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DOE OSTI2025

Tribology and Tribocorrosion of Case-Hardened Steels: A Review

This report reviews the tribological and tribocorrosion performance of steels that are case-hardened via boriding, chromizing, carburizing, nitriding, nitrocarburizing, and carbonitriding. Case-hardening is commonly used to improve the hardness, impact durability, wear resistance, and corrosion resistance of steel alloys and has been successfully applied in various industries, providing a cost-effective, high-throughput solution for applications involving contact and sliding interfaces in complex service environments. This article summarizes the literature results of the wear and friction behavior of common case-hardening methods for steel alloys under various conditions, including corrosive environments. Special attention is given to the influences of case-hardening process parameters and alloy composition on tribological performance. Furthermore, by discussing key findings from the literature, this review provides insights into optimizing case-hardening processes for improving the tribological and tribocorrosion performance of steel alloys.

Alloys↗

DOE OSTI2021

An Investigation of the Effects of Volume Fraction on Drag Coefficient of Non-Spherical Particles Using PR-DNS

Prediction of the drag coefficient is required in gas-particle multiphase flow modeling and simulation. Experimental data and correlations on the fixed-bed system of spherical particles with high volume fractions for various possible arrangements are available in the literature. However, the effect of volume fraction on the drag coefficient of non-spherical particles is not well studied. In solving the momentum equation, the volume fraction plays a vital role in determining the flow resistances. In this paper, we study the impact of volume fraction in the range of 0.069 to 0.65 on the drag coefficient using the computational fluid dynamics (CFD) simulation of air for Reynold number in the range of 10 to 10000 using particle resolved direct numerical solution (PR-DNS). Regular non-spherical particles such as a cube, tetrahedron, and spheroids are used in this study since their single particle’s drag coefficient data are available in the literature for comparison. For this work, the simulations are carried out in the Ansys Fluent using polyhedral mesh, which consumes significantly less computational time and power. The study showed the sphericity and volume fraction have significant impact on the bed pressure drop and average drag coefficient of the particles in the bed especially in high Reynolds number regime. The bed of the spheroid experiences the lowest drag being the most streamlined particle, and the particles with the edges result in a large drag coefficient due to flow separation at the discontinuity. The vector plots verify this behavior where large wake regions are observed behind the tetrahedron particle.

Mahyawansi, Pratik↗

DOE OSTI2021

Gaining Insights in Loading Events for Wind Turbine Drivetrain Prognostics

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (WEC). This can cause the bearing lifetime to be reduced to 5–10% of its design value. Multiple hypotheses are available in literature concerning its cause. To be able to validate or disprove these hypotheses, it is essential to have historic high-frequency measurement data (e.g., load and vibration levels) available. In time, this will allow linking to the history of the turbine operating data with failure data. This paper discusses the dynamic loading on the turbine during certain events (e.g., emergency stops, run-ups, and during normal operating conditions). By combining the number of specific events that each turbine has seen with the severity of each event, it becomes possible to assess which turbines are most likely to show signs of damage.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2022

Effect of Thermal Expansion Coefficient, Viscosity and Melting Range in Simulation of PCM Embedded Heat Exchangers With and Without Fins

Phase change material heat exchangers (PCMHX) have an important role in integrating renewable energy systems. PCMHX can offer high storage density at various temperatures of interest and can be used for grid load shifting purposes. Numerical models enable engineers to estimate PCMHX performance for different design parameters and operating conditions. Modeling phase change phenomena is challenging due to the complex time-dependent nature of the process. The accuracy of models is highly sensitive to PCM thermo-physical properties. Thermal expansion coefficient (β), viscosity (μ) and melting temperature range (MR) of a PCM are important properties, especially when natural convection is not negligible. In PCMHX modeling, using less than accurate values for these properties can have significant impact on the simulation outcomes. These properties and discussions thereof are not readily available in the literature. This paper presents a brief review of the literature and a numerical study investigating the model sensitivity to the above-mentioned properties for PCMHX with and without fins. CFD is used to evaluate the charging (melting) phenomena. The study quantifies the impact of uncertainty in these properties on the melting rate and temperature profile. Constant wall temperature was considered as heat source with no heat loss to ambient. Here, the results show that β and μ has significant effect on the melting rate and evolution of the melting front. For a non-finned domain when comparing results for different published values of β and μ, the deviation in melting time can be up to 12.9% and 57.6% respectively. For high wall temperatures, change in melting range did not impact melting time. But when the wall temperature is reduced, up to 9.8% deviation in melting time is observed.

CFD↗

DOE OSTI2024

Heave Plate Hydrodynamic Coefficients for Floating Offshore Wind Turbines - A Compilation of Data

The National Renewable Energy Laboratory's OpenFAST software is utilized by academics and industry professionals alike to simulate offshore wind turbines. The software's modeling of hydrodynamic loads on heave plates attached to these structures relies on user-specified hydrodynamic coefficients. To guide the proper selection of these coefficients and potentially develop a new functionality within OpenFAST that automatically prescribes and/or adjusts the heave-plate hydrodynamic coefficients, we review past literature to examine the dependence of the added mass, damping, and drag coefficients on various relevant nondimensional parameters, including the Keulegan-Carpenter number, the frequency parameter, and the plate thickness ratio. Existing data in the literature show strong dependence of the hydrodynamic coefficients on the Keulegan-Carpenter number. We observe consistent trends across a range of different plate geometry, plate porosity, and flow conditions. Secondary dependence of the coefficients on the frequency parameter and plate thickness ratio is also present.

added mass↗

DOE OSTI2020

Role of consistent terminology in XPS reproducibility

The use of inconsistent and incorrect terminology in scientific publications contributes to misunderstanding, confusion, and erroneous results in the scientific literature. This issue is of particular importance in x-ray photoelectron spectroscopy (XPS) that is in widespread use for many different purposes by scientists with diverse backgrounds. A set of surface analysis terminology, approved through consensus by international experts, has been developed by International Organization for Standardization Technical Committee TC201 on Surface Chemical Analysis. To encourage wide use, the terminology is accessible at several web sites free of charge. This short overview provides examples to highlight the importance of agreed terminology in eliminating confusion between similar terms. Examples are provided of terms that are commonly misused or confused in the literature. Other examples highlight terminology that provides a common basis for comparing instrument parameters and performance. As science advances, it is important to clarify terminology for describing evolving concepts and developments important to XPS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

Epitaxial oxide thin films for oxygen electrocatalysis: A tutorial review

Epitaxial thin films can offer unprecedented definition of an electrocatalyst surface, defining the crystallographic orientation, interaction with a current collector, and providing an atomically flat surface for quantitative spectroscopy yielding mechanistic insight. This review introduces experts in thin films to the important parameters of electrochemical setups, techniques, and methods for quantifying electrocatalytic activity and comparing it across the literature. Examples are drawn from the literature measuring oxygen electrocatalysis on epitaxial oxide thin films. Furthermore, we share best practices in the robust measurement of intrinsic activity of thin films, including methods of electrical contact and assessment of charge transport in situ. We conclude by highlighting recent insights from epitaxial films in understanding oxygen electrocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Wavelength- and irradiance-dependent changes in intracellular nitric oxide level

Photobiomodulation (PBM) refers to beneficial effects of low energy light absorption. Although there is a large and growing body of literature describing down-stream physiological benefits of PBM, there is limited understanding of the molecular mechanisms that initiate and mediate these effects at the level of photon absorption. At present, the leading hypothesis is that light absorption induces release of nitric oxide (NO) from the active site of cytochrome c oxidase (COX), allowing it to bind O 2 instead. This is believed to increase mitochondrial respiration, and result in greater overall health of the cell due to increased ATP production. Aim: Although NO itself is a powerful signaling molecule involved in a host of biological responses, less attention has been devoted to the mechanisms of NO release and subsequent effects on cell signaling. The purpose of this work is to investigate wavelength-specific effects on intracellular NO release in living cells. Approach: We have conducted in depth dosimetry analyses of NO production and function in an in vitro retinal model in response to low energy exposure to one or more wavelengths of laser light. Results: We found statistically significant wavelength-dependent elevations (10-30%) in intracellular NO levels following laser exposures at 447, 532, 635 or 808 nm. Sequential or simultaneous exposures to light at two different wavelengths enhanced the NO modulation up to 50% of unexposed controls. Additionally, the immediate increases in cellular NO levels were independent of the function of nitric oxide synthase, depended greatly on the substrate source of electrons entering the electron transport chain, and did not result in increased levels of cGMP. Conclusions: This study concludes the simple model of light-mediated release of NO from COX is unlikely to explain the wide variety of PBM effects reported in the literature. Our multi-wavelength method provides a novel tool for studying immediate and early mechanisms of PBM, as well as exploring intracellular NO signaling networks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Characterizing particle-based thermal storage performance using optical methods for use in next generation concentrating solar power plants

Concentrating Solar Power (CSP) generation is an attractive option for low-emission power generation; however, the high costs of thermal storage associated with concentrating solar create a large barrier for their use and adaptation into modern life. Lowering their operation costs, while maintaining high thermal storage and transfer performance is essential. Solid particle-based heat exchange systems can reduce CSP cost but are often less efficient. Efforts to increase their performance have led to use of binary size particle mixes. Presented is an optical-based thermal analysis technique used to measure near-wall thermal conductivity of particle beds essential in determining their heat exchanger efficiency. Modulated Photothermal Radiometry is used to make dynamic temperature measurements, allowing for the extraction of the most relevant thermal properties like thermal conductivity, specific heat, and effusivity. The system uses a modulated laser source causing a damped periodic heat flux, resulting in a frequency and thermal property dependent surface temperature, of which is measured using radiometry. Lock-In techniques are used to extrapolate the amplitude of the signal. Plotting the amplitude against the root angular frequency allows for effusivity measurement by ratio to a known sample. Using specific heat measurements from literature and density measurements, the thermal conductivity of the particle mixes can be calculated. The simplicity of MPTR to probe through the depth of the bed is ideal for use in CSP for dynamic thermal performance monitoring.

Corona, Javier↗

DOE OSTI2020

Provenance infrastructure for multimodal x-ray experiments and reproducible analysis

Exploring relationships between material structures and desired properties for material discovery and synthesis requires mining machine-readable databases and linking experimental data with sufficiently rich annotations. There are vast gaps both in these data sources and in the connections between them. We present a provenance-based data management and analysis framework that enables capturing, persisting, and reanalyzing experimental data from the BNL National Synchrotron Light Source II (NSLS-II). Our system leverages NSLS-II Bluesky for experimental data acquisition, captures analysis parameters, and enriches the search space with results from the scientific literature. We describe in detail the challenges and benefits of data-driven approaches when comparing computational and experimental data, such as the multiple possible scenarios of interest in often ill-posed inverse problems and missing data. Our infrastructure utilizes the NSLS-II event model and includes discovery mechanisms across multiple beamlines. A text and data mining module using natural language processing classification techniques extracts relevant information from the literature that is added to data sources. We describe a provenance acquisition and analysis rerun workflow based on a directed acyclic graph that keeps track of process and data provenance and allows to replay analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Constructing Neural Network Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. Here this article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation

Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.

AI-driven discovery↗

DOE OSTI2021

Dry Deposition of Atmospheric Aerosols: Approaches, Observations, and Mechanisms

Aerosols are liquid or solid particles suspended in the atmosphere, typically with diameters on the order of nanometers to microns. These particles impact air quality and the radiative balance of the planet. Dry deposition is a key process for the removal of aerosols from the atmosphere and plays an important role in controlling the lifetime of atmospheric aerosols. Dry deposition is driven by turbulence and shows a strong dependence on particle size. This review summarizes the mechanisms behind aerosol dry deposition, including measurement approaches, field observations, and modeling studies. We identify several gaps in the literature, including deposition over the cryosphere (i.e., snow and ice surfaces) and the ocean; in addition, we highlight new techniques to measure black carbon fluxes. While recent advances in aerosol instrumentation have enhanced our understanding of aerosol sources and chemistry, dry deposition and other loss processes remain poorly investigated.

Chemistry↗

DOE OSTI2020

Investigating the Effects of Lithium Deposition on the Abuse Response of Lithium-Ion Batteries

Li deposition at the graphitic anode is widely reported in literature as one of the leading causes of capacity fade in lithium-ion batteries (LIBs). Previous literature has linked Li deposition resulting from low-temperature ageing to diminished safety characteristics, however no current research has probed the effects of Li deposition on the abuse response of well-characterized cells. Using overtemperature testing, a relationship between increased concentrations of Li deposition and exacerbated abuse response in 1 Ah pouch cells has been established. A novel Li deposition technique is also investigated, where cells with n:p < 1 (anode-limiting) have been cycled at a high rate to exploit Li + diffusion limitations at the anode. Scanning Electron Microscopy of harvested anodes indicates substantial Li deposition in low n:p cells after 20 cycles, with intricate networks of Li(s) deposits which hinder Li + intercalation/deintercalation. Peak broadening and decreased amplitude of differential capacity plots further validates a loss of lithium inventory to Li + dissolution, and Powder X-ray Diffraction indicates Li + intercalation with staging in anode interstitial sites as the extent of Li deposition increases. A cradle-to-grave approach is leveraged on cell fabrication and testing to eliminate uncertainty involving the effects of cell additives on Li deposition and other degradation mechanisms.

25 ENERGY STORAGE↗

DOE OSTI2021

Circuit Analysis of Ionizing Surface Potential Measurements of Electrolyte Solutions

Surface potential measurement values of the gas-liquid interface can be ambiguous despite the numerous electrochemical approaches used for quantification of the reported values. Calibration and normalization methods are not standardized, which often undermines the robustness of the reported values. Surface potential instrumentation and data interpretation also varies significantly across literature. Here, we propose a circuit model for an ionizing surface potential method based on the alpha decay of a radioactive americium-241 electrode. We evaluate the robustness of the circuit model for quantifying the surface potential at the air-aqueous interface. We then show successful validation of our circuit model through determination of the surface tension of the air-electrolyte interface with comparison to respective surface tension literature values. This validation reveals the reliability of surface potential measurements using the americium-241 ionizing method. We also report the surface potential difference of the air/water interface to be –0.49 V ± 0.01 V consistent with hydrogens of water pointing toward the air phase.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

BattPhase—A Convergent, Non-Oscillatory, Efficient Algorithm and Code for Predicting Shape Changes in Lithium Metal Batteries Using Phase-Field Models: Part I. Secondary Current Distribution

Electrochemical models at different scales and varying levels of complexity have been used in the literature to study the evolution of the anode surface in lithium metal batteries. This includes continuum, mesoscale (phase-field approaches), and multiscale models. Thermodynamics-based equations have been used to study phase changes in lithium batteries using phase-field approaches. However, grid convergence studies and the effect of additional parameters needed to simulate these models are not well-documented in the literature. In this paper, using a motivating example of a moving boundary model in one- and two-dimensions, we show how one can formulate phase-field models, implement algorithms for the same and analyze the results. An open-access code with no restrictions is provided as well. This article concludes with some thoughts on the computational efficiency of phase-field models for simulating dendritic growth.

25 ENERGY STORAGE↗

DOE OSTI2023

Membrane Pretreatment and Cell Conditioning for Proton Exchange Membrane Water Electrolysis

As the research community studying proton exchange membrane water electrolysis (PEMWE) grows, it is important to develop methods that achieve transparent, reproducible research. Reproducibility of performance and durability is a challenge facing the PEMWE field, as the published literature includes a wide spread of results obtained with nominally similar materials. Prior round-robin performance benchmarking efforts [1] have identified inadequate cell conditioning as a major source of variation in apparent cell performance. Inadequate pre-treatment and conditioning can lead to instability in initial performance and adds ambiguity to durability measurements. However, excessively prolonged conditioning procedures limit the throughput of testing and the pace of research. Pretreatment and conditioning methods vary significantly across the research literature, but little systematic investigation is available into the mechanisms of these procedures or how procedural differences may impact results. This presentation will discuss investigations into the effects of membrane pre-treatment and operating procedures during cell conditioning on initial performance and catalyst-specific accelerated stress tests, with the aim of recommending procedures to enable clear, reproducible, and high-throughput research. Methods investigated include the use of hydrogen peroxide, acids, and hydration at elevated temperature for membrane pretreatment, and conditioning procedures such as current or voltage holds and cycling. The investigations cover both the impacts of these procedures on cell performance and stability as well as underlying mechanisms and processes taking place in the cell materials.

cell↗