Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Reliability Specifications”

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.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Rooftop Unit (RTU) Maintenance Guide

The commercial buildings industry has long emphasized affordability, energy efficiency, and useful service life in rooftop unit (RTU) equipment. The trade-offs between these characteristics can impact decisions about the initial cost of equipment and ongoing operating costs. Often, the desire for lower initial costs leads to adoption of low-efficiency systems or neglect of proper maintenance in the interest of short-term cost savings. Maintenance of these systems is a critical step to ensure system efficiency and reliability, low operating costs, and a reasonable payback period. This guide is intended for facility managers who own or are interested in purchasing and installing RTUs and need to familiarize themselves with the maintenance requirements. This resource contains best practices, important considerations, and additional information related to commercial RTU equipment, but does not replace manufacturer-specific instructions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Benchmark Specifications for Select Experiments Conducted at the Kansas State University Gallium Thermal-hydraulic Experiment Facility

The Department of Energy (DOE) – Nuclear Energy University Programs (NEUP) supported the creation and operation of the Gallium Thermal-hydraulic Experiment (GaTE) facility at Kansas State University (KSU) as part of a larger effort to understand thermal stratification behavior in liquid-metal-cooled reactors. GaTE was designed to simulate transients in a reactor plenum that are known to cause thermal stratification. High-reliability and high-resolution measurements describing stratification behavior in the coolant were collected for use as experimental benchmarks in validation efforts for computational models. The results of these tests contribute to a greater understanding of thermal stratification behavior of liquid metal under various configurations and operating conditions. This report provides a complete description of the benchmark problem, including all necessary details and description of a set of four forced flow and four natural circulation tests and measured data for comparison with model results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Irradiation-Induced Structural Disorder and Its Influence on the Mechanical Response of Polycrystalline MoS2

Molybdenum disulfide (MoS2) thin films are widely used as dry-film lubricants and protective coatings in aerospace and other radiation-exposed environments. Conventional synthesis routes produce polycrystalline films whose grain boundaries and other native defects cause their mechanical and tribological behavior to differ substantially from that of ideal single crystals. Under irradiation, these films progressively evolve from polycrystalline structures, composed of layered MoS2 grains, into highly disordered and eventually amorphous structures, altering both their tribological performance and mechanical integrity. Here, we employ reactive atomistic simulations to investigate irradiation-driven structural evolution in bulk polycrystalline MoS2. Using controlled primary knock-on atom (PKA) events, we characterize the progressive transition from a polycrystalline microstructure to an amorphous network by tracking defect accumulation and structural disorder. We then establish how this transition modifies the dominant deformation mechanisms and the temperature-dependent tensile response. Specifically, irradiation suppresses interlayer sliding and delamination, mechanisms which facilitate the deformation of the pristine polycrystal, resulting in defect-induced hardening. Broadly, our results establish direct process–structure–property relationships linking irradiation-induced defect accumulation, microstructural evolution, deformation mechanisms, and mechanical behavior, providing an atomistic framework for understanding the structural integrity and long-term reliability of irradiated MoS2 coatings.

Moore, Daniel [Sandia National Laboratories (SNL)]↗
DOE OSTI2023

Charge transport in mixed metal halide perovskite semiconductors

Investigation of the inherent field-driven charge transport behaviour of three-dimensional lead halide perovskites has largely remained challenging, owing to undesirable ionic migration effects near room temperature and dipolar disorder instabilities prevalent specifically in methylammonium-and-lead-based high-performing three-dimensional perovskite compositions. Here, in this paper, we address both these challenges and demonstrate that field-effect transistors based on methylammonium-free, mixed metal (Pb/Sn) perovskite compositions do not suffer from ion migration effects as notably as their pure-Pb counterparts and reliably exhibit hysteresis-free p-type transport with a mobility reaching 5.4 cm 2 V –1 s -1 . The reduced ion migration is visualized through photoluminescence microscopy under bias and is manifested as an activated temperature dependence of the field-effect mobility with a low activation energy (~48 meV) consistent with the presence of the shallow defects present in these materials. An understanding of the long-range electronic charge transport in these inherently doped mixed metal halide perovskites will contribute immensely towards high-performance optoelectronic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Power System Resilience Evaluation Framework and Metric Review

Power system resilience has been an emerging hot topic in recent years to investigate the increasing threats of extreme events, such as natural disasters, severe weather, and cyberattacks. Although much research has been done to define, model, and quantify resilience from different aspects, the lack of universally accepted evaluation methods and resilience metrics makes it difficult to assess and compare resilience across different power systems, such as what is typically done in power system reliability studies. In this paper, first, we review the definitions of resilience, and we summarize two core concepts shared by most of the literature. Then, we develop a new framework to assess power system resilience from two perspectives - i.e., pre-event estimation and post-event evaluation - to capture system resilience performance in both general and specific fashions. We conduct a thorough review of existing resilience metrics and categorize them using the proposed framework, where recommendations are also proposed to capture core concepts of resilience.

power system resilience↗
DOE OSTI2023

Single-Phase Jet Impingement Cooling for a Power-Dense Silicon Carbide Power Module

The adoption of silicon carbide (SiC) devices in the electric vehicle (EV) industry is increasing due to their superior performance over silicon devices. SiC devices enable further miniaturization of EV inverters, increasing their power density, which results in thermal management challenges. Here in this paper, the limits of single-phase jet impingement cooling are explored for an automotive SiC power module. We propose embedding pin fins in the direct-bonded-copper (DBC) substrate of the power module package using laser powder bed fusion additive manufacturing. The thermalhydraulic performance of the DBC-embedded pin fins is compared against folded fins that are directly soldered to the DBC substrate. A heat conduction analysis was conducted on a SiC package to determine the target heat transfer coefficient (HTC) for the heat sink. A water-ethylene glycol (WEG) jet impingement on the proposed concepts was studied using unit-cell models to achieve the target HTC. The studied designs put emphasis on the reliability and manufacturability requirements of the automotive industry. The thermal performance of DBC-embedded pin fins outperformed the DBC-soldered folded fins. The performance of the DBC-embedded pin fins is benchmarked against WEG-based cooling systems of commercial EVs. With the proposed cooling solution, we have shown a pathway of reducing the specific thermal resistance by 75% compared to BMW i3 thermal management system without any penalty on pressure drop or parasitic power.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

The rapid integration of variable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process (e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the CAISO system. Furthermore, results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data-Driven Probabilistic Anomaly Detection for Electricity Market under Cyber Attacks

Information and communication technologies have been widely used in smart grid for efficient operation. However, these technologies are vulnerable to malicious cyber attacks, which may lead to severe reliability and economic issues. Recently, a variety of data-driven anomaly detection approaches have been explored to detect potential cyber attacks in smart grids. In this paper, we researched on the electricity market data aiming to identify anomalies from the locational marginal prices (LMPs) and provide a new indicator for potential cyber attacks in power grids. Specifically, a novel data-driven probabilistic anomaly detection framework is proposed for electricity market, which consists of three major components: long short-term memory (LSTM) based deterministic electricity price forecasting, probabilistic electricity price forecasting and anomaly detection. This framework is tested on a model-based electricity market simulator under two types of cyber attacks, i.e., load redistribution attack (LRA) and price responsive attack (PRA). Numerical results on the simulated LMPs show that the proposed framework is capable of detecting data anomalies over these attacks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting

Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ~8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Benchmark Specification for Select Experiments Conducted at the University of Wisconsin-Madison Thermal Stratification Test Facility

The Department of Energy (DOE)-Nuclear Energy University Programs (NEUP) supported the creation and operation of the Thermal Stratification Test Facility (TSTF) at the University of Wisconsin Madison (UWM) as part of a larger effort to understand thermal stratification behavior in liquid-metal-cooled reactors. The TSTF was designed to simulate transients in a reactor plenum that are known to cause thermal stratification. High-reliability and high-resolution measurements of the flow and temperature were collected for use as experimental benchmarks to support validation efforts for computational models. The results of these tests contribute to the greater understanding of thermal stratification behavior of liquid sodium under various configurations and operating conditions. The six TSTF tests selected for benchmarking are a set of forced circulation tests at a fixed flow rate with different Upper Internal Structure (UIS) configurations in the test section (no UIS, solid UIS, and a UIS with flow area of 4, 8, 12, and 100%). This report provides a complete description of the benchmark problems, including all key test facility details, descriptions of each test condition, and measured data for comparison with modeled results.

42 ENGINEERING↗
DOE OSTI2023

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (Φ PSIImax ), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R 2 = 0.86, RMSE=1.99 μmol m -2 s -1 ) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. Further, the seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Φ PSIImax . These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in Φ PSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

StressNet - Deep learning to predict stress with fracture propagation in brittle materials

Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring Dynamic P-Q Capability and Abnormal Operations of Inverter-Based Resources

Nowadays, inverter-based resources are rapidly growing in electric power systems. On the other side, abnormal inverter operations are also occurring more often. Traditionally, inverter PQ capability charts are employed by electric utility companies to ensure safe and reliable operations of inverter-based resources. However, it is found in this paper that existing inverter PQ capability charts do not address the operating characteristics of an inverter correctly, which can result in irregular and unstable inverter operations. Here in this paper, a novel study is conducted to investigate inverter PQ capability charts, where constraints that are specific to an inverter are considered. It is found in this paper that the inverter PQ capability charts exhibit a unique dynamic nature under uncertain grid conditions. This dynamic nature has not been considered by the industry in managing, designing, and controlling an inverter as well as in developing international standards for inverter-based resources. Via both electromagnetic transient simulation and hardware experimental investigations, it is found that the dynamic PQ capability nature, together with the limitations of the existing control methods, are the underlying causes of many unusual inverter operations that have been reported in the literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗