration laboratory defines the organization, disciplines, and methodology for managing and controlling logistics support. Those elements requiring management include maintainability and reliability, maintenance planning, support and test equipment, supply support, transportation and handling, technical data, facilities, personnel and training, funding, and management data.

Source record↗
NASA NTRS1976

Standardized development of computer software. Part 1: Methods

This work is a two-volume set on standards for modern software engineering methodology. This volume presents a tutorial and practical guide to the efficient development of reliable computer software, a unified and coordinated discipline for design, coding, testing, documentation, and project organization and management. The aim of the monograph is to provide formal disciplines for increasing the probability of securing software that is characterized by high degrees of initial correctness, readability, and maintainability, and to promote practices which aid in the consistent and orderly development of a total software system within schedule and budgetary constraints. These disciplines are set forth as a set of rules to be applied during software development to drastically reduce the time traditionally spent in debugging, to increase documentation quality, to foster understandability among those who must come in contact with it, and to facilitate operations and alterations of the program as requirements on the program environment change.

Tausworthe, R. C.↗
NASA NTRS1984

Evaluation methodologies for an advanced information processing system

The system concept and requirements for an Advanced Information Processing System (AIPS) are briefly described, but the emphasis of this paper is on the evaluation methodologies being developed and utilized in the AIPS program. The evaluation tasks include hardware reliability, maintainability and availability, software reliability, performance, and performability. Hardware RMA and software reliability are addressed with Markov modeling techniques. The performance analysis for AIPS is based on queueing theory. Performability is a measure of merit which combines system reliability and performance measures. The probability laws of the performance measures are obtained from the Markov reliability models. Scalar functions of this law such as the mean and variance provide measures of merit in the AIPS performability evaluations.

Schabowsky, R. S., Jr.↗