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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-GPU immersed boundary method hemodynamics simulations

Large-scale simulations of blood flow that resolve the 3D deformation of each comprising cell are increasingly popular owing to algorithmic developments in conjunction with advances in compute capability. Among different approaches for modeling cell-resolved hemodynamics, fluid structure interaction (FSI) algorithms based on the immersed boundary method are frequently employed for coupling separate solvers for the background fluid and the cells within one framework. GPUs can accelerate these simulations; however, both current pre-exascale and future exascale CPU-GPU heterogeneous systems face communication challenges critical to performance and scalability. In this paper, we describe, to our knowledge, the largest distributed GPU-accelerated FSI simulations of high hematocrit cell-resolved flows with over 17 million red blood cells. We compare scaling on a fat node system with six GPUs per node and on a system with a single GPU per node. Through comparison between the CPU- and GPU-based implementations, we identify the costs of data movement in multiscale multi-grid FSI simulations on heterogeneous systems and show it to be the greatest performance bottleneck on the GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Search for Higgs boson production at high transverse momentum in the WW decay channel in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for Higgs boson (H) production at high transverse momentum (p T ) in the WW decay channel is presented. The analysis uses proton-proton collisions at $\sqrt{s}=13$ TeV recorded by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb −1 . The visible decay products of the Higgs boson are reconstructed as a single large-radius jet with one isolated lepton or none (1ℓ and 0ℓ, respectively; ℓ = e, μ). The H-candidate jets are identified using an advanced transformer-based algorithm and are calibrated with the Lund jet plane reweighting technique. The 1ℓ channel is further split into gluon fusion, vector boson fusion, and associated production with hadronically decaying vector boson categories, while the 0ℓ channel considers all production processes inclusively. The measured cross section times the H → WW branching fraction relative to the standard model expectation is $\mu =-{0.19}_{-0.46}^{+0.48}$, indicating no evidence of a signal above the background. This measurement represents the first dedicated study of highly Lorentz-boosted H → WW decays, complementing earlier searches for high-p T Higgs boson in other decay channels.

Hadron-Hadron Scattering↗
DOE OSTI2023

Ten questions concerning reinforcement learning for building energy management

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. Here, in this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2021

Mechanisms of Dynamic Near-Wake Modulation of a Utility-Scale Wind Turbine

The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g. blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviours is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The time scales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different time scales are explored. Under short time scales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and time scale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a time scale of the order of the rotor time scale or slower.

flow-structure interactions↗
DOE OSTI2021

Computational Framework for Machine-Learning-Enabled 13 C Fluxomics

13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

13C metabolic flux analysis↗
DOE OSTI2022

Improving DFIG performance under fault scenarios through evolutionary reinforcement learning based control

The double-fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage during system fault events. In this paper, a novel data-driven approach is proposed to enhance the DFIG performance under fault scenarios. An advanced reinforcement learning (RL) algorithm called guided surrogate-gradient-based evolution strategy (GSES) is used to control the DFIG power and capacitor DClink voltage by adjusting the optional reference signals. This controller is able to prevent the rotor of DFIG from overcurrent risk and maintain the grid-connected operation. The proposed GSES-based control algorithm was evaluated through simulations on a 3.6-MW DFIG in PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSESbased control algorithm in improving the DFIG performance under fault scenarios.

42 ENGINEERING↗
DOE OSTI2023

Dyson maps and unitary evolution for Maxwell equations in tensor dielectric media

The propagation and scattering of electromagnetic waves in dielectric media is of theoretical and experimental interest in a wide variety of fields. An understanding of observational results generally requires a numerical solution of Maxwell equations—usually implemented on conventional computers using sophisticated numerical algorithms. In recent years, advances in quantum information science and in the development of quantum computers have piqued curiosity about taking advantage of these resources for an alternate numerical approach to Maxwell equations. This requires a reformulation of the classical Maxwell equations into a form suitable for quantum computers which, unlike conventional computers, are limited to unitary operations. In this paper, a unitary framework is developed for the propagation of electromagnetic waves in a spatially inhomogeneous, passive, nondispersive, and anisotropic dielectric medium. For such a medium, generally, the evolution operator in the combined Faraday-Ampere equations is not unitary. There are two steps needed to convert this equation into a unitary evolution equation. In the first step, a weighted Hilbert space is formulated in which the generator of dynamics is a pseudo-Hermitian operator. In the second step, a Dyson map is constructed which maps the weighted-physical-Hilbert space to the original Hilbert space. Furthermore, the resulting evolution equation for the electromagnetic wave fields is unitary. Utilizing the framework developed in these steps, a unitary evolution equation is derived for electromagnetic wave propagation in a uniaxial dielectric medium. The resulting form is suitable for quantum computing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Observation of 𝑡⁢𝑊⁢𝑍 Production at the CMS Experiment

The first observation of single top quark production in association with a 𝑊 and a 𝑍 boson in proton-proton collisions is reported. The analysis uses data at center-of-mass energies of 13 and 13.6 TeV recorded with the CMS detector at the CERN LHC, corresponding to a total integrated luminosity of 200 fb −1 . Events with three or four charged leptons, which can be electrons or muons, are selected. Advanced machine-learning algorithms and improved reconstruction methods, compared to an earlier analysis, result in an unprecedented sensitivity to 𝑡⁢𝑊⁢𝑍 production. The measured cross sections for 𝑡⁢𝑊⁢𝑍 production are 248 ± 52 fb and 242 ± 77 fb for $\sqrt{s}$ =13 and 13.6 TeV, respectively. The signal is established with a statistical significance of 5.8 standard deviations, with 3.5 expected, compared to the background-only hypothesis.

Hayrapetyan, Aram [Yerevan Physics Institute]↗
DOE OSTI2024

Topological grain boundary segregation transitions

Engineering the structure of grain boundaries (GBs) by solute segregation is a promising strategy to tailor the properties of polycrystalline materials. Solute segregation triggering phase transitions at GBs has been suggested theoretically to offer different pathways to design interfaces, but an understanding of their intrinsic atomistic nature is missing. Here, we combined atomic resolution electron microscopy and atomistic simulations to discover that iron segregation to GBs in titanium stabilizes icosahedral units (“cages”) that form robust building blocks of distinct GB phases. Owing to their five-fold symmetry, the iron cages cluster and assemble into hierarchical GB phases characterized by a different number and arrangement of the constituent icosahedral units. Our advanced GB structure prediction algorithms and atomistic simulations validate the stability of these observed phases and the high excess of iron at the GB that is accommodated by the phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Research & Experimental Medicine↗
DOE OSTI2023

BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes

Background: Exploring metagenomic contigs and “binning” them into metagenome-assembled genomes (MAGs) are essential for the delineation of functional and evolutionary guilds within microbial communities. Despite the advances in automated binning algorithms, their capabilities in recovering MAGs with accuracy and biological relevance are so far limited. Researchers often find that human involvement is necessary to achieve representative binning results. This manual process however is expertise demanding and labor intensive, and it deserves to be supported by software infrastructure. Results: We present BinaRena, a comprehensive and versatile graphic interface dedicated to aiding human operators to explore metagenome assemblies via customizable visualization and to associate contigs with bins. Contigs are rendered as an interactive scatter plot based on various data types, including sequence metrics, coverage profiles, taxonomic assignments, and functional annotations. Various contig-level operations are permitted, such as selection, masking, highlighting, focusing, and searching. Binning plans can be conveniently edited, inspected, and compared visually or using metrics including silhouette coefficient and adjusted Rand index. Completeness and contamination of user-selected contigs can be calculated in real time. In demonstration of BinaRena’s usability, we show that it facilitated biological pattern discovery, hypothesis generation, and bin refinement in a complex tropical peatland metagenome. It enabled isolation of pathogenic genomes within closely related populations from the gut microbiota of diarrheal human subjects. It significantly improved overall binning quality after curating results of automated binners using a simulated marine dataset. Conclusions: BinaRena is an installation-free, dependency-free, client-end web application that operates directly in any modern web browser, facilitating ease of deployment and accessibility for researchers of all skill levels. The program is hosted at https://github.com/qiyunlab/binarena, together with documentation, tutorials, example data, and a live demo. It effectively supports human researchers in intuitive interpretation and fine tuning of metagenomic data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Status Report on the INL IES Plug-and-Play Framework

This report discusses the advancements and status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and usage, which aims to ease the sharing and simulation of complex dynamic models. This report discusses the FMI/FMU development advancements, overall focusing on the deployment of methodologies for RAVEN to export and use FMI/FMU of both Python-based models and advanced AI-constructed algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Optimal Operation and Impact Assessment of Distributed Wind for Improving Efficiency and Resilience of Rural Electricity Systems

This project aims to empower rural utilities by developing advanced optimization models and algorithms for effectively integrating distributed wind energy alongside battery storage and other distributed energy resources (DERs). The primary objectives are to reduce peak demand, ensure reliable emergency power supply, and regulate voltage and frequency. To address operational challenges, the project introduces innovative mitigation strategies and ultrafast assessment frameworks to evaluate the impacts of distributed wind and DERs on rural grids, offering actionable solutions to potential issues. Economic viability is assessed through cost-benefit analysis using real rural utility data, ensuring the practical application of the project outcomes.

17 WIND ENERGY↗
DOE OSTI2022

Incipient Sensor Fault Impacts on Building Performance Through HVAC Controls: A Pilot Study

Sensors are crucial input components for HVAC controls. Studies show faults are common for buidings and HVAC systems. Sensors with faults will compromise the control perfrormance regardless how advanced of the control algorithms. Majority studies assume the sensor fault to be constant the whole year. In reality, the sensor faults might evolve or develop with time, which is essentially the incipient (i.e. evolving) fault. The incipient sensor faults impacts remain a research gap. This study aims to investigate the incipient sensor fault impacts to control sequences of multi-zone VAV boxes and AHU system following the ASHRAE Guideline 36-2018: High-Performance Sequences of Operation.

Li, Yanfei↗
DOE OSTI2021

Scalable Approaches to Selecting Key Entities in Large Networked Infrastructure Systems

This work aims at bringing advances in discrete optimization algorithms to solving practical engineering problems at scale. Often times, in many engineering design problems, there is a need to select a small set of influential or representative elements from a large ground set of entities in an optimal fashion. Submodular optimization provides for a formal way to solve such problems. Common examples with infrastructure systems involve sensor placement and identification of key entities with certain objectives. However, scaling these approaches to large infrastructure systems can be challenging because of the high computational complexity of the overall framework that include the optimization algorithms as well as high-complexity compute-oracles that provide the necessary objective function values. In this work, we explore a well-studied and widely-applicable paradigm, namely leader-selection in a multi-agent networked setting in the context of scalable methodologies. We demonstrate novel frameworks that utilize variations of accelerated submodular optimization algorithms along with linear-algebraic methods that can help accelerate the oracle computations. We further explore this combination in conjunction with graph partitioning paradigms to take advantage of the accelerated algorithms in a distributed setting. Finally we demonstrate the key findings on a practical problem in an operational setting. For this, we leverage an example road network with approximately 18k nodes and 27k edges in a traffic control application, where we seek a limited number of k=200 key intersections. This problem can be solved in a serial setting in just under 5 hours providing more than 2 orders of magnitude speed-up over methods that do not consider acceleration techniques.

Visweswara Sathanur, Arun↗
DOE OSTI2020

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXI

This volume contains 12 sessions, 28 papers and 26 presentations on the following topics: Photonic Integrated Circuits and Plasmonic Sensing; Advancs in CBE Signature Modeling and Sensor Algorithms I; Standoff Sensing of CBRNE Hazards I; Standoff Sensing of CBRNE Hazards II; Advances in CBE Signature Modeling and Sensor Algorithms II; Biological Hazard Sensing; Chemical Hazard Sensing; Joint Session with Conferences 11390 and 11416: Spectroscopy for CBRNE Detection and Warning; Radiological and Nuclear Sensing; Explosive Material Sensing; Poster Session.

47 OTHER INSTRUMENTATION↗