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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data for Chemical-Free Production of Multiple High-Value Bioproducts from Metabolically Engineered Transgenic Sugarcane ‘Oilcane’ Bagasse and Their Recovery Using Nanofiltration

Bioenergy crops have potential for being a sustainable and renewable feedstock for biofuels and various value-added bioproducts. The study utilizes recently developed transgenic sugarcane (“oilcane”) bagasse for chemical-free coproduction of high-value bioproducts, i.e., furfurals, HMF, acetic acid, cellulosic sugars, and vegetative lipids. Hydrothermal pretreatment was optimized at 210 °C for 5 min to coproduce 6.91%, 2.67%, 5.07%, 2.42% and 37.82% (w/w) furfurals, HMF, acetic acid, vegetative lipids, and cellulosic sugars, respectively from lignocellulosic biomass. Additionally, nanofiltration system in-series was successfully established to recover sugars, furfurals, HMF, and acetic acid from the pretreatment liquor. 1st nanofiltration with Duracid NF membrane rejected ∼99% sugars. Concentrated sugars with significantly reduced inhibitory products were obtained in retentate for fermentation. 2nd nanofiltration with NF90 membrane used permeate of 1st nanofiltration as feed and rejected ∼ 86% furfurals. The work demonstrates the feasibility of coproducing and recovering multiple biochemicals from lignocellulosic biomass.

Biomass Analytics↗
DOE OSTI2022

MENUS—Materials engineering by neutron scattering

Materials engineering by neutron scattering (MENUS) at the second target station will be a transformational high-flux, versatile, multiscale materials engineering diffraction beamline with unprecedented new capabilities for the study of complex materials and structures. It will support both fundamental and applied materials research in a broad range of fields. MENUS will combine unprecedented long-wavelength neutron flux and unique detector coverage to enable real-time studies of complex structural and functional materials under external stimuli. The incorporated small angle neutron scattering and transmission/imaging capabilities will extend its sensitivity to larger length scales and higher spatial resolution. Multimodal MENUS will provide crystallographic and microstructure data to the materials science and engineering community to understand lattice strain/phase transition/microstructure/texture evolution in three orthogonal directions in complex material systems under combined extreme applied conditions. Finally, the capabilities of MENUS will open new scientific opportunities and meet the research needs for science challenges to enable studies of a range of phenomena and answer the key questions in material design/exploration, advanced material processing, transformative manufacturing, and material operations of national impacts in our daily life.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Methods for the Automated Determination of Sustained Maximum Amplitudes in Oscillating Signals

Machine condition monitoring has been proven to reduce machine downtime and increase productivity. The state-of-the-art research uses vibration monitoring for tasks such as maintenance and tool wear prediction. A less explored aspect is how vibration monitoring might be used to monitor equipment sensitive to vibration. In a manufacturing environment, one example of where this might be needed is in monitoring the vibration of optical linear encoders used in high-precision machine tools and coordinate measuring machines. Monitoring the vibration of sensitive equipment presents a unique case for vibration monitoring because an accurate calculation of the maximum sustained vibration is needed, as opposed to extracting trends from the data. To do this, techniques for determining sustained peaks in vibration signals are needed. Here, this work fills this gap by formalizing and testing methods for determining sustained vibration amplitudes. The methods are tested on simulated signals based on experimental data. Results show that processing the signal directly with the novel Expire Timer method produces the smallest amounts of error on average under various test conditions. Additionally, this method can operate in real-time on streaming vibration data.

Industrial Internet of Things↗
DOE OSTI2023

Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder

Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold trap to prevent corrosion and obstructing small orifices. We have developed a deep learning long short-term memory (LSTM) autoencoder for continuous monitoring of a cold trap and detection of operational anomaly. Transient data were obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The cold trap purification at METL is monitored with 31 variables, which are sensors measuring fluid temperatures, pressures and flow rates, and controller signals. Loss-of-coolant type anomaly in the cold trap operation was generated by temporarily choking one of the blowers, which resulted in temperature and flow rate spikes. The input layer of the autoencoder consisted of all the variables involved in monitoring the cold trap. The LSTM autoencoder was trained on the data corresponding to cold trap startup and normal operation regime, with the loss function calculated as the mean absolute error (MAE). The loss during training was determined to follow log-normal density distribution. During monitoring, we investigated a performance of the LSTM autoencoder for different loss threshold values, set at a progressively increasing number of standard deviations from the mean. The anomaly signal in the data was gradually attenuated, while preserving the noise of the original time series, so that the signal-to-noise ratio (SNR) averaged across all sensors decreased below unity. Results demonstrate detection of anomalies with sensor-averaged SNR < 1.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A Deterministic Multivariate Clustering Method for Drive Cycle Generation from In-Use Vehicle Data

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data. This paper explores the development and initial validation of a method of generating representative drive cycles from large collections of real-world vehicle data using a deterministic multivariate clustering approach. Starting with theory and diving into the methodology behind representative cycle generation, the paper aims to also present graphical and tabular results of initial validation via vehicle simulation and chassis dynamometer testing. Additional topics for further research and areas for ongoing development will also be presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Cybersecurity of Wide Area Monitoring, Protection and Control Systems for HVDC Applications

The flexibility provided by High Voltage Direct Current (HVDC) systems can be further extended by Wide Area Monitoring, Protection, and Control (WAMAPC) systems. WAMPAC systems enable many HVDC applications and, on the other hand, inevitably introduce cybersecurity concerns that need to be addressed. In this work, a security domain layer and decision framework is reported to detect and mitigate the impact of false data injection (FDI) attacks targeting HVDC stations. Specifically, a rule-based cyber-attack detection method is introduced and implemented on Raspberry Pi and tested on the real-time HVDC simulation facility on the real-time digital simulator (RTDS) platform at ABB U.S. Corporate Research Center.

cybersecurity↗
DOE OSTI2021

Driving magnetic domains at the nanoscale by interfacial strain-induced proximity

Here, we investigate the local nanoscale changes of the magnetic anisotropy of a Ni film subject to an inverse magnetostrictive effect by proximity to a V 2 O 3 layer. Using temperature-dependent photoemission electron microscopy (PEEM) combined with X-ray magnetic circular dichroism (XMCD), direct images of the Ni spin alignment across the first-order structural phase transition (SPT) of V 2 O 3 were obtained. We find an abrupt temperature-driven reorientation of the Ni magnetic domains across the SPT, which is associated with a large increase of the coercive field. Moreover, angular dependent ferromagnetic resonance (FMR) shows a remarkable change in the magnetic anisotropy of the Ni film across the SPT of V 2 O 3 . Micromagnetic simulations based on these results are in quantitative agreement with the PEEM data. Direct measurements of the lateral correlation length of the Ni domains from XMCD images show an increase of almost one order of magnitude at the SPT compared to room temperature, as well as a broad spatial distribution of the local transition temperatures, thus corroborating the phase coexistence of Ni anisotropies caused by the V 2 O 3 SPT. We show that the rearrangement of the Ni domains is due to strain induced by the oxide layers’ structural domains across the SPT. Our results illustrate the use of alternative hybrid systems to manipulate magnetic domains at the nanoscale, which allows for engineering of coercive fields for novel data storage architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fast Response Temperature by airborne measurements over BNF

The original data were collected during the AAF Engineering Flights (AEF2025) in the vicinity of the ARM Bankhead National Forest (BNF) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/bnf ) in northwestern Alabama in March 2025. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at the public-use airport of Posey Field, Alabama (FAA LID: 1M4, 34.28027778° N, 87.60055556° W, 283m MSL) from March 9 through March 24, 2025. The ArcticShark UAS performed nine flights, including eight research flights over the AMF3 (BNF Main Site) and Supplemental Facilities to measure atmospheric state, turbulence, surface IR temperature and imagery, and aerosol number concentration and size distribution. The current data set presents fast response temperature in the atmospheric boundary layer and lower free troposphere measured on the airborne platform throughout the field campaign. The primary instruments used to create the current data set were the fine wire thermocouple probe, the Aircraft Integrated Meteorological Measurement System (AIMMS-30), the Pitot-static system (part of UAS flight control), and the infrared gas analyzer sensor for H2O and CO2 (LI-840). All parameters used in temperature calculations (static pressure, True Air Speed, and absolute humidity in form of dew point temperature) were included in the data set. For user convenience, one additional parameter was also included: the type of flight flag (level, up, down, turn, and combination of thereof).

Air temperature, fast response↗
DOE OSTI2025

Turbulent Parameters by airborne measurements over BNF in March 2025

The original data were collected during the AAF Engineering Flights (AEF2025) in the vicinity of the ARM Bankhead National Forest (BNF) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/bnf ) in northwestern Alabama in March 2025. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at the public-use airport of Posey Field, Alabama (FAA LID: 1M4, 34.28027778° N, 87.60055556° W, 283m MSL) from March 10 through March 24, 2025. The ArcticShark UAS performed nine flights, including eight research flights over the AMF3 (BNF Main Site) and Supplemental Facilities to measure atmospheric state, turbulence, surface IR temperature and imagery, and aerosol number concentration and size distribution. The current data set presents a collection of turbulent parameters in the atmospheric boundary layer or lower free troposphere based on airborne measurement throughout the field campaign. The primary instruments used to create the current data set were the Aircraft Integrated Meteorological Measurement System (AIMMS-30) and the fine-wire thermocouple probe.

Atmosphere↗
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗
DOE OSTI2023

Design and requirements of a hydrogen component reliability database (HyCReD)

Hydrogen technologies are expected to play a key role in the decarbonization of several sectors including energy storage and transportation. Rigorous investigation and quantification of the risk and reliability issues associated with hydrogen technologies will be critical to ensuring both their wider adoption and safe, economical operations. Quantitative risk assessment (QRA) is an important tool that has been used to enable the safe deployment of many engineering systems, including hydrogen fueling stations and hydrogen storage systems. However, QRA studies require reliability data which is currently lacking for expanding applications of hydrogen systems. Here, to address this gap, we present a new structure for a hydrogen component reliability database (HyCReD) that can be used to generate reliability data to be used in QRA, reliability, safety studies, maintenance planning, and more. Building on our previous work examining four major hydrogen safety data collection tools (West et al., 2022) [1], our approach in this work was to consult scientific literature on reliability data collection as well as a number of existing reliability engineering databases in the oil & gas, chemical processing, and nuclear power plant sectors. The evaluation of these databases led to identifying best practices to be implemented in a data collection framework for a hydrogen component reliability database. Based on these best practices, a set of 24 requirements for the proposed database are presented, covering its characteristics and the types of data to be collected. We define the structure of the HyCReD database and 25 data elements to be collected, spanning system description, failure, shutdown, or near-miss events, and maintenance events. The data elements are then defined according to international standards used in the safety and reliability practice and potential choice lists are provided for each field. Since this database is being piloted for hydrogen fueling stations, a generic station component hierarchy developed by West (2021) [2] is used to standardize system data. Finally, we demonstrate populating the database with information extracted from five narrative reports on hydrogen fueling station incidents.

08 HYDROGEN↗
DOE OSTI2024

Improving the User Interface of the DeepLynx Data Warehouse

DeepLynx is an open-source ontology-based data warehouse created by INL to support the creation and life cycle of digital engineering projects, with a particular emphasis on digital twins [1]. Digital twins are systems that represent physical assets and process in a real-time digital environment [1]. Most well-known commercial data warehouses use Graphical User Interfaces (GUIs) for users to interact with their systems [3]. Limited publications have addressed the design of these interfaces and understanding of their target users. The current users and development team acknowledge the need to improve the current UI, not just for aesthetics but to improve functionality and workflow of DeepLynx. Traditional data warehouse users are developers, data scientists and business analysts [2]. DeepLynx users have a vast range of experience using data warehouses, and diverse roles, including engineers, scientists and management positions. Because there is a broader audience of target users for DeepLynx than a typical data warehouse, it is essential that DeepLynx has a useable and intuitive user interface. To achieve this the team performed human-computer interaction methods, including a Heuristic Evaluation of current UI using Neilsen’s Usability Heuristic, create personas based on current users by designing a user survey, data analysis and develop of personas. Followed by a redesign of the UI following using Neilsen’s Usability Heuristic and Norman’s Principles of Interactive Design in industry standard software Figma. Lastly a Heuristic Evaluation of new UI design, using Neilsen’s Usability Heuristic and User testing of redesign UI and have a group of users complete a Thinking Aloud Test of the new UI. Preliminary results of the Heuristic Evaluation of current UI arise issue with Consistency and Standards, Visibility of System Status, Match System and Real World and Recognition Rather than Recall. These issues were addressed in the proposed redesign by applying Neilsen’s Usability Heuristic and Norman’s Principles of Interactive Design. Next steps include formalized list of lessons learned and design implications for future publications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Control-Oriented Modeling of Cycle-to-Cycle Combustion Variability at the Misfire Limit in SI Engines

A control-oriented model is presented that can capture the prior-cycle correlation of combustion cycles during conditions with high levels of exhaust gas recirculation (EGR). Combustion events are modeled in discrete time and the dynamic evolution is captured by the residual air, fuel, and inert gas trapped in the combustion chamber. The mathematical formulation of the model is presented together with the calibration procedure to emulate a particular engine operating condition. A cycle-to-cycle system identification methodology is described which allows regressing model parameters from experimental data. Simulations are presented and compared to real engine measurements to show the modeling potential for analysis and control of combustion events.

Maldonado Puente, Bryan↗
DOE OSTI2026

2010-2012 California Household Travel Survey

The 2010-2012 California Household Travel Survey (CHTS) was administered by the California Department of Transportation, which collected demographic and travel behavior characteristics for residents across the entire state. At the time, it was the largest such regional or statewide survey ever conducted in the United States. Detailed travel behavior information was obtained from more than 42,500 households via multiple data-collection methods, including computer-assisted telephone interviewing, online and mail surveys, wearable (7,574 participants) and in-vehicle (2,910 vehicles) global positioning system devices, and on-board diagnostic sensors that gathered data directly from a vehicle's engine. Details of personal travel behavior were gathered within the region of residence, inter-regionally within the state, and in adjoining states and Mexico. The survey sampling plan was designed to ensure an accurate representation of the entire population of the state. The CHTS included additional features, such as vehicle-acquisition decisions, parking choices, work schedules and flexibility, use of toll lanes/priced facilities, and walk and bicycle trips, to support advanced model development.

1Hz data↗
DOE OSTI2020

Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes

Input signals may be received from monitoring nodes of the industrial asset, each input signal comprising time series data representing current operation. A neutralization engine may transform the input signals into feature vectors in feature space, each feature vector being associated with one of a plurality of overlapping batches of received input signals. A dynamic decision boundary may be generated based on the set of feature vectors, and an abnormal state of the asset may be detected based on the set of feature vectors and a predetermined static decision boundary. An estimated neutralized value for each abnormal feature value may be calculated based on the dynamic decision boundary and the static decision boundary such that a future set of feature vectors will be moved with respect to the static decision boundary. An inverse transform of each estimated neutralized value may be performed to generate neutralized signals comprising time series data that are output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sim2Ls: FAIR simulation workflows and data

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls , their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls , a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗