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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effect of fuel composition and EGR on spark-ignited engine combustion with LPG fueling: Experimental and numerical investigation

This paper presents an experimental and numerical investigation of a spark-ignited (SI) cooperative fuel research (CFR) engine fueled with different liquefied petroleum gas (LPG) fuels and exhaust gas recirculation (EGR). Here, the effects of LPG fuel composition on engine combustion characteristics are initially evaluated at two different compression ratios (CR). Results show normal combustion at CR 7 and heavy knocking combustion at CR 10 for all the tested fuels, with a more substantial impact for the LPG fuel with high proportions of n-butane species. The Livengood-Wu (LW) integral method is then used to analyze the knock occurrence risk of individual fuel based on the reactivity of the tested fuels. The introduction of EGR then demonstrates the potential of knock intensity reduction below the borderline knock limit. A zonal-based kinetic interactions study is also performed to understand the knock mitigation effectiveness of EGR over the pressure–temperature domain relevant to SI engine operation. Finally, a multidimensional, computational fluid dynamics (CFD) simulation model is shown to predict the LPG combustion characteristics and presents the evolution of in-cylinder temperature and chemical species to demonstrate the development of end-gas autoignition events without and with EGR.

02 PETROLEUM↗
DOE OSTI2025

Creep performance and microstructure of grade 91 steel weldments with integrated welding and thermal processing

Ferritic-Martensitic steel welds typically require post weld heat treatment (PWHT) to restore toughness and high temperature performance. This off-line thermal process reduces disparities between weld and base metal, but can cause distortion, cracking, or simply be impractical due to assembly size and joint non-uniformity. Here we show integrated welding and thermal processing applied to modified 9Cr-1Mo (Grade 91) steel, favored for advanced power generation applications, performed in real time through the addition of a secondary heat source near the primary weld head. Optimal integrated processing reduces weld fusion and heat affected zone hardness by 125 HV, approaching performance of conventional 730 °C, 60 min PWHT processing. Microstructures and mechanical performance are compared for mechanized GTAW welds, with equivalent lifetimes noted in cross-weld creep rupture tests up to 234 MPa at 550 °C, and up to 104 MPa at 650 °C. The integrated process was validated on a Grade 91 pressure vessel with multipass cold wire feed GTAW. After 550 °C, 71.4 bar thermomechanical cyclic testing, the maximum weld hardness is <350 HV.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Custom Low-Noise Silicon Photodiode Detector Designed for Use With X-Ray Capillary Optics

We present the first results of a custom low-noise silicon photodiode detector designed for use with X-ray capillary optics. This system is intended to perform as a smaller, lighter, and low-powered alternative to currently existing X-ray telescope technologies. The detector has an active area with a diameter of 100 μm, and its small size leads to a low capacitance of well under 1 pF and a leakage current of 1 pA or less at the depletion voltage. We tested the detector with both a discrete preamp and an ultralow-noise front-end application-specific integrated circuit (ASIC). The front-end ASIC, developed at Brookhaven National Laboratory, was designed for low-capacitance detectors but had not been previously tested with a detector. Here, we discuss the detector design, the experimental setup, and the resulting detector performance. Using radioactive laboratory sources, we measure a full-width at half-maximum (FWHM) of 211 ± 8 eV at 5.9 keV with the ASIC and are able to set the threshold as low as 0.6 keV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Scaling Building Energy Audits through Machine Learning Methods on Novel Drone Image Data

Building energy audits are time-consuming and labor-intensive. This paper describes a new method using machine learning (ML) techniques on novel data sources (drone images) to improve the identification of building characteristics and retrofit opportunities, and thereby reduce the effort for audits. The new ML method includes: (1) Building footprint extraction using line extraction, polygonization, and polygon-merging, (2) Building envelope extraction using PIX4d modeling software to reconstruct a building 3D model, (3) Visualization tool for viewing images from the 3D model, (4) Window-to-wall ratio (WWR) using state-of-art deep neural network semantic segmentation, (5) Envelope thermal anomaly detection using an unsupervised machine learning clustering algorithm, and (6) Rooftop energy equipment detection based on an object detection algorithm. The testing of this method involved a comparison of additional ML-generated information overlaid on current ‘state-of-practice’ audit and remote assessment baselines using evaluation metrics: labor time and associated cost, marginal benefits of using ML-generated information in workflows for audits and remote assessments, integration potential with existing processes and tools, and replicability/scalability of the method. In two test buildings in California that had comprehensive drawings and meter data available, the ML method effectively generated a building footprint, envelope, rooftop equipment, WWR, and locations of envelope thermal anomalies. Projected target segments of the ML method are sites with minimal drawings and energy data, and underserved sectors such as multistoried housing, disadvantaged communities, and schools for which the ML method can enable identification of building asset characteristics and prioritization of envelope retrofits and decentralized energy equipment retrofits.

Singh, Reshma↗
DOE OSTI2023

Experimental and theoretical investigations of rotating algae biofilm reactors (RABRs): Areal productivity, nutrient recovery, and energy efficiency

Microalgae biofilms have been demonstrated to recover nutrients from wastewater and serve as biomass feedstock for bioproducts. However, there is a need to develop a platform to quantitatively describe microalgae biofilm production, which can provide guidance and insights for improving biomass areal productivity and nutrient uptake efficiency. Here, this paper proposes a unified experimental and theoretical framework to investigate algae biofilm growth on a rotating algae biofilm reactor (RABR). Experimental laboratory setups are used to conduct controlled experiments on testing environmental and operational factors for RABRs. We propose a differential–integral equation‐based mathematical model for microalgae biofilm cultivation guided by laboratory experimental findings. The predictive mathematical model development is coordinated with laboratory experiments of biofilm areal productivity associated with ammonia and inorganic phosphorus uptake by RABRs. The unified experimental and theoretical tool is used to investigate the effects of RABR rotating velocity, duty cycle (DC), and light intensity on algae biofilm growth, areal productivity, nutrient uptake efficiency, and energy efficiency in wastewater treatment. Our framework indicates that maintaining a reasonable light intensity range improves biomass areal productivity and nutrient uptake efficiency. Our framework also indicates that faster RABR rotation benefits biomass areal productivity. However, maximizing the nutrient uptake efficiency requires a reasonably low RABR rotating speed. Energy efficiency is strongly correlated with RABR rotating speed and DC.

09 BIOMASS FUELS↗
DOE OSTI2025

Innovative Method for Reliable Measurement of PEM Water Electrolyzer Component Resistances

Understanding the sheet resistance of porous electrodes is essential for improving the performance of polymer electrolyte membrane (PEM) water electrolyzers and related technologies. Despite its importance, existing methods often fail to provide reliable and comprehensive data, especially for porous materials with complex morphologies and non‐uniform thicknesses. This study introduces a robust and straightforward method for determining the sheet resistance of porous electrodes using a novel probe concept based on industrial printed circuit board (PCB) technology. This probe measures resistance across ten distances, ranging from 250 µm to 2500 µm, enabling local mapping of resistance. The study focuses on the sheet resistance of key components in PEM water electrolyzers, including the gas diffusion layer (GDL), porous transport layer (PTL), and catalyst layers deposited on a membrane. Additionally, an image‐processing‐based method is presented to obtain the thickness distribution of the studied catalyst layers, facilitating a detailed analysis of the electrical in‐plane resistivity with thickness variations. Overall, this methodology has the potential to expedite material integration and bridge the gap between electrode engineering and single‐cell testing, thereby advancing the development of PEM water electrolyzers.

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

Current research in low temperature proton exchange membrane-based electrolysis and a necessary shift in focus

While hydrogen has limited use in energy markets today, significant growth is possible in grid storage and as an energy carrier in other sectors. Along with lower electricity prices, further hydrogen cost reduction can enable electrochemical water splitting, particularly as the use of intermittent, renewable power sources increases and the quantity and duration of storage requirements limit electrical storage options. With the improved viability of large-scale hydrogen production, research in low temperature electrolysis has dramatically increased. The overwhelming majority of research efforts within proton exchange membrane-based systems has focused on catalyst development, although a smaller set have developed transport layers as well. As proton exchange membrane-based electrolysis shifts into higher technology readiness levels, however, there is a growing need for work optimizing materials integration, developing component-specific and device-level accelerated stress tests, and addressing manufacturing considerations. These areas directly impact electrolyzer performance, durability, and cost, and are critical to advancing hydrogen’s value and electrochemical water splitting.

08 HYDROGEN↗
DOE OSTI2021

Anisotropic fluid dynamical simulations of heavy-ion collisions

We present VAH, a (3+1)–dimensional simulation that evolves the far-from-equilibrium quark-gluon plasma produced in ultrarelativistic heavy-ion collisions with anisotropic fluid dynamics. We solve the hydrodynamic equations on an Eulerian grid using the Kurganov–Tadmor algorithm in combination with a new adaptive Runge–Kutta method. Our numerical scheme allows us to start the simulation soon after the nuclear collision, largely avoiding the need to integrate it with a separate pre-equilibrium dynamics module. We test the code's performance by simulating on the Eulerian grid conformal and non-conformal Bjorken flow as well as conformal Gubser flow, whose (0+1)–dimensional solutions are precisely known. Finally, we compare non-conformal anisotropic hydrodynamics to second-order viscous hydrodynamics in central Pb+Pb collisions and find that the former's longitudinal flow profile responds more consistently to the fluid's gradients along the spacetime rapidity direction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. Here, we exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions

Computational fluid dynamics↗
DOE OSTI2025

Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites

A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle-reinforced metal matrix nanocomposites (MMNCs). Using aluminum–silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification-regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning-enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by in-situ scanning electron microscopic tensile testing on perforated Al specimens. This study underscores the promise of integrating physics-informed machine learning with hierarchical modeling to capture the interface dominated phenomena and guide the design of advanced MMNCs.

Al-SiC↗
DOE OSTI2022

A SiPM-based optical readout system for the EIC dual-radiator RICH

Silicon photomultipliers (SiPM) are candidates selected as the potential photodetector technology for the dual-radiator Ring-Imaging Cherenkov (dRICH) detector at the future Electron-Ion Collider (EIC). SiPM are cheap devices, highly efficient and insensitive to the high magnetic field at the expected location of the sensors in the experiment. Here, on the other hand, SiPM are not radiation tolerant. Despite the integrated radiation level is expected to be moderate it should be tested whether single photon-counting capabilities and the increase in dark count rate can be kept under control to maintain the optimal dRICH detector performance across the years. The current status of the research for the EIC dRICH application and the first results on studies performed on a sample of commercial and prototype SiPM sensors are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

SMR safety through HTTF modeling and benchmark efforts for code validation for gas-cooled reactor applications

Accurate modeling and simulation tools for thermal-hydraulics calculations are a key element needed to design and license new advanced reactors including Small Modular Reactors (SMR) and Microreactors. Uncertainties in modeling and simulation can have significant safety and economic implications. The High Temperature Test Facility (HTTF) at Oregon State University (OSU) is a scaled integral effects experiment designed to investigate transient behavior in high-temperature gas-cooled prismatic-block nuclear reactors. High-quality measurement data is available from the HTTF that is suitable for a thermal-hydraulics code validation benchmark for gas-cooled reactor simulations. Here, this paper summarizes individual HTTF modeling efforts to date for tool validation at Idaho National Laboratory (INL), Argonne National Laboratory (ANL), Oregon State University (OSU) and Canadian Nuclear Laboratories (CNL) using system thermal-hydraulics codes, Computational Fluid Dynamics (CFD) codes and system-CFD code couplings. Also, the paper introduces the ongoing OECD Nuclear Energy Agency (NEA) High Temperature Gas Reactor Thermal-Hydraulics (HTGR T/H) benchmark that allows for better comparisons of results between different international modeling teams. The benchmark provides well defined computational problems that include code-to-code comparisons and comparisons to measured data. These problems provide an avenue for quantifying accuracy and identifying sources of uncertainty in thermal-hydraulics calculations, including in measured thermophysical properties, as part of validation for gas-cooled reactor simulation tools.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2021

Revisiting diffusion-based moisture functions: why do they fail?

Microbial activities in soil are controlled by the balance between physical and biological mechanisms that are currently implied by soil moisture-respiration relationships, but not explicitly represented. Diffusion based moisture functions focus on physical controls, while substrate kinetics based models primarily address biological drivers. How to harmonize these two types of models for robust predictions of soil respiration and other processes has not been addressed. Here we derived a generalizable analytical solution of diffusion-limited nonlinear microbial uptake kinetics, in which microbial metabolic rates were governed by both physical and biological drivers via substrate diffusion and microbial affinity. We proposed a soil trait concept to integrate both physical and biological perspectives into one unified function, and tested it using both laboratory and field based observations from a variety of soils. This approach provides a tractable and scalable approach that incorporates soil heterogeneity and functional complexity in macroscopic model descriptions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Abstract Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Lab-Scale Cable-Driven Parallel Robot Prototype for Automated Prefabricated Component Manipulation

This paper presents the design and evaluation of a lab-scale cable-driven parallel robot (CDPR) developed as a flexible platform for automated installation of prefabricated components onto exterior building envelopes. Traditional manual installation methods for prefabricated components, which depend on scaffolding, cranes, cherry pickers, and verbal coordination, are not only labor-intensive and error-prone but also face significant limitations in dense urban environments due to site access constraints. To address these challenges, we developed a lab-scale CDPR platform capable of autonomously transporting building envelope components from a designated pickup zone to their target installation location, minimizing the need for human intervention. This study describes the system’s mechanical design, actuation architecture, real-time feedback system, and control strategy of the CDPR, and evaluates its performance in a laboratory environment. The robot’s actuation system uses torque control for end-effector manipulation. The robot’s real-time pose feedback comes from a construction-grade total station and a wireless inertial measurement unit (IMU), which together support precise end-effector control. Experimental results demonstrate the successful integration of the hardware, sensing, state estimation, and control subsystems. Preliminary tests showed that our lab-scale prototype can position the end effector with an error of less than 3 mm, which is a level of precision not previously achieved by existing CDPRs in construction applications. The key findings are twofold: (1) torque-only control is necessary but not sufficient for minimizing final pose error, and (2) incorporating real-time pose feedback can achieve the desired placement accuracy.

Liu, Yifang [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2021

Overhaul and installation of the ICARUS-T600 liquid argon TPC electronics for the FNAL Short Baseline Neutrino program

The ICARUS T600 liquid argon (LAr) time projection chamber (TPC) underwent a major overhaul at CERN in 2016–2017 to prepare for the operation at FNAL in the Short Baseline Neutrino (SBN) program. This included a major upgrade of the photo-multiplier system and of the TPC wire read-out electronics. The full TPC wire read-out electronics together with the new wire biasing and interconnection scheme are described. The design of a new signal feed-through flange is also a fundamental piece of this overhaul whose major feature is the integration of all electronics components onto the signal flange. Initial functionality tests of the full TPC electronics chain installed in the T600 detector at FNAL are also described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Comparison of heavy-ion transport simulations: Mean-field dynamics in a box

Within the transport model evaluation project (TMEP) of simulations for heavy-ion collisions, the mean-field response is examined here. Specifically, zero-sound propagation is considered for neutron-proton symmetric matter enclosed in a periodic box, at zero temperature and around normal density. The results of several transport codes belonging to two families (BUU-like and QMD-like) are compared among each other and to exact calculations. For BUU-like codes, employing the test particle method, the results depend on the combination of the number of test particles and the spread of the profile functions that weight integration over space. These parameters can be properly adapted to give a good reproduction of the analytical zero-sound features. QMD-like codes, using molecular dynamics methods, are characterized by large damping effects, attributable to the fluctuations inherent in their phase-space representation. Moreover, for a given nuclear effective interaction, they generally lead to slower density oscillations, as compared to BUU-like codes. The latter problem is mitigated in the more recent lattice formulation of some of the QMD codes. Furthermore, the significance of these results for the description of real heavy-ion collisions is discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Module-OT: A Hardware Security Module for Operational Technology

Increased penetration levels of renewable energy and other types of distributed energy resources (DERs) on the modern electric grid-combined with technological advancements for electric system monitoring and control-introduce new cyberattack vectors and increase the cyberattack surface of energy systems. According to the IEEE Std. 1547-2018, DERs must use Modbus, Distributed Network Protocol 3 (DNP3), or Smart Energy Profile 2.0 (SEP2) as their communication protocol. Previous research identified several vulnerabilities and security breaches in each one of these communication protocols; despite this, existing standards for DERs do not recommend cybersecurity measures. In order to reduce vulnerabilities in power distribution systems, this paper presents a novel open-source hardware security module that improves both information and operational security to better protect data and communications on the distribution grid. The security hardware is called “module for operational technology,” or simply Module-OT, and it has been validated and tested in an emulated distribution system application. Module-OT is integrated within a communication system in the transport layer of the Open Systems Interconnection (OSI) model. It improves system security through encryption, authentication, authorization, certificate management, and user access control. The main advancement of Module-OT is the addition of hardware cryptographic acceleration that improves the overall communication performance in terms of end-to-end latency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗