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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 National Household Travel Survey - Oahu Add-On

# 2022 National Household Travel Survey – Oahu Add-On The Oahu add-on survey supplements the 2022 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Survey Methodology The 2022 NHTS, which covered assigned travel dates from January 2022 to January 2023, collected data on the demographic and socioeconomic composition of households as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies—like the Oahu Metropolitan Planning Organization—had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. ## Survey Records, Data, and Documentation Survey records include 7,397 participants from 3,170 households in Oahu, Hawaii, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 14,868 trips totaling 165,000 vehicle miles traveled.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Initial Characterization of the NREL Large-Amplitude Motion Platform

The Large Amplitude Motion Platform (LAMP) at NREL represents a significant advancement in the controlled testing of Wave Energy Converters (WECs) under laboratory conditions. Originally designed by E2M as a six-degree-of-freedom (DOF) Stewart platform for flight simulation, LAMP has been adapted by NREL to facilitate the mounting and evaluation of WECs. This adaptation enables dry testing of WECs using motion profiles similar to the ocean, facilitating the iterative design, testing, and validation of WEC performance prior to ocean deployments. This report presents the initial work completed to characterize LAMP, with particular emphasis on its stability and operational capabilities across various single and multi-degree-of-freedom (DOF) motion profiles. The report includes planned comparisons at three distinct mass payloads, aimed at assessing the platform's positional accuracy, frequency response, and endurance over extended runtime periods. These experimental tests are critical for establishing the platform's limitations and ensuring that the data generated during WEC validation is both accurate and reproducible. The outcomes of this study not only contribute to a deeper understanding of LAMP's capabilities but also lay the groundwork for future advancements in WEC testing methodologies. By providing robust and reliable performance data within a controlled laboratory setting, the findings are expected to significantly enhance the development and commercialization of marine energy technologies. This report presents the initial findings of the LAMP Characterization work and proposed steps to further understand and characterize LAMP. Data collected during this work can be found on MHKDR at: https://mhkdr.openei.org/submissions/602 Note that this report shares the measured/found instantaneous maximum operating range of LAMP. For most applications the maximum operating range cannot be used for system health and longevity. The operating range and capabilities of LAMP will be evaluated on a case-by-case basis, single-DOF position, velocity, and acceleration values presented in Table 4a-c, and Table 5 should be taken as instantaneous absolute maximum values. Future use of LAMP will likely be limited to smaller values.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Prediction of Reactive Nitrous Acid Formation in Rare‐Earth MOFs via ab initio Molecular Dynamics

Abstract Reactive gas formation in pores of metal–organic frameworks (MOFs) is a known mechanism of framework destruction; understanding those mechanisms for future durability design is key to next generation adsorbents. Herein, an extensive set of ab initio molecular dynamics (AIMD) simulations are used for the first time to predict competitive adsorption of mixed acid gases (NO 2 and H 2 O) and the in‐pore reaction mechanisms for a series of rare earth (RE)‐DOBDC MOFs. Spontaneous formation of nitrous acid (HONO) is identified as a result of deprotonation of the MOF organic linker, DOBDC. The unique DOBDC coordination to the metal clusters allows for proton transfer from the linker to the NO 2 without the presence of H 2 O and may be a factor in DOBDC MOF durability. This is a previously unreported mechanisms of HONO formation in MOFs. With the presented methodology, prediction of future gas interactions in new nanoporous materials can be achieved.

Vogel, Dayton J.↗
DOE OSTI2021

Prediction of Reactive Nitrous Acid Formation in Rare‐Earth MOFs via ab initio Molecular Dynamics

Abstract Reactive gas formation in pores of metal–organic frameworks (MOFs) is a known mechanism of framework destruction; understanding those mechanisms for future durability design is key to next generation adsorbents. Herein, an extensive set of ab initio molecular dynamics (AIMD) simulations are used for the first time to predict competitive adsorption of mixed acid gases (NO 2 and H 2 O) and the in‐pore reaction mechanisms for a series of rare earth (RE)‐DOBDC MOFs. Spontaneous formation of nitrous acid (HONO) is identified as a result of deprotonation of the MOF organic linker, DOBDC. The unique DOBDC coordination to the metal clusters allows for proton transfer from the linker to the NO 2 without the presence of H 2 O and may be a factor in DOBDC MOF durability. This is a previously unreported mechanisms of HONO formation in MOFs. With the presented methodology, prediction of future gas interactions in new nanoporous materials can be achieved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Engineering evaluation of the EQSIM simulated ground-motion database: The San Francisco Bay Area region

Ground-motion simulations for infrequent earthquake scenarios are gaining increasing interest in the engineering community for their potential to inform performance-based structural design and assessment, particularly in regions where major earthquakes are expected, but recordings from consistent historical events are not available. However, the absence of empirical data makes the evaluation of such scenarios and the interpretation of the simulation results very challenging. In this context, this paper presents the evaluation of the first EQSIM (v.1.1.0) ground-motion simulations generated for the San Francisco Bay Area (SFBA) region. The current database, which is at the first stages of development, includes eight realizations of a Hayward Fault M w 7 event. The evaluation of the simulated motions is first conducted on the average ground-motion amplitudes through comparison against the NGA-W2 GMPEs and a selected population of real records. A methodology for selecting a population of real records consistent with the simulated event is proposed. The objective of the proposed evaluation is twofold: (1) build confidence in the realistic character of the simulated motions for their use in engineering applications and (2) offer a critical physics-based interpretation of the simulation results that can help improve key features of the simulation models. To further investigate the implications of using the simulated ground motions for site-specific structural assessments, the ASCE7-16 approach is employed for the analysis of two hazard-consistent near-field sites in the SFBA. Results are discussed. Furthermore, this study offers a critical review of the EQSIM (v.1.1.0) SFBA ground-motion simulations and suggestions for improving the earthquake rupture models.

58 GEOSCIENCES↗
DOE OSTI2020

Direct Nano‐Synthesis Methods Notably Benefit Mg‐Battery Cathode Performance

Abstract Rechargeable magnesium batteries are promising candidates for next‐generation electrochemical energy storage, but their development is severely hindered by sluggish solid‐state diffusion and significant desolvation penalties of the divalent cation. Studies suggest that nano‐sized electrode materials alleviate these issues by shortening diffusion lengths and increasing electrode/electrolyte interaction. Here, the effect of particle size and synthetic methodology on the electrochemical performance of four sulfide cathode materials in Mg batteries is investigated: layered TiS 2 , CuS, spinel Ti 2 S 4 , and CuCo 2 S 4 . In these sulfide hosts, the direct preparation of nano‐dimensional crystallites is critical to activate or improve electrochemistry. Even promising cathode materials can appear electrochemically inert when micron‐sized particles are investigated (e.g., CuCo 2 S 4 ), and mechanical milling leads to surface degradation of active material which severely limits performance. However, nano‐sized CuCo 2 S 4 prepared directly reaches a capacity nearly double that of ball‐milled material and delivers 350 mAh g −1 at 60 °C. This work provides synthetic considerations which may be crucial in the discovery and design of novel Mg cathode materials, so that promising candidates are not overlooked. By extension, in oxide materials where Mg 2+ diffusion is expected to be much more sluggish, this factor is anticipated to be even more important when screening for new hosts.

Blanc, Lauren E.↗
DOE OSTI2025

Cross-scale modeling and experimental integration for advancing cathode electrolyte interphase studies in high energy density lithium-ion batteries

Electrochemical interfaces are critical to the performance and durability of lithium-ion batteries (LIBs). The solid electrode-electrolyte interphase (SEI and CEI) structures that form during cycling can passivate reactive surfaces, ensuring safe operation, but also may contribute to performance degradation. Understanding the microscopic factors influencing interphase formation, growth, and evolution is essential for balanced battery design. While significant research has focused on the anode-electrolyte interphase (SEI), the cathode-electrolyte interphase (CEI) remains less explored, despite its importance in high-voltage and advanced battery technologies. Challenges in conducting in-situ or operando experiments arise from the occluded nature of these interfaces and the long timescales involved, often leading to biased interpretations. A validated multi-scale, multi-physics modeling approach, integrated with advanced characterization techniques, can effectively elucidate the intrinsic stability of electrolyte and cathode surfaces, the impact of chemical heterogeneity, and the role of microstructural features on CEI performance. In conclusion, this article reviews current modeling and simulation strategies for studying CEI in advanced LIBs and highlights opportunities for future methodological advancements and experimental integration.

Cathode-electrolyte interphase↗
DOE OSTI2021

Energy Efficiency and Performance Evaluation of Self-Contained, Medium Temperature, Reach-In Refrigerated Display Cases

This project was part of an effort by Commonwealth Edison Company (ComEd) to evaluate the energy and peak demand saving potential of emerging technologies in the Chicago area. This document focuses on the assessment of energy-efficient, medium-temperature, self-contained refrigerated display cases utilizing environmentally friendly refrigerants. The results of this evaluation will be considered by CLEAResult to develop a new energy efficiency rebate measure for ComEd's incentive programs. This rebate measure will become an addition to the Technical Reference Manual (TRM). In 2016, the United Nations passed the Kigali Montreal Protocol Amendment which placed restrictions on certain types of refrigerants. In compliance with this amendment, the US Environmental Protection Agency (EPA) placed a ban on the manufacture of refrigeration systems using hydrofluorocarbons including R134a starting in January 2020. Although the ban has halted manufacture, the EPA continues to allow the use of these refrigerants. Therefore, it is critical to provide incentives for replacing these refrigerants with other environmentally friendly and energy-efficient alternatives. The energy-efficient refrigerator cases evaluated here (referred to as EE Case A and B) contain environmentally-friendly refrigerants in compliance with the EPA hydrofluorocarbon ban. These consist of natural refrigerant propane (R290), and HFC drop-in hydrofluoroolefin R513a, respectively. These cases also contain other energy-efficient components including efficient lighting, more robust evaporator and condenser fans and different-sized heat exchangers. EE Case A is also built with materials that better insulate the case, which improves energy efficiency. To ascertain the energy efficiency contribution of these design components, the consumption of the evaporator and condenser fan motors, compressor, and lighting/controller were evaluated individually. The medium-temperature, self-contained reach-in refrigerated display case was selected due to its widespread use in convenience stores and small supermarkets. Self-contained refrigeration has also seen increased use in restaurants due to curbside pickup during the COVID-19 pandemic. For this study, the refrigerated display cases' performance was evaluated in a controlled environmental chamber at representative indoor dry-bulb and humidity conditions found in supermarkets within ComEd's service territory climate zone. The test method used in this project was foundationally inspired by the ANSI/ASHRAE 72-2018 method of testing. However, modifications to the ANSI/ASHRAE test methodology were implemented to better represent customer operation of the units. In addition to the indoor supermarket conditions, the cases were also evaluated at the "upper target," or environmental conditions used in ANSI/ASHRAE 72-2018. The case total power and case components were metered to obtain their daily power (kW) and energy consumption(kWh). The cases were filled with thermal filler mass to replicate thermal mass of product loading. Additionally, product simulators were used to provide product temperature information. Door actuators were mounted to each of the cases' three doors to replicate regular door openings and effects of shopper traffic.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
DOE OSTI2022

Kinetics-Informed Neural Networks

Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions to solve ordinary differential equations (ODEs) constrained by differential algebraic equations (DAEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multi-objective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We analyze a set of scenarios to establish the extent to which kinetic parameters can be retrieved from transient kinetic data, and assess the robustness of the methodology with respect to statistical noise. Furthermore, this approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spike-Free Adaptive Sliding Mode Control: Application to Permanent Magnet Synchronous Motors

A new methodology for adaptive sliding mode control (ASMC) has been widely used to improve the control performance in various systems. This method exhibits several advantages, including low sliding mode control (SMC) chattering, no knowledge of the system disturbance bound, and no overestimation of the control gain. Despite its advantages, this method can be hampered by the spike phenomenon, slow control gain convergence, and difficulty in achieving optimal performance under varying disturbances. Consequently, this article proposes a spike-free ASMC method with a disturbance observer (DOB) to address these problems. Previous ASMC methods have been analyzed via simulations to verify the aforementioned problems. Here, this analysis highlights the need for disturbance compensation and improvements in the SMC gain adaptation law. Therefore, a DOB is designed to mitigate the spike phenomenon by compensating for disturbances. Subsequently, an SMC gain adaptation law based on disturbance error estimation is designed to eliminate the spike phenomenon completely. The proposed adaptation law makes the SMC gain to converge to a slightly higher value than the disturbance estimation error. Consequently, the proposed method not only eliminates the spike phenomenon, but also ensures optimal performance under varying disturbances. The performance of the proposed method is experimen tally validated through a comparative study.

42 ENGINEERING↗
DOE OSTI2020

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Science & Technology - Other Topics↗
DOE OSTI2023

Reducing microgrid availability to reduce costs for coastal Puerto Rican communities

Renewable microgrids are sustainable, resilient solutions to mitigate and adapt to climate change. Making electric loads nearly 100% available (i.e., power remains on) during outages increases cost. Near 100% availability is required when human life or high-cost assets are involved, but availability can be reduced for less consequential loads leading to lower costs. This study analyses costs for photo-voltaic and lithium-ion battery microgrids with availability ranging from 0–99%. We develop a methodology to analyse three Puerto Rican coastal communities. Here, we consider power outage effects for hurricanes, earthquakes, and everyday outages. The results show cost versus availability from 0–99%. There is 27–31% cost reduction at 80% availability in comparison to 99% availability. A regression model of microgrid availability versus three ratios: 1) the annual generation to demand ratio, 2) storage to interruption energy ratio, and 3) peak storage to load ratio produced a coefficient of determination of 0.99949 with 70% of the data used for training and 30% for testing. The results can therefore be extended to other coastal Puerto Rican communities of varying sizes that have ratios within the ranges analysed in this study. This can empower decision makers to rapidly analyse designs that have availabilities well below 100%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Analytical Large-Signal Modeling of Inverter-Based Microgrids With Koopman Operator Theory for Autonomous Control

The microgrid (MG) plays a crucial role in the energy transition, but its nonlinearity presents a significant challenge for large-signal power systems studies in the electromagnetic transient (EMT) time scale. In this paper, we develop a large-signal linear MG model that considers the detailed dynamics of the primary and zero-control levels based on the Koopman operator (KO) theory. Firstly, a set of observable functions is carefully designed to capture the nonlinear dynamics of the MG. The corresponding linear KO is then analytically derived based on these observables, resulting in the linear representation of the original nonlinear MG with observables as the new coordinate. The influence of external input on the system dynamics is also considered during the derivation, enabling control of the MG. We solve the voltage control problem using the traditional linear quadratic integrator (LQI) method to demonstrate that textbook linear control techniques can accurately control the original nonlinear MG via the developed KO-linearized MG model. Our proposed KO linearization method is generic and can be easily extended for different control objectives and MG structures using our analytical derivation procedure. Here, we validate the effectiveness of our methodology through various case studies.

25 ENERGY STORAGE↗