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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NSTX-U National Research Program: White Paper in Response to Call from FESAC Sub-Committee

Both scientific and technical innovation is needed for the realization of an attractive engineering solution for a timely and cost-effective Pilot Plant, the design and construction of which is the overarching recommendation of the FESAC Long Range Plan, and the 2021 NASEM Pilot Plant reports, which underpin the Bold Decadal Vision. The two most significant plasma physics gaps to close for a Compact Pilot Plant (CPP) are core confinement improvement and heat flux mitigation, neither of which have been closed in an integrated fashion for any planned fusion power production device. High core confinement and stability are essential for producing majority self-driven plasmas in CPPs with reduced size and auxiliary heating power requirements, with an improvement in confinement being the major driver for cost reduction of a CPP. The National Spherical Tokamak Experiment - Upgrade (NSTX-U) is a unique low aspect ratio research facility that will address the fundamental challenge of developing the science and technology basis for a CPP design that integrates high core and edge confinement with the ability to mitigate very high incident heat fluxes. NSTX-U capabilities will enable the high performance, already achieved on NSTX, to extend into physics regimes much closer to those anticipated in Spherical Tokamak (ST)-based CPPs. These confinement and stability properties will be assessed by a full complement of diagnostics and analysis tools, which will also aid in the development of the underlying theory and predictive models needed for further optimization. Both conventional and transformative heat flux mitigation methods, such as liquid lithium plasma-facing components, will be developed and tested in-situ in NSTX-U at incident heat fluxes of ~100 MW/m 2 , and will inform plans and reduce risk for a subsequent major upgrade to the device to fully heated, high-Z wall and full liquid lithium divertor capability, a technology that potentially could then be implemented on any magnetic confinement device at any aspect ratio. NSTX-U research is fully complementary to programs performed on other STs, nationally and internationally. Furthermore, NSTX-U research has a direct connection to the private sector by informing design choices for future power production facilities being developed by these companies. The NSTX-U program will operate as a national User Facility, with collaborating researchers, engineers, and graduate students from 19 outside institutions, and open to participation and experiments led by researchers from both public and private entities. The research program will advance workforce development through training of young scientists, engineers, and technicians, and it will also serve for further diagnostic innovation, especially for high heat flux and high-Z wall environments, and implementation of advanced artificial intelligence (AI) for plasma and heat flux control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Quantum Computing for AI-based Design and Optimization of Electric Motors

Knowledge-based artificial intelligence and hierarchical fuzzy logic offer an interpretable framework for electricvehicle motor preliminary design, but their computational burden grows with linguistic granularity and coupled design-space size. This paper presents a reduced quantum reformulation of the hierarchical fuzzy inference of air-gap flux density, a representative level-one motor-design parameter. Starting from the published electric-vehicle motor-design framework, a three-term fuzzy prototype is constructed from the original inference structure. The reduced model is then reformulated as a modular quantum register-oracle system, in which each hierarchical subrelation is encoded as a block oracle and evaluated through superpositionbased candidate-label testing. The proposed modular quantum formulation reproduces the reduced classical prototype after block fusion. A resource analysis shows that the reduced modular system requires seven qubits per block and twenty-two qubits in a straightforward four-block implementation. Finally, a crossovercomplexity model is derived to identify the regime in which quantum candidate search may become favorable relative to hierarchical fuzzy inference. The results show that no quantum advantage should be claimed for the present one-output reduced benchmark, but that a plausible crossover emerges for larger joint candidate spaces and higher linguistic granularity. The work therefore establishes a technically consistent starting point for future quantum-assisted electric-vehicle motor-design optimization.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
DOE OSTI2024

Determination of the Relative Sign of the Higgs Boson Couplings to W and Z Bosons Using WH Production via Vector-Boson Fusion with the ATLAS Detector

The associated production of Higgs and W bosons via vector-boson fusion is highly sensitive to the relative sign of the Higgs boson couplings to W and Z bosons. In this Letter, two searches for this process are presented, using 140 fb -1 of proton-proton collision data at $\sqrt{s} =$ 13 TeV recorded by the ATLAS detector at the LHC. The first search targets scenarios with opposite-sign couplings of the W and Z bosons to the Higgs boson, while the second targets standard model-like scenarios with same-sign couplings. Both analyses consider Higgs boson decays into a pair of b quarks and W boson decays with an electron or muon. The data exclude the opposite-sign coupling hypothesis with a significance beyond 5⁢σ, and the observed (expected) upper limit set on the cross section for vector-boson fusion W⁢H production is 9.0 (8.7) times the standard model value at 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗
DOE OSTI2024

Test of CP-invariance of the Higgs boson in vector-boson fusion production and in its decay into four leptons

A search for CP violation in the decay kinematics and vector-boson fusion production of the Higgs boson is performed in the H → ZZ * → 4 ℓ ( ℓ = e, μ ) decay channel. The results are based on proton-proton collision data produced at the LHC at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector from 2015 to 2018, corresponding to an integrated luminosity of 139 fb –1 . Matrix element-based optimal observables are used to constrain CP-odd couplings beyond the Standard Model in the framework of Standard Model effective field theory expressed in the Warsaw and Higgs bases. Differential fiducial cross-section measurements of the optimal observables are also performed, and a new fiducial cross-section measurement for vector-boson-fusion production is provided. All measurements are in agreement with the Standard Model prediction of a CP-even Higgs boson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. Here, the performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

Bounded domains↗
DOE OSTI2024

Analysis of Waste Material Feedstocks Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Predicting properties such as heating value, ash fusion temperature, and mineral ash composition from Laser-Induced Breakdown Spectroscopy (LIBS) data can make gasifiers more flexible to different feedstocks. Understanding these feedstock properties in-situ improves feedstock conversion modelling methods that allow for consistent operation, higher carbon conversion, and reduced fouling and erosion rates. The purpose of this study is to demonstrate methods for model creation that take LIBS data as predictor features and estimate higher order material properties as a function of feedstock material properties. Six samples were chosen to represent a mixture of abundant and carbon rich waste materials. LIBS measurements were performed on these samples for elemental wavelengths and intensity values. Laboratory analytical results were obtained for each sample’s heating value, proximate and ultimate analysis, mineral ash composition, ash fusion temperatures, and viscosity temperatures. Thermal conductivity was measured using a HotDisk TPS 2500S. LIBS measurements were processed and used as predictor features for machine learning (ML) models to predict the sample’s material properties. Predictor feature selection algorithms, particularly minimum redundancy maximum relevance (mRMR), reduced the dimensionality of ML models. Many modelling methods such as Gaussian process regression (GPR), regression tree, neural networks (NN), and support vector machines (SVM) were demonstrated to be effective at predicting higher order properties; however, mRMR with GPR stood out as a clear winning combination.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

EFIT‐AI: Machine Learning and Artificial Intelligence Assisted Equilibrium Reconstruction for Tokamak Experiments and Burning Plasmas (Final Report)

The EFIT-AI project is creating a modern advanced equilibrium reconstruction code suitable for tokamak experiments of burning plasmas. EFIT [1,2] was the first and is the most extensively used equilibrium reconstruction code in the world. This project builds on the production-level experience and adds key elements as follows. 1. A Model Order Reduction (MOR) version of the two-dimensional (2D) Grad-Shafranov equation solver (EFIT-MORNN) using physics-informed neural networks. 2. Improved optimization and data analysis capabilities using a Bayesian framework enhanced with machine learning. 3. A MOR version of the three-dimensional (3D) perturbed equilibrium reconstruction tool.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Search for Higgs bosons produced in association with a high-energy photon via vector-boson fusion and decaying to a pair of b -quarks in the ATLAS detector

A search for Standard Model Higgs bosons produced in association with a high-energy photon and decaying to $b\bar{b}$ is performed using 133 fb −1 of $\sqrt{s}$ = 13 TeV pp collision data collected with the ATLAS detector at the Large Hadron Collider at CERN. The photon requirement reduces the multijet background, and the H → $b\bar{b}$ decay is the dominant decay mode. Event selection requirements target events produced by vector-boson fusion, the dominant production mode in this channel. Several improvements enhance the search sensitivity compared to previous measurements. These improvements include better background modelling and characterization, the use of a neural-network classifier, and an updated signal extraction strategy adopting a direct binned-likelihood fit to the classifier output. With these improvements, the Higgs boson signal strength is measured to be 0.2 ± 0.7 relative to the Standard Model prediction. This corresponds to an observed significance of 0.3 standard deviations, compared to an expectation of 1.5 standard deviations assuming the Standard Model.

ATLAS experiment↗
DOE OSTI2024

Search for light long-lived neutral particles from Higgs boson decays via vector-boson-fusion production from pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

A search is reported for long-lived dark photons with masses between 0.1 GeV and 15 GeV, from exotic decays of Higgs bosons produced via vector-boson-fusion. Events that contain displaced collimated Standard Model fermions reconstructed in the calorimeter or muon spectrometer are probed. This search uses the full LHC Run 2 (2015–2018) data sample collected in proton–proton collisions at $\sqrt{s}=13$ TeV, corresponding to an integrated luminosity of 139 fb –1 . Dominant backgrounds from Standard Model processes and non-collision sources are estimated using data-driven techniques. The observed event yields in the signal regions are consistent with the expected background. Upper limits on the Higgs boson to dark photon branching fraction are reported as a function of the dark photon mean proper decay length or of the dark photon mass and the coupling between the Standard Model and the potential dark sector. This search is combined with previous ATLAS searches obtained in the gluon–gluon fusion and WH production modes. A branching fraction above 10% is excluded at 95% CL for a 125 GeV Higgs boson decaying into two dark photons for dark photon mean proper decay lengths between 173 and 1296 mm and mass of 10 GeV.

Aad, G. (ORCID:0000000266654934)↗
DOE OSTI2024

Search for pair production of boosted Higgs bosons via vector-boson fusion in the $b$$\overline{b}$$b$$\overline{b}$ final state using $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for Higgs boson pair production via vector-boson fusion is performed in the Lorentz-boosted regime, where a Higgs boson candidate is reconstructed as a single large-radius jet, using 140 fb−1 of proton–proton collision data at $\sqrt{s}$ = 13 TeV recorded by the ATLAS detector at the Large Hadron Collider. Only Higgs boson decays into bottom quark pairs are considered. The search is particularly sensitive to the quartic coupling between two vector bosons and two Higgs bosons relative to its Standard Model prediction, K 2V . This study constrains K 2V to 0.55 < K 2V < 1.49 at the 95% confidence level. The value K 2V = 0 is excluded with a significance of 3.8 standard deviations with other Higgs boson couplings fixed to their Standard Model values. A search for new heavy spin-0 resonances that would mediate Higgs boson pair production via vector-boson fusion is carried out in the mass range of 1–5 TeV for the first time under several model and decay-width assumptions. No significant deviation from the Standard Model hypothesis is observed and exclusion limits at the 95% confidence level are derived.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Search for a light CP-odd Higgs boson decaying into a pair of τ -leptons in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

This paper reports a search for a light CP-odd scalar resonance with a mass of 20 GeV to 90 GeV in 13 TeV proton-proton collision data with an integrated luminosity of 140 fb -1 collected with the ATLAS detector at the Large Hadron Collider. The analysis assumes the resonance is produced via gluon-gluon fusion and decays into a $τ^+ τ^-$ pair which subsequently decays into a fully leptonic $µ^+ ν_µ \overline{ν}_τ e^- \overline{ν}_ e ν_τ$ or $e^+ ν_e \overline{ν}_τ µ^- \overline{ν}_µ ν_τ$ final state. No significant excess of events above the predicted Standard Model background is observed. The results are interpreted within a favour-aligned two-Higgs-doublet model, and a model independent cross-section interpretation is also given. Upper limits at 95% confidence level between 3.0 pb and 68 pb are set on the cross-section for producing a CP-odd Higgs boson that decays into a $τ^+ τ^-$ pair.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Differential cross-sections for events with missing transverse momentum and jets measured with the ATLAS detector in 13 TeV proton-proton collisions

Measurements of inclusive, differential cross-sections for the production of events with missing transverse momentum in association with jets in proton-proton collisions at √s = 13 TeV are presented. The measurements are made with the ATLAS detector using an integrated luminosity of 140 fb –1 and include measurements of dijet distributions in a region in which vector-boson fusion processes are enhanced. They are unfolded to correct for detector resolution and efficiency within the fiducial acceptance, and are designed to allow robust comparisons with a wide range of theoretical predictions. A measurement of differential cross sections for the Z → νν process is made. The measurements are generally well-described by Standard Model predictions except for the dijet invariant mass distribution. Auxiliary measurements of the hadronic system recoiling against isolated leptons, and photons, are also made in the same phase space. Ratios between the measured distributions are then derived, to take advantage of cancellations in modelling effects and some of the major systematic uncertainties. These measurements are sensitive to new phenomena, and provide a mechanism to easily set constraints on phenomenological models. To illustrate the robustness of the approach, these ratios are compared with two common Dark Matter models, where the constraints derived from the measurement are comparable to those set by dedicated detector-level searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Combination of searches for singly and doubly charged Higgs bosons produced via vector-boson fusion in proton–proton collisions at s = 13 TeV with the ATLAS detector

A combination of searches for singly and doubly charged Higgs bosons, H ± and H ±± , produced via vector-boson fusion is performed using 140 fb -1 of proton–proton collisions at a centre-of-mass energy of 13 TeV, collected with the ATLAS detector during Run 2 of the Large Hadron Collider. Searches targeting decays to massive vector bosons in leptonic final states (electrons or muons) are considered. New constraints are reported on the production cross-section times branching fraction for charged Higgs boson masses between 200 GeV and 3000 GeV. The results are interpreted in the context of the Georgi-Machacek model for which the most stringent constraints to date are set for the masses considered in the combination.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Search for heavy resonances in final states with four leptons and missing transverse momentum or jets in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for a new heavy boson produced via gluon-fusion in the four-lepton channel with missing transverse momentum or jets is performed. The search uses proton-proton collision data equivalent to an integrated luminosity of 139 fb -1 at a centre-of-mass energy of 13 TeV collected by the ATLAS detector between 2015 and 2018 at the Large Hadron Collider. This study explores the decays of heavy bosons: R → SH and A → ZH, where R is a CP-even boson, A is a CP-odd boson, H is a CP-even boson, and S is considered to decay into invisible particles that are candidates for dark matter. In these processes, S → invisible and H → ZZ. The Z boson associated with the heavy scalar boson H decays into all decay channels of the Z boson. The mass range under consideration is 390–1300 (320–1300) GeV for the R (A) boson and 220–1000 GeV for the H boson. No significant deviation from the Standard Model backgrounds is observed. The results are interpreted as upper limits at a 95% confidence level on the cross-section times the branching ratio of the heavy resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Constraint on the total width of the Higgs boson from Higgs boson and four-top-quark measurements in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

This Letter presents a constraint on the total width of the Higgs boson (Γ H ) using a combined measurement of on-shell Higgs boson production and the production of four top quarks, which involves contributions from off-shell Higgs boson-mediated processes. This method relies on the assumption that the tree-level Higgs-top Yukawa coupling strength is the same for on-shell and off-shell Higgs boson production processes, thereby avoiding any assumptions about the relationship between on-shell and off-shell gluon fusion Higgs production rates, which were central to previous measurements. The result is based on up to 140 fb -1 of proton–proton collisions at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected with the ATLAS detector at the Large Hadron Collider. The observed (expected) 95% confidence level upper limit on Γ H is 450 MeV (75 MeV). Additionally, considering the constraint on the Higgs-top Yukawa coupling from loop-induced Higgs boson production and decay processes further yields an observed (expected) upper limit of 160 MeV (55 MeV).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗