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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Manufacturing of HEAs at Different Scales

The knowledge gained from melting and processing of high-entropy alloys (HEAs) at different scales is discussed with respect to melt parameters and characteristics. The melting techniques considered are button melting, vacuum induction melting (VIM) and electroslag remelting (ESR). While VIM produces HEAs with enhanced chemical homogeneity, particularly after being subjected to a homogenization heat treatment, and refined grain structure, other concerns arise from elemental contaminants associated with industrial-grade melt stock. Although ESR of the VIM product decreases the concentration of tramp elements, the narrow melt range typically found in HEAs or medium entropy alloys was found to decrease the melt efficiency. The experiments presented were performed on ingots ranging from 100 g to 75 kg.

Detrois, Martin↗
DOE OSTI2024

Numerical Simulation of Biogenic Fluid Catalytic Cracking (BFCC) Regenerators at Different Scales with MFIX-Exa

Catalytic Fast Pyrolysis (CFP) is a process that converts biomass into liquid intermediates suitable for transportation fuels by rapidly heating it in the presence of a catalyst, aiming to produce stable oils with reduced oxygen content. During CFP, the catalyst can become deactivated by the accumulation of coke, a carbon-rich deposit formed from the decomposition of biomass components. Unlike in petroleum refining, regenerating coked catalysts from biomass pyrolysis requires specific approaches due to the different chemical nature of the coke formed. An experimental technique, Temperature Programmed Oxidation (TPO), was used to study the de-coking process by gradually increasing temperature while monitoring the production of CO and CO2, which provides data for kinetic modeling. Utilizing data from TPO experiments, coke combustion kinetic model was developed to describe the rate of coke removal at different temperatures, allowing for simulation of regeneration processes. Then kinetic model is integrated into MFIX-Exa for the simulation of Biogenic Fluid Catalytic Cracker (BFCC) regenerator at different scales, enabling analysis of catalyst flow, temperature distribution, and regeneration efficiency under various operating conditions.

biogenic fluid catalytic cracking↗
DOE OSTI2022

Maximum likelihood estimation of difference scaling functions for suprathreshold judgments

Maximum likelihood estimation (MLE) has been used to produce perceptual scales from binary judgments of triads and quadruples. This method relies on Thurstone’s theory of a stochastic perceptual process where the perceived difference of two stimuli is the difference in their perceived strengths. It is possible that the perception of a suprathreshold difference is overestimated when adding smaller differences, a phenomenon referred to as diminishing returns. The current approach to construct a perceptual scale using MLE does not account for this phenomenon. We present a way to model the perception of differences using MLE and Thurstone’s theory, adapted to allow the possibility of diminishing returns. This method is validated using Monte Carlo simulated responses to experimental triads and can correctly model diminishing returns, the absence of diminishing returns, and the opposite of diminishing returns both in the cases when a perceptual scale is known and when the true perceived strengths of the stimuli are unknown. Additionally, this method was applied to empirical data sets to determine its feasibility in investigations of perception. Ultimately, it was found that this analysis allows for more accurate modeling of suprathreshold difference judgments, a more complete understanding of the perceptual processes underlying comparisons, and the evaluation of Thurstone’s theory of difference judgments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functions with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including signal and image processing. Implementations of ADMM often utilize a single hyperparameter, referred to as the penalty parameter, which needs to be tuned to control the rate of convergence. However, in problems with multiple constraints, ADMM may demonstrate slow convergence regardless of penalty parameter selection due to scale differences between constraints. Accounting for scale differences between constraints to improve convergence in these cases requires introducing a penalty parameter for each constraint. The proposed method is able to adaptively account for differences in scale between constraints, providing robustness with respect to problem transformations and initial selection of penalty parameters. It is also simple to understand and implement. Our numerical experiments demonstrate that the proposed method performs favorably compared to a variety of existing penalty parameter selection methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modularization of Ceramic Hollow Fiber Membrane Technology for Air Separation

This proposed project is aimed at studying high performance and economically competitive ceramic membrane technology for air separation and high-purity oxygen production using hollow fiber ceramic membrane stack and module technology. The design of the single permeate membrane is a hollow fiber substrate-supported thin tri-layer structure. Radially well-aligned micro-channels are embedded in the thick substrate and open at the inner surface of the substrate, enabling facile air/gas diffusion. The tri-layer structure of thin dense membrane layer (~ 10 µm) sandwiched by porous surface layer on either side is then built on the substrate using advanced fabrication process. The tri-layer structure design allows different materials to be used in different layers, where the materials of surface layers have high surface exchange coefficients while the material of dense layer has high bulk diffusivity. Such a synergetic combination leads to high permeation performance. The focus of the proposed project will be on membrane stack/module development using the developed single hollow fiber membranes, including: 1) fabrication and characterization of novel single hollow fiber membranes; 2) membrane stack design and assembly using fabricated single membranes; 3) stack modeling and analysis to guide membrane stack designs; 4) permeation performance testing and characterization of membrane stacks. The hollow fiber feature and simple sealing requirement enable very compact design of membrane stacks, which have excellent flexibility for further modularizations at different scales. The operations of such membrane stack and module may employ the exhaust heat from other components of Integrated Gasification Combined Cycle and oxy-combustion systems. Therefore, modularization of such an air separation membrane technology can be incorporated into the DOE’s REMS (radically engineered modular systems)-gasification skid and support the oxidant feed of an oxygen-blown REMS gasifier scaled to different ranges.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Proximity Portability and in Transit , M-to-N Data Partitioning and Movement in SENSEI [Book Chapter]

In high-performance parallel in situ processing, the term in transit processing refers to those configurations where data must move from a producer to a consumer that runs on separate resources. In the context of parallel and distributed computing on an HPC platform one of the central challenges is to determine a mapping of data from producer ranks to consumer ranks. This problem is complicated by the heterogeneity that arises in producer-consumer pairs, such as when producer and consumer codes have different levels of concurrency, different scaling characteristics, or different data models. The resulting mapping and movement of data from M producer to N consumer ranks can have a significant impact on aggregate application performance, particularly when the data consumer requires only a subset of the overall data for its task. This chapter focuses on the design considerations that underlie SENSEI’s implementation to this challenging problem. These design considerations extend the core SENSEI architecture and include ideas like the need to accommodate flexibility in the choice of different partitioning methods, the ability for a data consumer to request and receive only the subset of data needed for its particular operation, and the ability to leverage any of several different data transport tools. The idea of proximity portability, being able to use different data transport methods as part of an in transit workflow, is illustrated through the use of three different transport layers where switching from one transport tool to another is accomplished with only a configuration file change. Here, the chapter also includes a performance analysis summary showing the performance gains that are possible in terms of multiple metrics, such as memory footprint, time to solution, and amount of data moved, when using optimized partitioners in an in transit setting, gains that are made possible by the implementation shaped by specific design considerations.

Bethel, E. Wes↗
DOE OSTI2026

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 4.

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 1.0 in 2017, followed by 2.0 in 2018, and 3.0 in 2021. This new release of FRED 4.0 now has 213,941 observations of 238 root traits, for a combined total of roughly 3.4 million data fields for root traits and ancillary data together. FRED 4.0 has 39.8% more root trait observations than FRED 3.0 and a 34.4% increase in unique data sources. This release of FRED 4.0 also includes significant increases in geographic regions that have long been underrepresented in global datasets, notably in the tropical low latitudes. Ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently with root trait observations. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. Despite this fine-root focus, FRED accepts data collected from roots of all sizes and contains observations of many root classes including coarse roots. Data collection will continue for the foreseeable future. The FRED4_Entire_Database_2026.csv file is the flat csv data file for FRED 4.0, and the FRED4_dd.csv file is the data dictionary of all columns available in FRED, including column IDs, column names, definitions, and unit (where applicable).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings

We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. Furthermore, the efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH 2 NH 2 + .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deciphering baryonic feedback with galaxy clusters

Abstract Upcoming cosmic shear analyses will precisely measure the cosmic matter distribution at low redshifts. At these redshifts, the matter distribution is affected by galaxy formation physics, primarily baryonic feedback from star formation and active galactic nuclei. Employing measurements from theMagneticumandIllustrisTNGsimulations and a dark matter + baryon (DMB) halo model, this paper demonstrates that Sunyaev-Zel'dovich (SZ) effect observations of galaxy clusters, whose masses have been calibrated using weak gravitational lensing, can constrain the baryonic impact on cosmic shear with statistical and systematic errors subdominant to the measurement errors of DES-Y3 and LSST-Y1, with systematic errors on S 8 and Ω m reaching 10% and 50% of the statistical errors, respectively. For LSST-Y6 and Roman surveys, these systematic errors increase to 150% and 100% of the statistical errors, indicating the necessity for further model developments for future surveys. We further dissect the contributions from different scales and halos with different masses to cosmic shear, highlighting the dominant role of SZ clusters at scales critical for cosmic shear analyses. These findings suggest a promising avenue for future joint analyses of Cosmic Microwave Background (CMB) and lensing surveys.

Astronomy & Astrophysics↗
DOE OSTI2023

On the use of a multigrid-reduction-in-time algorithm for multiscale convergence of turbulence simulations

Simulations of turbulent flow present challenges in terms of accuracy and affordability on modern highly-parallel computer architectures. A multigrid-reduction-in-time algorithm is used to provide a framework for separately evolving different scales of turbulence and for parallelizing the temporal domain, thereby increasing the concurrency. It is hypothesized that the space–time locality of the small scales of turbulence can be used to circumvent difficulties in applying temporal multigrid to flows dominated by inertial physics. For algorithms that fall well short of spectral accuracy (fourth-order is used in this work) attention must be paid to the accuracy of features on scales transferred between multigrid levels. Numerical experiments were performed using implicit large-eddy simulation. Results from applying the approach to an infinite-Reynolds number Taylor–Green flow and a double-shear flow at a Reynolds number of 11650 provide strong evidence that the approach has merit. The multigrid-reduction-in-time framework can be used to parallelize the temporal domain of a high-Reynolds-number turbulent flow and permit independent convergence of different scales. Establishing this foundation allows for future research in reducing the wall-clock time to solve turbulent flows while retaining the same accuracy as sequential solvers. In conclusion, current performance results from parallelizing the temporal domain are not competitive with those from sequential-in-time methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Detecting Passing Valves at Scale Across Different Buildings and Systems: A Brick Enabled and Mortar Tested Application

Heating hot water distribution systems are typically used in commercial buildings to condition spaces to provide occupant thermal comfort. However, recent research shows significant distribution losses within these systems that drive down the overall hot water plant efficiency. This research focuses on detecting passing valves in reheat coils found in variable air volume (VAV) terminal units to reduce distribution losses. A passing valve allows hot water flow when the actuator on the valve is commanded to be closed. The fluid causes unintentional heating or cooling to occur, causing comfort and control issues, and wasting energy. We developed the passing valve detection algorithm using a framework based on the Brick schema and Mortar platform to ensure that the application is portable and can scale to many buildings. We applied the same application to analyze 1,335 VAV reheat terminal units in 20 buildings. The diversity found in these large datasets increases confidence that any building with VAV reheat terminal units with the required sensors and Brick data model can run our open-source algorithm with little or no modification. In aggregate, 5% of VAV units analyzed were categorized as having a sensor fault, 14% with potential passing valve fault, and 81% with no faults detected. However, there is a significant variation in the proportion of VAV units with a passing valve detected (1% to 83%) of each building’s analyzed units.

Roa, Carlos Duarte↗
DOE OSTI2023

The HPC Container Experience on the Summit Supercomputer

Containers are seeing widespread use in the world of High Performance Computing, with many HPC Centers either providing their own containerization solution or adopting existing ones like Singularity and Apptainer. The demand for containerization options come from users who want to take advantage of the portability and reproducibility containers can provide, as well as being able to build and use applications that are only distributed in container form or are otherwise unsuited to natively run in an HPC environment. The users served by the Oak Ridge Leadership Computing Facility are no exception. We go over the past and current containerization offerings at the Oak Ridge Leadership Computing Facility, mainly focusing on the Summit supercomputer. We arrive at using a combination of Podman and Singularity to allow users to build and run containers directly on Summit, without requiring external resources or hardware for any step of the process. We look at a couple of projects running on Summit that greatly benefited from being able to use containers on Summit. And we compare benchmarks running natively and in containers on Summit at different scales, observing minimal performance difference and consistent behavior across all tests.

Abraham, Subil↗
DOE OSTI2023

system2atoms

system2atoms is a suite of models that can simulate the performance of hydrogen infrastructure across multiple scales. Examples of the different scale levels include: 1. System Scale - techno-economic analysis for hydrogen applications, integration of hydrogen infrastructure with the electrical grid, and supply and demand constraints across applications 2. Component Scale - continuous-flow catalyst beds, electrochemical conversion units, gas-phase separators, pumps, compressors, storage tanks, evaporators, heaters, and coolers 3. Material Scale - surface reactions, degradation mechanisms, and catalyst poisoning

Mcnenly, MatthewJ↗