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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Bilevel Nodal Behind-the-meter Solar Disaggregation Under Unexpected Extreme Weather Conditions

As the power grid undergoes significant paradigm shift due to the increasing penetration of renewable generation, the ever-growing installation of behind-the-meter (BTM) solar generation in the power grid also has a significant impact on nodal loads, posing challenges on transmission operators. Furthermore, increasing frequent and severe extreme weather events intertwine with ubiquitous BTM solar generations and have amplified the challenges of accurately model nodal load profiles, especially under the lack of ground-truth information for verification. To tackle these challenges, this paper introduces a bilevel model that utilizes year-long data (e.g., proxy solar, zonal load, and individual node load profiles) to disaggregate metered profiles into actual demand and BTM solar generation at each transmission node. The proxy solar not only scales the BTM solar generation of individual nodes but also create a compensation term for enhancing performance on days with unexpected extreme weather events. The proposed algorithm is validated with real-world PJM Interconnection data during unexpected events like the recent Winter Storm Elliott. For quantitative evaluations, a novel Score error is introduced, which is based on mean percentages and load scales and offers a universal assessment method suitable for all nodes and different data formats (e.g., normalized or raw values).

behind-the-meter solar, load disaggregations, load↗
DOE OSTI2025

PQML: Enabling the Predictive Reproducibility on NISQ Machines for Quantum ML Applications

Quantum computing represents a groundbreaking approach to high-performance computing. In recent years, quantum computers have progressed from single-qubit processors to systems boasting over 400 qubits. The presence of such a large number of qubits offers significant advantages, including enhanced computational speed—a capability beyond classical computing methods. However, the current stage of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era. The existence of noise in this era presents challenges in testing quantum computing applications, leading to considerable variance in application results. Furthermore, the diverse noise characteristics observed across different machines exacerbate this issue, complicating the selection of the appropriate machine for application execution. In response to these challenges, we introduce our Predictive Quantum Machine Learning (PQML) tool. This tool is designed to predict outcomes when executing identical quantum machine learning applications—specifically, a critical suite of variational quantum algorithms—across various quantum computers during the NISQ era. This effort relies on data collected over a 12-month period. To the best of our knowledge, this study represents the first attempt to ensure reproducibility across quantum computers for complex circuits. Additionally, we have developed a model capable of forecasting the accuracy of quantum computers for variational quantum algorithms, with a particular emphasis on quantum machine learning as a case study.

Senapati, Priyabrata [Kent State University]↗
DOE OSTI2025

Visualization of Noisy and Less Noisy Computational Basis States in Quantum Computing

Quantum computing technology holds substantial promise as a reliable computational paradigm. However, current noisy intermediate scale quantum (NISQ) systems, are significantly impacted by noise originating from hardware inconsistencies. This noise causes errors and lowers output fidelity. So we must find which basis states cause errors. However, there are two main challenges in analyzing noise corresponding to basis states. First, the noise distribution data is high dimensional in nature, thereby making its analysis challenging. Second, although functional box plots have been used in the state of the art research to understand such a high dimensional data, they suffer from clutter and occlusion issues because of overplotting. In this study, we introduce an innovative visualization pipeline to address the aforementioned challenges to provide a clear depiction of noisy and less-noisy basis states. Specifically, our proposed visualization pipeline comprises three stages namely, low dimensional embedding, clustering, and violin plot visualization, to reduce visual clutter and effectively analyze high-dimensional noise distribution data. Our analysis uses quantum machine learning (QML) circuits as case study for drawing a distinction between noisy and less noisy basis states.

Senapati, Priyabrata [Kent State University]↗
DOE OSTI2024

Labeling sequential data from noisy annotations

Crowdsourcing algorithms often work under the assumption that the data samples are independent. Recent work has shown that data dependence, such as temporal correlations in sequential data, can be leveraged to improve the label quality. Existing methods that exploit this special structure rely on third-order statistics of the annotator outputs to ensure the identifiability of key latent parameters, which are costly to acquire. This work proposes an approach for integrating crowdsourced annotations under the Dawid-Skene/Hidden Markov Model (DS-HMM) for sequential data based on second-order statistics, which naturally enjoys a lower sample complexity. An effective algorithm is proposed to tackle the challenging optimization problem associated with the proposed estimator. Numerical experiments showcase the effectiveness of the data labeling paradigm.

Marrinan, Timothy P.↗
DOE OSTI2024

Enabling HPC Scientific Workflows for Serverless

The convergence of edge computing, big data analytics, and AI with traditional scientific calculations is increasingly being adopted in HPC workflows. Workflow management systems are crucial for managing and orchestrating these complex computational tasks. However, it is difficult to identify patterns within the growing population of HPC workflows. Serverless has emerged as a novel computing paradigm, offering dynamic resource allocation, quick response time, fine-grained resource management and auto-scaling. In this paper, we propose a framework to enable HPC scientific workflows on serverless. Our approach integrates a widely used traditional HPC workflow generator with an HPC serverless workflow management system to create benchmark suites of scientific workflows with diverse characteristics. These workflows can be executed on different serverless platforms. We comprehensively compare executing workflows on traditional local containers and serverless computing platforms. Our results show that serverless can reduce CPU and memory usage respectively by 78.11% and 73.92% without compromising performance.

Andrei da silva, Anderson↗
DOE OSTI2024

State-of-the-Art Medium- and High-Voltage Silicon Carbide Power Modules, Challenges and Mitigation Techniques: A Review

Silicon carbide (SiC) power modules have been demonstrated potential for improving power density and efficiency for low-voltage power electronics systems. This has resulted in a paradigm shift toward development of medium-voltage and high-voltage (MV/HV) SiC power modules to revolutionize the future power grid and transportation systems. However, designing MV/HV SiC power modules involves significant design challenges due to higher blocking voltage and exacerbation of side effects due to high switching dv/dt and di/dt of SiC devices-concerns that may not be as critical as in low-voltage module development. This article reviews the development of state-of-the-art MV/HV SiC power modules, ranging from 3.3 kV to 40 kV, from both industry and academia. First, a discussion on SiC modules based on voltage level is presented. This is followed by a discussion of challenges associated with designing and testing MV/HV modules- including parasitic controls, electromagnetic interference (EMI), partial discharge, and thermal management-and the corresponding mitigation approaches from various perspectives. We conclude with a summary of major findings and future directions for the development of MV/HV modules.

EMI↗
DOE OSTI2024

Guest Editorial: Special section on Resilient Control of Cyber-Physical Power and Energy Systems

Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats. Finally, understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗
DOE OSTI2026

Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays

This paper presents a multi-agent reinforcement learning (MARL) approach for controlling adjustable metallic reflector arrays to enhance wireless signal reception in non-line-of-sight (NLOS) scenarios. Unlike conventional reconfigurable intelligent surfaces (RIS) that require complex channel estimation, our system employs a centralized training with decentralized execution (CTDE) paradigm where individual agents corresponding to reflector segments autonomously optimize reflector element orientation in three-dimensional space using spatial intelligence based on user location information. Through extensive ray-tracing simulations with dynamic user mobility, the proposed multi-agent beam-focusing framework demonstrates substantial performance improvements over single-agent reinforcement learning baselines, while maintaining rapid adaptation to user movement within one simulation step. Comprehensive evaluation across varying user densities and reflector configurations validates system scalability and robustness. The results demonstrate the potential of learning-based approaches for adaptive wireless propagation control.

deep reinforcement learning↗
DOE OSTI2025

Fast and Invertible Simplicial Approximation of Magnetic‐Following Interpolation for Visualizing Fusion Plasma Simulation Data

We introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.

Ren, Congrong [The Ohio State Univ., Columbus, OH ↗
DOE OSTI2025

Biocrust Mosses and Cyanobacteria Exhibit Distinct Carbon Uptake Responses to Variations in Precipitation Amount and Frequency

ABSTRACT Dryland organisms exhibit varied responses to changes in precipitation, including event size, frequency, and soil moisture duration, influencing carbon uptake and reserve management strategies. This principle, central to the pulse‐reserve paradigm, has not been thoroughly evaluated in biological soil crusts (biocrusts), essential primary producers on dryland surfaces. We conducted two experiments to investigate carbon uptake in biocrusts under different precipitation regimes. In the first, we applied a gradient of watering amounts to biocrusts dominated by moss or cyanobacteria, hypothesising distinct pulse‐response strategies. The second experiment extended watering treatments over three months, varying pulse size and frequency. Our results revealed distinct carbon uptake patterns: moss crusts exhibited increased CO 2 uptake with larger, less frequent watering events, whereas cyanobacteria crusts maintained similar carbon uptake across all event sizes. These findings suggest divergent pulse‐response strategies across biocrust types, with implications for modelling dryland carbon dynamics and informing land management under changing precipitation regimes.

Young, Kristina E. [USDA‐ARS Jornada Experimental ↗
DOE OSTI2025

Which Plant Traits Increase Soil Carbon Sequestration? Empirical Evidence From a Long‐Term Poplar Genetic Diversity Trial

Plants play a key role in mediating soil response to global change, and breeding or engineering crops to increase soil organic carbon (SOC) storage is a potential route to land-based carbon dioxide removal in agricultural systems. However, due to limited observational datasets plus shifting paradigms of SOC stabilization, it is unclear which plant traits are most important for enhancing different types of soil organic matter. Existing long-term common gardens of genetically diverse plant populations may provide an opportunity to evaluate biological controls on SOC, separate from environmental or management variability. Here we report on soil and root chemical data collected for 24 genotypes within a 13-year-old common garden in northwestern Oregon planted with a large natural variant population of Populus trichocarpa. Fractionating surface soil (0–15 cm) revealed substantial variation in stocks of mineral-associated organic matter (MAOM; 18–67 t C/ha) and particulate organic matter (POM; 2–22 t C/ha). Tree genotype explained 24% and 26% of the MAOM and POM stock variability, respectively, after controlling for background variability. We found minimal association between SOC concentration and either aboveground tree productivity or root biomass recalcitrance (C/N ratios and lignin content). In contrast, root elemental content appeared influential for MAOM-C concentration, which showed a strong positive association with root aluminum (Al) and a strong negative association with root boron (B) and magnesium (Mg). Furthermore, root concentrations of these elements were highly heritable (57%–78%) and not simply a reflection of background variation in soil elemental concentrations. We estimate that surface SOC stocks under these 24 genotypes have diverged at rates of up to 1.2–4.3 t C/ha/year. These results suggest that long-term genetic diversity trials have value for elucidating biological controls on soil organic matter dynamics, and that traits associated with root elemental content may be a useful target for enhancing biosequestration.

biomass recalcitrance↗
DOE OSTI2024

Biophysical and Structural Features of αβT ‐Cell Receptor Mechanosensing: A Paradigmatic Shift in Understanding T‐Cell Activation

ABSTRACT αβT cells protect vertebrates against many diseases, optimizing surveillance using mechanical force to distinguish between pathophysiologic cellular alterations and normal self‐constituents. The multi‐subunit αβT‐cell receptor (TCR) operates outside of thermal equilibrium, harvesting energy via physical forces generated by T‐cell motility and actin‐myosin machinery. When a peptide‐bound major histocompatibility complex molecule (pMHC) on an antigen presenting cell is ligated, the αβTCR on the T cell leverages force to form a catch bond, prolonging bond lifetime, and enhancing antigen discrimination. Under load, the αβTCR undergoes reversible structural transitions involving partial unfolding of its clonotypic immunoglobulin‐like (Ig) domains and coupled rearrangements of associated CD3 subunits and structural elements. We postulate that transitions provide critical energy to initiate the signaling cascade via induction of αβTCR quaternary structural rearrangements, associated membrane perturbations, exposure of CD3 ITAMs to phosphorylation by non‐receptor tyrosine kinases, and phase separation of signaling molecules. Understanding force‐mediated signaling by the αβTCR clarifies long‐standing questions regarding αβTCR antigen recognition, specificity and affinity, providing a basis for continued investigation. Future directions include examining atomistic mechanisms of αβTCR signal initiation, performance quality, tissue compliance adaptability, and T‐cell memory fate. The mechanotransduction paradigm will foster improved rational design of T‐cell based vaccines, CAR‐Ts, and adoptive therapies.

Immunology↗
DOE OSTI2024

Performance of chatbots in queries concerning fundamental concepts in photochemistry

Abstract The advent of chatbots raises the possibility of a paradigm shift across society including the most technical of fields with regard to access to information, generation of knowledge, and dissemination of education and training. Photochemistry is a scientific endeavor with roots in chemistry and physics and branches that encompass diverse disciplines ranging from astronomy to zoology. Here, five chatbots have each been challenged with 13 photochemically relevant queries. The chatbots included ChatGPT 3.5, ChatGPT 4.0, Copilot, Gemini Advanced, and Meta AI. The queries encompassed fundamental concepts (e.g., “Why is the fluorescence spectrum typically the mirror image of the absorption spectrum?”), practical matters (e.g., “What is the inner filter effect and how to avoid it?”), philosophical matters (“Please create the most important photochemistry questions.”), and specific molecular features (e.g., “Why are azo dyes non‐fluorescent?”). The chatbots were moderately effective in answering queries concerning fundamental concepts in photochemistry but were glaringly deficient in specialized queries for dyes and fluorophores. In some instances, a correct response was embedded in verbose scientific nonsense whereas in others the entire response, while grammatically correct, was utterly meaningless. The unreliable accuracy makes present chatbots poorly suited for unaided educational purposes and highlights the importance of domain experts.

Taniguchi, Masahiko↗
DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗
DOE OSTI2025

Revisiting the assignment of atomic charges in metal oxides based on core-level x-ray photoelectron spectra: The case of Ti in SrTiO3(001)

We demonstrate that assigning formal charges to transition metal (TM) cations based on core-level (CL) x-ray photoemission binding energies in oxides leads to physically inconsistent pictures of electronic structure. O 2p–TM 3d hybridization is well known to result in significant covalency in TM–O bonds, thereby reducing TM cation charges from their fully ionic values. However, the ionic bonding model remains the working paradigm for assigning TM CL features, and the resulting cation charges are often taken to be representative of the material under study. Here, we show that a more physically meaningful way to assign charges is to extract information about charge distributions utilizing Dirac–Hartree–Fock theory to calculate CL spectra from first principles and then use the resulting wave functions to determine charges based on orbital occupancies. TM cation charges can also be determined using density functional theory and Bader population analysis. We illustrate these two methods using the Ti 2p spectrum for SrTiO3(001) and show that the agreement between them is excellent. Significantly, the resulting Ti charge is considerably lower than the formal charge. The high degree of similarity between the Ti 2p spectrum for SrTiO3 and those for the rutile and anatase polymorphs of TiO2 suggests that the charge densities surrounding Ti in the latter materials are similar to that in SrTiO3. Taking a broader perspective, oxides containing other first-row transition metals also exhibit covalent character, leading to TM cation charges lower than the analogous fully ionic values in these materials as well.

Chambers, Scott A. (ORCID:000000025415043X)↗
DOE OSTI2024

Dispersive nonreciprocity between a qubit and a cavity

The dispersive interaction between a qubit and a cavity is ubiquitous in circuit and cavity quantum electrodynamics. It describes the frequency shift of one quantum mode in response to excitations in the other and, in closed systems, is necessarily bidirectional, i.e., reciprocal. Here, we present an experimental study of a nonreciprocal dispersive-type interaction between a transmon qubit and a superconducting cavity, arising from a common coupling to dissipative intermediary modes with broken time reversal symmetry. We characterize the qubit-cavity dynamics, including asymmetric frequency pulls and photon shot noise dephasing, under varying degrees of nonreciprocity by tuning the magnetic field bias of a ferrite component in situ. We introduce a general master equation model for nonreciprocal interactions in the dispersive regime, providing a compact description of the observed qubit-cavity dynamics agnostic to the intermediary system. Our result provides an example of quantum nonreciprocal phenomena beyond the typical paradigms of non-Hermitian Hamiltonians and cascaded systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

The weak land carbon sink hypothesis

Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (−6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO 2 gradient, and O 2 trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis.

Randerson, James T. [Univ. of California, Irvine, ↗