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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A micromorphic filter for determining stress and deformation measures from direct numerical simulations of lower length scale behavior

A micromorphic filter is presented for the extraction of stress and deformation measures from underlying direct numerical simulation (DNS) of microstructural mechanical response. The filter is consistent with the micromorphic continuum theory of Eringen and Suhubi (1964), and allows the interrogation of a DNS to generate higher order constitutive models. The filter adopts aspects of the overlap coupling techniques for atomistic-continuum scale-bridging in order to update the nodal degrees of freedom of the finite-element-based filter. Here, two methods for the extraction of quantities of interest from the underlying DNS are presented. The first is a least squares fitting technique which is appropriate for finite-element-style macro-scale domains, and the second is a variationally based approach which generalizes the method to any macro-scale domain which has a variational form. No restrictions on the form of the micro-scale simulation are inherent to the approach, provided that the required quantities (stress, position, etc.) can be provided. The filter is applied to a simple homogeneous medium undergoing homogeneous deformation to demonstrate the recovery of a classical continuum response, what we call the null case. We then apply the framework to the same homogeneous medium with a central and edge penny-shaped crack to show micromorphic effects. We demonstrate that varying the size of the filter causes the micromorphic behaviors to arise and then dissipate as the filter becomes smaller than the crack's length scale. Additionally we show the effect of filter size on a chiral element network along with a plate perforated with evenly spaced and perturbed holes through the thickness. The micromorphic effects are shown to be present for the chiral elements under any loading but become most pronounced when a single element is located within each averaging domain. Micromorphic effects become pronounced for the plate upon the onset of localization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Optical Absorption Spectroscopy Probes Water Wire and Its Ordering in a Hydrogen-Bond Network

Water wires, quasi-one-dimensional chains composed of hydrogen-bonded (H-bonded) water molecules, play a fundamental role in numerous chemical, physical, and physiological processes. Yet direct experimental detection of water wires has been elusive so far. Based on advanced many-body theory that includes electron-hole interactions, we report that optical absorption spectroscopy can serve as a sensitive probe of water wires and their ordering. In both liquid and solid water, the main peak of the spectrum is discovered to be a charge-transfer exciton. In water, the charge-transfer exciton is strongly coupled to the H-bonding environment where the exciton is excited between H-bonded water molecules with a large spectral intensity. In regular ice, the spectral weight of the charge-transfer exciton is enhanced by a collective excitation occurring on proton-ordered water wires, whose spectral intensity scales with the ordering length of water wire. The spectral intensity and excitonic interaction strength reaches its maximum in ice XI, where the long-range ordering length yields the most pronounced spectral signal. Our findings suggest that water wires, which widely exist in important physiological and biological systems and other phases of ice, can be directly probed by this approach. Published by the American Physical Society 2025

Tang, Fujie (ORCID:0000000197615359)↗
DOE OSTI2024

DECOVALEX-2023: Task F2 Salt Final Report

The subject of Task F of DECOVALEX-2023 concerns performance assessment modelling of radioactive waste disposal in deep mined repositories. The primary objectives of Task F are to build confidence in the models, methods, and software used for performance assessment (PA) of deep geologic nuclear waste repositories, and/or to bring to the fore additional research and development needed to improve PA methodologies. In Task F2- (salt), these objectives have been accomplished through staged development and comparison of the models and methods used by participating teams in their PA frameworks. Coupled-process submodels and deterministic simulations of the entire PA model for a reference scenario for waste disposal in domal salt have been conducted. The task specification has been updated continuously since the initiation of the project to reflect the staged development of the conceptual repository model and performance metrics. Thermal, hydrological, mechanical, and chemical properties of individual components of the engineered and natural system were chosen for relevance by participating teams. The salt reference case system was characterized using data and measurements collected at relevant underground research laboratories (URLs), field sites, and simulation results from teams with specialized modelling capability. Participating teams made a wide range of model assumptions from compartmentalized networks to full 3D models of the salt formation. No single contributed model includes full-fidelity representation of all the features, events, and processes (FEPs) detailed in the task specification, but almost all features and processes are represented in at least one model. Despite differences in the modelling strategies developed by participating teams, all models indicate that salt compaction and radionuclide diffusion are key processes in the repository, and for the FEPs and model scenario considered, little of the disposed radionuclides will migrate beyond the repository seal over the 100,000 year simulations. In general, the model output quantities have the largest differences over the short term and near the waste. The models tend to be more similar further from waste and at later time. Disparities between the models are believed to be due to differing simplifications from the task specification, some of which are chosen simplifications to reduce complexity, and some are restrictions imposed by the modelling tools. A second round of this task has been accepted for DECOVALEX-2027 in conjunction with Task F1 on crystalline PA modelling. The future round includes waste package heating, improved modelling of salt creep closure, additional comparisons of coupled-process sub-models, and the impact of repository engineering design on radionuclide migration in the repository. Participants will also propose and finalize a set of uncertain inputs for the reference case simulations, propagate these uncertainties in a set of realizations, and conduct sensitivity analyses on the simulation results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Electrochemical Control of Magnetism on the Breathing Kagome Network of Li x ScMo 3 O 8

Controlling properties within a given functional inorganic material structure type is often accomplished through tuning the electronic occupation, which is in turn dictated by the elemental composition determined at the time of material preparation. We employ electrochemical control of the lithium content, with associated electronic occupancy control, to vary the magnetic properties of a material where a kagome-derived network of Mo 3 triangles carry the spin. In this case, Li is electrochemically inserted into LiScMo 3 O 8 , a layered compound containing a breathing Mo kagome network. Up to two additional Li can be inserted into LiScMo 3 O 8 , transforming it into Li 3 ScMo 3 O 8 . Li 2 ScMo 3 O 8 prepared by electrochemical lithiation is compared to the quantum spin liquid candidate compound Li 2 ScMo 3 O 8 prepared through high-temperature solid-state methods, which has a slightly different structural stacking sequence but a similar kagome-derived network. Magnetic measurements are supported by first-principles calculations, showing that electrons remain localized on the Mo clusters throughout the doping series. As x is varied in Li x ScMo 3 O 8 , the measurements and calculations reveal the evolution from a diamagnetic band insulator at x = 1 to a geometrically frustrated magnet at x = 2, back to a diamagnetic insulator at x = 3. These results indicate a likelihood of strong coupling between the degree of Li disorder and charge/magnetic ordering over the Mo 3 clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Learning emergent partial differential equations in a learned emergent space

We propose an approach to learn effective evolution equations for large systems of interacting agents. This is demonstrated on two examples, a well-studied system of coupled normal form oscillators and a biologically motivated example of coupled Hodgkin-Huxley-like neurons. For such types of systems there is no obvious space coordinate in which to learn effective evolution laws in the form of partial differential equations. In our approach, we accomplish this by learning embedding coordinates from the time series data of the system using manifold learning as a first step. In these emergent coordinates, we then show how one can learn effective partial differential equations, using neural networks, that do not only reproduce the dynamics of the oscillator ensemble, but also capture the collective bifurcations when system parameters vary. The proposed approach thus integrates the automatic, data-driven extraction of emergent space coordinates parametrizing the agent dynamics, with machine-learning assisted identification of an emergent PDE description of the dynamics in this parametrization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Coherent Coupling of Two Remote Magnonic Resonators Mediated by Superconducting Circuits

We demonstrate microwave-mediated distant magnon-magnon coupling on a superconducting circuit platform, incorporating chip-mounted single-crystal Y 3 Fe 5 O 12 (YIG) spheres. Coherent level repulsion and dissipative level attraction between the magnon modes of the two YIG spheres are demonstrated. The former is mediated by cavity photons of a superconducting resonator, and the latter is mediated by propagating photons of a coplanar waveguide. Overall, our results open new avenues toward exploring integrated hybrid magnonic networks for coherent information processing on a quantum-compatible superconducting platform.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

KIPM Detector Characterization in QUIET

Kinetic Inductance Phonon‑Mediated (KIPM) detectors are superconducting microwave resonators on silicon that sense bursts of energy through tiny shifts in their resonance. In this work, we installed new devices in the QUIET facility's cryogenic test setup and used a network analyzer to drive them across a range of power levels. At each setting, we measured the transmitted signal and fit it to a simple notch‑filter model, allowing us to extract key performance metrics. And despite the increased noise at low power, our model consistently captured the resonance behavior. We observed that the resonant frequency stayed stable across most powers, the device s internal quality factor improved with stronger signals, and its coupling strength peaked mid‑range before leveling off. Overall, these findings confirm the detector s performance, laying groundwork for future work on energy calibration with pulsed optical sources.

Savitala, Akash [Washington U., Seattle; Fermilab]↗
DOE OSTI2020

Highly Tunable Thiol-Ene Photoresins for Volumetric Additive Manufacturing

Volumetric additive manufacturing (VAM) forms complete 3D objects in a single photocuring operation without layering defects, enabling 3D printed polymer parts with mechanical properties similar to their bulk material counterparts. This study presents the first report of VAM-printed thiol-ene resins. With well-ordered molecular networks, thiol-ene chemistry accesses polymer materials with a wide range of mechanical properties, moving VAM beyond the limitations of commonly used acrylate formulations. Since free-radical thiol-ene polymerization is not inhibited by oxygen, the nonlinear threshold response required in VAM is introduced by incorporating 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) as a radical scavenger. Tuning of the reaction kinetics is accomplished by balancing inhibitor and initiator content. Coupling this with quantitative measurements of the absorbed volumetric optical dose allows control of polymer conversion and gelation during printing. Importantly, this work thereby establishes the first comprehensive framework for spatial–temporal control over volumetric energy distribution, demonstrating structures 3D printed in thiol-ene resin by means of tomographic volumetric VAM. Mechanical characterization of this thiol-ene system, with varied ratios of isocyanurate and triethylene glycol monomers, reveals highly tunable mechanical response far more versatile than identical acrylate-based resins. This broadens the range of materials and properties available for VAM, taking another step toward high-performance printed polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

X-ray Nanocomputed Tomography in Zernike Phase Contrast for Studying 3D Morphology of Li–O 2 Battery Electrode

In this work, we investigated the lithium peroxide (Li 2 O 2 ) and pore size distribution in lithium-O 2 battery electrodes at different states of charge using transmission X-ray microscopy coupled with Zernike phase contrast to carry out nano-Computed Tomography. We report that such technique enables, at the nanoscale, to distinguish light elements such as carbon and Li 2 O 2 in Li-O 2 battery cathode electrodes. We verified by wave-propagation simulation that this approach efficiently improves the contrast of images in comparison with the pure absorption. The Li 2 O 2 distribution and thickness, interphases, and pore network are visualized and quantified, which give a valuable insight into our cathode architecture. From this 3D analysis, we highlight modifications of the air-cathode morphology and the Li 2 O 2 spatial organization as well as their potential implication in terms of carbon surface passivation and pore-clogging. After the full recharge process, this technique can also reveal the spatial distribution of the residual Li 2 O 2 and other byproducts.

25 ENERGY STORAGE↗
DOE OSTI2019

Magnetic field-temperature phase diagram of multiferroic (NH4)2FeCl5·H2O

Abstract Owing to their overall low energy scales, flexible molecular architectures, and ease of chemical substitution, molecule-based multiferroics are extraordinarily responsive to external stimuli and exhibit remarkably rich phase diagrams. Even so, the stability and microscopic properties of various magnetic states in close proximity to quantum critical points are highly under-explored in these materials. Inspired by these opportunities, we combined pulsed-field magnetization, first-principles calculations, and numerical simulations to reveal the magnetic field–temperature ( B – T ) phase diagram of multiferroic (NH 4 ) 2 FeCl 5 ⋅H 2 O. In this system, a network of intermolecular hydrogen and halogen bonds creates a competing set of exchange interactions that generates additional structure in the phase diagram—both in the vicinity of the spin flop and near the 30 T transition to the fully saturated state. Consequently, the phase diagrams of (NH 4 ) 2 FeCl 5 ⋅H 2 O and its deuterated analog are much more complex than those of other molecule-based multiferroics. The entire series of coupled electric and magnetic transitions can be accessed with a powered magnet, opening the door to exploration and control of properties in this and related materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multicarrier Energy Systems: Shaping Our Energy Future

Multicarrier energy systems (MCESs) are characterized by strong coordination in operation and planning across multiple energy vectors and/or sectors to deliver reliable, cost-effective energy services to end users/customers with minimal impact on the environment. They have efficiency and flexibility benefits and are deployed in large and small scales on the supply and demand sides and at the network level but are more complex to control and manage. Here, MCESs are reviewed in the context of future low carbon energy systems based on electrification and very high variable renewable energy penetrations. Fully exploiting these systems requires some cost reductions, more sophisticated operations enabled by standardized communications and control capabilities detailed planning paradigms, and addressing their corresponding economic challenges. All these point toward the direction of analysis, markets, and technology research and development coupled with better policy and regulatory frameworks. One futuristic vision of a very low carbon energy system is proposed that illustrates potential pathways to an MCES-dominated energy future.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
DOE OSTI2025

ChemComp: Compiling and Computing with Chemical Reaction Networks

The exponential growth in computing demands driven by scientific computing, data analytics, and artificial intelligence is pushing conventional CMOS-based high-performance computing systems to their physical and energy efficiency limits. As we approach the era of post-exascale computing, disruptive approaches are necessary to overcome these barriers and achieve substantial gains in energy efficiency. Analog and hybrid digital-analog computing systems have emerged as promising alternatives, offering the potential for orders-of-magnitude improvements in efficiency. Among these, biochemical computing stands out as a novel paradigm capable of leveraging the natural efficiency of chemical reactions, which have shown promise in solving optimization problems by converging to steady states. By scaling up reaction networks or reaction vessel sizes, biochemical systems present an opportunity to meet the high-performance demands of modern computing tasks. Despite their promise, significant theoretical and practical challenges remain, particularly in formulating and mapping computational problems to chemical reaction networks (CRNs) and designing viable biochemical computing devices. This paper addresses these challenges by introducing new ideas to ChemComp, a compilation and emulation framework for chemical computation. This work describes the mechanisms through which solutions to ordinary differential equations (ODEs) that can be represented as CRN systems can be achieved. Furthermore, we explain the design principles of an ODE dialect implemented as a multi-level intermediate representation (MLIR) compiler extension that will be coupled with existing infrastructure. We demonstrate the potential of our framework through a case study emulating a simplified chemical reservoir computing device. This work establishes foundational tools and methodologies necessary to harness the computational power of chemistry, paving the way for the development of energy-efficient, high-performance computing systems tailored to contemporary and future computational needs.

Bohm Agostini, Nicolas↗
DOE OSTI2024

Method for Coupled Electromagnetic and Circuit Simulations to Evaluate Surge Arrester Performance in Protecting Equipment Against E1 HEMP

Surge arrester behavioral modeling for realistic systems embedded in an E1 high-altitude electromagnetic pulse environment inherently encompasses three interconnected complications: (1) the need to account for signal propagation across two domains, electromagnetics and electrical; (2) the need to include both linear and nonlinear circuit components in the analysis; and (3) the need to understand that the over-current and over-voltage mitigation performance is dependent not only on the properties of the surge arrester and protected load but also on the topology of the overall electrical network. This study presents a framework to address these challenges in a systematic manner to consider the effectiveness of protective measures for a common class of equipment in power generation facilities. Full-wave simulations were carried out to derive circuit-domain (i.e., lumped element–based) equivalent models for the excitation waveform and the physical components of the system. Then, these equivalent models were imported to a circuit solver and combined with a high-frequency surge arrester model to evaluate mitigation performance. The methodology outlined is general enough such that it can be applied for other electromagnetic interference problems that involve E2/E3 HEMP or microwave emissions.

42 ENGINEERING↗
DOE OSTI2026

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

We present a benchmarking study of active learning (AL) schemes for designing planar multilayer nanophotonic metamaterials, where the design tasks are formulated as binary optimization problems. Different surrogate models, including factorization machine (FM), Gaussian process regression (GPR), and convolutional neural network (CNN), combined with different optimization methods, including exhaustive enumeration, discrete particle swarm optimization (DPSO), quantum annealing (QA), hybrid QA, and simulated annealing are studied. The benchmark cases investigated range from small problems with short binary lengths (N = 25) to large problems with N up to 100, focusing on the design of two classes of photonic structures, including antireflective coatings for the long-wavelength infrared region and transparent radiative coolers. For small problems, CNN coupled with DPSO in AL achieves the best performance. As N increases, FM with QA outperforms GPR and CNN. For FM-based AL, hybrid QA yields the best optimization results, particularly in high-dimensional cases (N = 100). These results demonstrate that the optimization method can significantly affect in AL performance as N increases, and that QA-based optimization can provide practical routes for mitigating the optimization bottleneck in high-dimensional problems.

Jung, Serang [Kyung Hee University, Korea]↗
DOE OSTI2021

Dimerization and spin decoupling in a two-leg Heisenberg ladder with frustrated trimer rungs

We study the antiferromagnetic spin-half Heisenberg ladder in the presence of an additional frustrating rung spin which is motivated and relevant also for the description of real two-dimensional materials such as the two-dimensional trimer magnet Ba 4 Ir 3 O 10 . We study the zero-temperature phase diagram, where we combine numerical and analytical methods into an overall consistent description. All numerical simulations are also accompanied by studies of the dynamical spin structure factor obtained via the density matrix renormalization group. Overall, we find in the regime of strong rung coupling a gapped dimerized phase related to competing symmetry sectors in Hilbert space that ultimately results in frustration-driven spin-Peierls transition. In the weak rung-coupling regime, the system is uniform, yet shows a gapped spinon continuum together with a sharp coherent low-energy branch which renders the system critical overall. In either case, the additional rung spin quickly get sidelined and nearly decouple in the regime when their bare coupling to the ladder is weakened

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗