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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 OSTI2021

Potential Dependent Mn Oxidation and Its Role in Passivation of Ni 38 Fe 20 Cr 22 Mn 10 Co 10 Multi-Principal Element Alloy Using Multi-Element Resolved Atomic Emission Spectroelectrochemistry

The identity of passivating oxides on multi-principal element alloys is of great interest as their optimization offers the potential for exceptional corrosion resistance in aqueous solutions over a broad range of potential and pH. This study focuses on a non-equiatomic Ni 38 Fe 20 Cr 22 Mn 10 Co 10 solid solution alloy and tracks the fate of each alloying element during linear sweep voltammetry, low and intermediate potential holds in the passive potential domain as well as during open circuit relaxation after anodic polarization in slightly acidified Cl − solution. Ni dissolves at all potentials investigated in this work, Fe and Co are incorporated into oxides or hydroxides in low concentrations whilst Cr and Mn are enriched at passive potentials. At low passivating potentials, Mn(II) dissolves and is incorporated in minor amounts in oxides containing large concentrations of Cr(III). Considerable enrichment in Mn(II)-species occurs relative to Cr(III) in the oxide at 0.1 V vs SCE. Electrochemical impedance spectroscopy suggests the presence of layered oxides with marginal passivation at high Mn(II) levels. The formation of these oxides depends on a combination of thermodynamic and kinetic factors as well as the sequence of passivation.

Han, Junsoo (ORCID:0000000192952954)↗
DOE OSTI2024

MULTI-LEADER: MULTI-source LEarning-Accelerated Design of high-Efficiency multi-stage compRessor (Final Technical Report)

The objective of MULTI-LEADER is to cut design costs by 80% while generating more energy-efficient designs of multi-stage compressors by developing and implementing novel machine learning (ML) techniques, which enable faster and fewer design iterations, improved solver performance, and concurrent multi-disciplinary design. Current industrial practices for the design of multi-stage compressors involve simulation-based design optimization with successive levels of model fidelity, iteratively evaluated between distinct disciplines, one stage at a time to tackle the high dimensional design variations. This project addresses these key design challenges: (1) concurrent optimization of multiple stages under many non-linear constraints; (2) multitude of evaluation of high-fidelity and expensive solvers and their gradients during optimization convergence in high-dimensional design; (3) multi-disciplinary design to maximize aerodynamic performance while guaranteeing structural integrity and additive manufacturability; (4) utilization of multiple fidelity of solvers with disparate parameterization and modeling assumptions. MULTI-LEADER achieved more than 5x speed up in detailed design of more energy-efficient compressors via these machine learning (ML) innovations: (i) rapid design surrogates by multi-source learning from diverse fidelities across multiple disciplines, (ii) physics-constrained data-augmented modeling for improved empiricism, (iii) generative manifold embedding for high dimensional concurrent design without gradient information; (iv) budget-constrained fidelity-adaptive sampling towards fewer design iterations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Interleaved multi-channel, multi-level, multi-quadrant DC-DC converters

Direct-current-to-direct-current (DC-DC) power converters that include two or more multi-quadrant, multi-level, DC-DC, switching converter subcircuits, connected in parallel at respective input and output sides, so as to provide a multi-channel, multi-quadrant, multi-level configuration, are disclosed. The DC-DC power converters further include a control circuit configured to control the switching converter subcircuits so that corresponding switching semiconductors in each of the switching converter subcircuits are switched in an interleaved manner. In some embodiments, each of the switching converter subcircuits is a three-level, neutral-point-clamped, four-quadrant DC-DC converter circuit. In other embodiments, each of the switching converter subcircuits is a three-level, neutral-point-clamped, two-quadrant DC-DC converter circuit. In any of these embodiments, a filter capacitor may be connected between across a pair of output terminals at the output sides of the switching converter subcircuits.

Du, Yu↗
DOE OSTI2026

Multi-task Parallelism for Robust Pre-training of Graph Foundation Models on Multi-source, Multi-fidelity Atomistic Modeling Data

Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decoding heads that predict data-specific outputs. This approach stabilizes pre-training and enhances a model’s transferability to unexplored chemical regions. Preliminary results on approximately four million structures are encouraging, yet questions remain about generalizability to larger, more diverse datasets and scalability on supercomputers. We propose a multi-task parallelism method that distributes each head across computing resources with GPU acceleration. Implemented in the open-source HydraGNN architecture, our method was trained on over 24 million structures from five datasets and tested on the Perlmutter, Aurora, and Frontier supercomputers, demonstrating efficient scaling on all three highly heterogeneous super-computing architectures.

Lupo Pasini, Massimiliano [ORNL] (ORCID:0000000249↗
DOE OSTI2022

Hardness Prediction by Incorporating Heat Transfer and Molten Pool Fluid Flow in a Multi-pass, Multi-layer Weld for Onsite Repair of Grade 91 steel

Introduction: In the current fleet of fossil-fired power plants, creep strength enhanced ferritic steels (CSEF) are used to sustain the harsh service conditions. Enhanced properties of Grade 91 steel result from tempered martensite with a fine distribution of MX and M23C6 carbides. Grade 91 steel is subjected to onsite welding repair to remedy their degradation due to extreme service condition. Knowledge of weld repairability of these steels, such as as-welded hardness distribution, is essential to establishing sound repair procedures. Experimental trial and error tests can consume a lot of time as many welding variables need to be studied. For numerical modelling, most of the multi-pass multi-layer models are based on finite element method, which are limited to solve the heat conduction equation and ignore convective heat transfer due to melt flow. Moreover, the mesh has to be pre-built based on a known or assumed weld cross-section geometry. These finite element based models thus have limited predictive capability as defects are not considered and nugget size are pre-assumed. This research aims at developing a thermal and microstructure evolution model incorporating molten pool dynamics in a multi-pass multi-layer material deposition to predict the as-welded hardness distribution. Technical Approach: All the thermal, physical, and metallurgical properties of Grade 91 as a function of temperature are collected from the literature and inputted into the thermo-fluid model based on Flow-3D, a computational fluid dynamics software. A multi-pass, multi-layer material deposition is simulated where the melting of filler wire into the molten pool is directly considered based on the volume of fluid (VOF) method. The flow behaviour of the molten pool is used to understand the formation of deposition geometry and defects. The temperature profiles during the multi-pass, multi-layer welding are calculated. The results computed using the new model are compared against the experimental data of fusion zone geometry and thermal cycles. Hardness prediction in the heat-affected zone (HAZ) are made using Johnson-Mehl-Avrami (JMA) equation for solid-state phase transformation kinetics. The JMA parameters are extracted from the experimental data available in the literature. For comparison, a standard finite element heat conduction model is also developed to predict the thermal cycles and hardness distribution in the multi-pass, multi-layer weld. Expected Result: Results obtained using the molten pool dynamic simulation versus the finite element heat conduction model are compared. Specifically, the effects of convective heat transfer on the accuracy of the calculated thermal history, bead shape and size, and HAZ hardness distribution are examined.

Modeling, Grade 91 steel, Multi-pass multi-layer d↗
DOE OSTI2022

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems: Preprint

We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). While many frameworks exist for training multi-agent (MA) RL policies, none exist to rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multi-device) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an open-source software package that helps to fill this gap. To highlight PowerGridworld's key features, we present two case studies and demonstrate learning multi-agent RL policies using both OpenAI's MADDPG and RLLib's PPO algorithms where, in both cases, at least some subset of agents incorporate elements of the power flow solution at each time step as part of their reward (negative cost) structures.

MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing al-location policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

Ranganath, Kiran↗
DOE OSTI2024

Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning

In multi-timescale multi-agent reinforcement learning (MARL), agents interact across different timescales. In general, policies for time-dependent behaviors, such as those induced by multiple timescales, are non-stationary. Learning non-stationary policies is challenging and typically requires sophisticated or inefficient algorithms. Motivated by the prevalence of this control problem in real-world complex systems, we introduce a simple framework for learning non-stationary policies for multi-timescale MARL. Our approach uses available information about agent timescales to define and learn periodic multi-agent policies. In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy. To learn such policies, we propose a policy gradient algorithm that parameterizes the actor and critic with phase-functioned neural networks, which provide an inductive bias for periodicity. The framework's ability to effectively learn multi-timescale policies is validated on a gridworld and building energy management environment.

control↗
DOE OSTI2021

Exact multi-instanton solutions to self-dual Yang–Mills equation on curved spaces

We find exact multi-instanton solutions to the self-dual Yang–Mills equation on a large class of curved spaces with SO(3) isometry, generalizing the results previously found on R 4 . Here, the solutions are featured with explicit multi-centered expressions and topological properties. As examples, we demonstrate the approach on several different curved spaces, including the Einstein static universe and R × dS$^{E}_{3}$, and show that the exact multi-instanton solutions exist on these curved backgrounds.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides

An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides . High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.

09 BIOMASS FUELS↗
DOE OSTI2022

System-on-chip integrated circuit technology applications on the DIII-D tokamak for multi-field measurements

Several mm-wave diagnostics on the DIII-D tokamak provide multi-scale and multi-dimensional measurements of plasma profile evolution and turbulence fluctuations. Mm-wave fusion plasma diagnostics that adopt system-on-chip integrated circuit technology can provide better space utilization, flexible installation, and improved sensitivity. In order to further extend this technology for additional fusion facilities with a higher toroidal magnetic field, V-band (55–75 GHz) and F-band (90–140 GHz) chips for Microwave Imaging Reflectometer (MIR) and Electron Cyclotron Emission Imaging (ECEI) instruments are developed and tested in the Davis Millimeter Wave Research Center (DMRC). Current measurement data show that correlation between these SoC-based diagnostic instruments with other state-of-the-art diagnostics enables co-located multi-field turbulence fluctuation measurement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Trifan, Anda↗
DOE OSTI2023

Nonlinear dynamics, bifurcations, and multi-stability in a vibro-impact system with geometric and multi-segmented freeplay nonlinearities

Freeplay is a common type of piecewise-smooth nonlinearity in dynamical systems, and it can cause discontinuity-induced bifurcations and other behaviors that may bring about undesirable and potentially damaging responses. Prior research has focused on piecewise-smooth systems with two or three distinct regions, but less attention is devoted to systems with more regions (i.e., multi-segmented systems). In this work, numerical analysis is performed on a dynamical system with multi-segmented freeplay, in which there are four stiffness transitions and five distinct regions in the phase space. Here, the effects of the multi-segmented parameters are studied through bifurcation diagram evolution along with induced multi-stable behavior and different bifurcations. These phenomena are interrogated through various tools, such as harmonic balance, basins of attraction, phase planes, and Poincaré section analysis. Results show that among the three multi-segmented parameters, the asymmetry has the strongest effect on the response of the system.

42 ENGINEERING↗
DOE OSTI2020

Shape-constrained Input Estimation for Efficient Multi-shaker Vibration Testing

Multi-shaker vibration testing is gaining interest from structural dynamics test engineers as it can provide a much more accurate match to complicated field vibration responses than traditional single-axis shaker tests. However, the force capabilities of the small modal shakers typically used in multi-shaker vibration tests has limited the achievable response levels. To date, most multi-shaker vibration tests have been performed using a variety of standard, commercially-available control systems. While these control systems are adequate for a wide range of multiple-input/multiple-output tests, their control algorithms have not been tailored for the specific problem of multi-shaker vibration tests: efficiently coordinating the various shakers to work together to achieve a desired response. Here, a new input estimation algorithm is developed and demonstrated using simulations and actual test data. This algorithm, dubbed shape-constrained input estimation, is shown to effectively coordinate multiple shakers using a set of constraint vectors based on the deflection shapes of the test structure. This is accomplished by using the singular vector shapes of the system frequency response matrix, which allows the constraint vectors to automatically change as a function of frequency. Simulation and test results indicate a significant reduction in the input forces required to achieve a desired response. Finally, the results indicate that shape-constrained input estimation is an effective method to achieve higher response levels from limited shaker forces which will enable higher level multi-shaker vibration tests to be performed.

42 ENGINEERING↗