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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging

Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughput environments, such as the CERN LHC. The quadratic complexity of transformer models demands substantial resources and increases latency during inference. In order to address these issues, we introduce the Spatially Aware Linear Transformer (SAL-T), a physics-inspired enhancement of the linformer architecture that maintains linear attention. Our method incorporates spatially aware partitioning of particles based on kinematic features, thereby computing attention between regions of physical significance. Additionally, we employ convolutional layers to capture local correlations, informed by insights from jet physics. In addition to outperforming the standard linformer in jet classification tasks, SAL-T also achieves classification results comparable to full-attention transformers, while using considerably fewer resources with lower latency during inference. Experiments on a generic point cloud classification dataset (ModelNet10) further confirm this trend. Our code is available at https://github.com/aaronw5/SAL-T4HEP.

Wang, Aaron [Illinois U., Chicago] (ORCID:00000003↗
DOE OSTI2024

Surface science insight note: A linear algebraic approach to elucidate native films on Fe 3 O 4 surface

Standard materials are often used to obtain spectra that can be compared to those from unknown samples. Spectra measured from these known substances are also used as a means of computing sensitivity factors to allow quantification by X-ray photoelectron spectroscopy (XPS) of less well-defined materials. Spectra from known materials also provide line shapes suitable for inclusion in spectral models which, when fitted to spectra, permit the chemical state for a sample to be assessed. Both types of information depend on isolating photoemission signals from the inelastically scattered signal. In this Insight note, technical issues associated with the use of XPS of as received Fe 3 O 4 powder sample surface are discussed. The Insight note is designed to show how linear algebraic techniques applied to data collected from a sample marketed as pure Fe 3 O 4 powder are used to verify that XPS has been performed on chemistry representative of the sample. The methods described in this Insight note can further be utilized in elucidating complex XPS data obtained from thin films formed or evolved during cyclic/non-steady use of complex (electro)catalyst surfaces, especially in the presence of contaminants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning

We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity partial differential equation (PDE) models governed by autonomous dynamical systems, leading to large-scale forward problems; (2) they solve a linear inverse problem to assimilate observational data to infer uncertain model components followed by a forward prediction of the evolving dynamics; and (3) the entire end-to-end, data-to-inference-to-prediction computation is carried out without approximation and in real time through a Bayesian framework that rigorously accounts for uncertainties. Several challenges must be overcome to realize this framework, including the large scale of the forward problem, the high dimensionality of the parameter space, and for a class of problems including those we target, the slow decay of the singular values of the parameter-to-observable map. Here we introduce a methodology to overcome these challenges by exploiting the autonomous structure of the forward model to decompose the solution of the inverse problem into a one-time-only offline phase in which the PDE model is solved a limited number of times (equal to the number of sensors), and an online phase that maps well onto GPUs and computes the parameter inference and prediction of quantities of interest in real time, given observational data. Our ultimate goal is to apply this framework to construct digital twins for subduction zones, including Cascadia, to provide early warning for tsunamis generated by megathrust earthquakes. To this end, we demonstrate how our methodology can be used to employ seafloor pressure observations, along with the coupled acoustic–gravity wave equations, to infer the earthquake-induced spatiotemporal seafloor motion (discretized with $\mathscr{O}$ (10 9 ) parameters) and forward predict the tsunami propagation. We present results of an end-to-end inference, prediction, and uncertainty quantification for a representative test problem with $\mathscr{O}$ (10 8 ) inversion parameters for which goal-oriented Bayesian inference is accomplished exactly and in real time, that is, in a matter of seconds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Techno-economic design of a linear Fresnel reflector for industrial process heat

A techno-economic model of a Concentrating Solar Thermal (CST) system using a Linear Fresnel Reflector (LFR) has been developed. LFRs can deliver process heat suitable for a range of industries, including food and beverage production. This model uses an adaptive algorithm to calculate the optimal secondary reflector shape given the geometry and optical properties of the rest of the system. A ray-tracing program is used to calculate optical efficiency over a wide range of longitudinal and transversal incidence angles and subsequently evaluate the annual efficiency at a given geographical location. A specific LFR design developed by Hyperlight Energy was modelled, and this industrial partner provided a detailed cost breakdown which was used as the basis of an economic model. Combining the technical and economic data facilitates the calculation of the Levelized Cost of Heat (LCOH). The influence of a number of parameters on the annual efficiency and LCOH is explored; notable parameters include the absorber height, the number, width, and spacing of the primary mirrors, the aim point, and the secondary reflector shape and width. By identifying an optimal combination of these parameters, we reduce the LCOH of the industry partner’s system design by 9.2%, from 14.4 $\$/MWh_{th}$ to 13.0 $\$/MWh_{th}$. In comparison, the LCOH of a natural gas boiler delivering the same annual quantity of heat is 29 $\$/MWh_{th}$, which indicates that LFRs can be a competitive heat source for industrial processes.

14 SOLAR ENERGY↗
DOE OSTI2026

Experimental and simulation study of target biasing effects on plasma transport in linear plasma device MPS-LD

Linear plasma devices (LPDs) are important experimental platforms for investigating plasma–material interactions (PMI). In PMI experiments, it has been found that applying a target bias not only effectively modifies the incident ion energy, but also induces significant changes in the electron density and electron temperature, whereby the evolution of these plasma parameters is primarily governed by plasma transport processes. However, at present, the physical process and mechanism underlying such bias-induced variations remain unclear. In this work, biasing experiments under argon plasma discharge conditions were first carried out on the MPS-LD device. For the corresponding experiments, an electric potential model was newly developed based on the BOUT++ LPD module, enabling self-consistent simulations of plasma transport under biased conditions. Numerical simulations were then performed to reproduce the experimental results and to validate the accuracy of the proposed model. Finally, by combining experimental measurements with numerical simulations, a bias-voltage scan was performed to investigate how the electron density and electron temperature vary with the bias voltage (U bias ). The results show that applying negative bias decreases the target electron density (n e,T ) while increasing the target electron temperature (T e,T ). In contrast, positive bias increases both n e,T and T e,T ; however, at high positive bias, n e,T first reaches a maximum and subsequently decreases with further increases in U bias . The underlying physical mechanisms are analyzed using particle flux, momentum, and energy conservation. It indicates that the applied bias regulates the parallel electric field, thereby changing ion and electron velocities, and consequently affecting the electron density. At high positive bias, the ion velocity is further influenced by ion viscosity, leading to the reversal in n e,T . Meanwhile, the enhanced parallel electric field drives stronger currents, significantly increasing ion–electron frictional work and converting the input bias power into electron energy, which raises the electron temperature. In conclusion, these results contribute to a deeper understanding of the effects and mechanisms of biasing on plasma transport in the MPS-LD device.

BOUT++ simulation↗
DOE OSTI2024

Interconnected Nanoporous Polysulfone by the Self-Assembly of Randomly Linked Copolymer Networks and Linear Multiblocks

Porous materials have attracted considerable attention due to their versatile applications, especially in water purification. Interconnected nanoporous structures are distinguished by their high degree of porosity and resistance to clogging, as well as their insensitivity to nanostructural orientation. Previous works on randomly linked copolymer systems have shown that they can effectively produce disordered cocontinuous nanostructures, which upon removal of one component yield interconnected nanoporous materials. However, the cocontinuous nanomaterials previously developed using polystyrene (PS) and poly(d,l-lactic acid) (PLA) strands, and the resulting interconnected nanoporous PS monoliths, were far too brittle to enable practical use as membranes. Here, we study the self-assembly of randomly linked copolymer networks prepared using blocks of the engineering polymer polysulfone (PSU). A wide cocontinuous regime (spanning 40 wt %) was found for randomly end-linked copolymer networks (RECNs) constructed from PSU and PLA strands, via a combination of mechanical testing, gravimetry, small-angle X-ray scattering, and scanning electron microscopy. The PSU/PLA cocontinuous nanomaterial with symmetric composition showed 2.4 times higher Young’s modulus and ~100 times greater toughness than the corresponding PS/PLA sample. The interconnected nanoporous PSU fabricated after etching of PLA even exhibited 1.6 times greater toughness than PS/PLA prior to PLA removal. To facilitate the production of thin films of cocontinuous nanomaterials, we applied solution-processable randomly linked linear PSU/PLA multiblock polymers onto ultrafiltration membranes. Here, the interconnected nanoporous PSU thin film generated by etching PLA was found to effectively reject 50 nm diameter particles without significantly compromising permeability. This discovery presents a valuable addition to the existing techniques used to fabricate PSU membranes. In contrast to traditional methods, which are sensitive to processing conditions, produce a wide range of pore sizes, and offer limited adjustability of pore size, the current technique is anticipated to enable interconnected PSU membranes with more uniform and tailorable porosity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Polariton spectra under the collective coupling regime. II. 2D non-linear spectra

In our previous work [Mondal et al., J. Chem. Phys. 162, 014114 (2025)], we developed several efficient computational approaches to simulate exciton–polariton dynamics described by the Holstein–Tavis–Cummings (HTC) Hamiltonian under the collective coupling regime. Here, we incorporated these strategies into the previously developed Lindblad-partially linearized density matrix (⁠$\mathscr{L}$-PLDM) approach for simulating 2D electronic spectroscopy (2DES) of exciton–polariton under the collective coupling regime. In particular, we apply the efficient quantum dynamics propagation scheme developed in Paper I to both the forward and the backward propagations in the PLDM and develop an efficient importance sampling scheme and graphics processing unit vectorization scheme that allow us to reduce the computational costs from $\mathscr{O}$($\mathscr{K}$ 2 )$\mathscr{O}$(T 3 ) to $\mathscr{O}$($\mathscr{K}$)$\mathscr{O}$(T 0 ) for the 2DES simulation, where $\mathscr{K}$ is the number of states and T is the number of time steps of propagation. As a result, we further simulated the 2DES for an HTC Hamiltonian under the collective coupling regime and analyzed the signal from both rephasing and non-rephasing contributions of the ground state bleaching, excited state emission, and stimulated emission pathways.

2D non-linear spectra↗
DOE OSTI2025

High-power test of a C-band linear accelerating structure with an RFSoC-based LLRF system

Normal conducting linear particle accelerators consist of multiple rf stations with accelerating structure cavities. Low-level rf (LLRF) systems are employed to set the phase and amplitude of the field in the accelerating structure and to compensate for the pulse-to-pulse fluctuation of the rf field in the accelerating structures with a feedback loop. The LLRF systems are typically implemented with analog rf mixers, heterodyne-based architectures, and discrete data converters. There are multiple rf signals from each of the rf stations, so the number of rf channels required increases rapidly with multiple rf stations. With a large number of rf channels, the footprint, component cost, and system complexity of the LLRF hardware will increase significantly. To meet the design goals of being compact and affordable for future accelerators, we have designed the next-generation LLRF (NG-LLRF) with a higher integration level based on RFSoC technology. The NG-LLRF system samples rf signals directly and performs rf mixing digitally. Further, the NG-LLRF has been characterized in loopback mode to evaluate the performance of the system and has also been tested with a standing-wave accelerating structure, a prototype for the Cool Copper Collider (C 3 ) with a peak rf power level up to 16.45 MW. The loopback test demonstrated amplitude fluctuation below 0.15% and phase fluctuation below 0.15°, which are considerably better than the requirements of C 3 . The rf signals from the different stages of the accelerating structure at different power levels are measured by the NG-LLRF, which will be critical references for the control algorithm designs. The NG-LLRF also offers flexibility in waveform modulation, so we have used rf pulses with various modulation schemes, which could be useful for controlling some of the rf stations in accelerators. In this paper, the high-power test results at different stages of the test setup will be summarized, analyzed, and discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Non-linear MHD modelling of transients in tokamaks: a review of recent advances with the JOREK code

Transient magneto-hydrodynamic (MHD) events like edge localized modes (ELMs) or disruptions are a concern for magnetic confinement fusion power plants. Research with the MHD code JOREK towards understanding control of such instabilities is reviewed here in a concise way to provide a complete overview, while we refer to the original publications for details. Experimental validation for unmitigated vertical displacement events progressed. The mechanism of vertical force mitigation by impurity injection was identified. Two-way eddy current coupling to CARIDDI was completed. Shattered pellet injection was simulated in JET, KSTAR, ASDEX Upgrade (AUG) and ITER. Benign runaway electron beam termination in JET and ITER was studied. Coupling of kinetic REs to the MHD is ongoing and a virtual RE synchrotron radiation diagnostic was developed. Regarding pedestal physics, regimes devoid of large ELMs in AUG were simulated and predictive JT60-SA simulations are ongoing. For ELM suppression by resonant magnetic perturbations (RMPs), AUG, ITER and EAST simulations were performed. A free boundary RMP model was validated against experiments. Evidence for penetrated magnetic islands at the pedestal top based on AUG experiments and simulations was found. Simulations of the naturally ELM-free quiescent H-mode in AUG and HL-3 show external kink mode formation prevents pedestal build-up towards an ELM within windows of the edge safety factor. With kinetic neutral particles, high field side high density formation in ITER was simulated and with kinetic impurities, tungsten transport in AUG RMP plasmas was studied. To capture turbulent transport, electro-static full-f particle in cell models for ion temperature gradient and trapped electron modes were established and benchmarked. Application to RMP plasmas shows enhanced turbulence in comparison to unperturbed states. Energetic particle interactions with MHD were studied. Flux pumping that prevents the safety factor on axis from dropping below unity was simulated. First non-linear stellarator applications include current relaxation in $l$ = 2 stellarators, while verification for advanced stellarators progresses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Surrogate models for linear response

Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field vacuum; however, its high computational cost limits model calibration and uncertainty quantification studies. Here, we present two complementary QRPA surrogate models and apply them to study response functions of finite nuclei. One is a reduced-order model that exploits the underlying QRPA structure, while the other utilizes the recently developed parametric matrix model algorithm to construct a map between the system’s Hamiltonian and observables. Our benchmark applications, the calculation of the electric dipole polarizability of 180 Yb and the 𝛽-decay half-life of 80 Ni, show that both emulators can achieve 0.1%–1% accuracy while offering a 6–7 orders of magnitude speedup compared to state-of-the-art QRPA solvers. These results demonstrate that the developed QRPA emulators are well positioned to enable Bayesian calibration and large-scale studies of computationally expensive physics models describing the properties of many-body systems.

Beta decay↗
DOE OSTI2025

Two-Stage Distributionally Robust Conic Linear Programming over 1-Wasserstein Balls

Here, this paper studies two-stage distributionally robust conic linear programming under constraint uncertainty over type-1 Wasserstein balls. We present optimality conditions for the dual of the worst-case expectation problem, which characterizes worst-case uncertain parameters for its inner maximization problem. This condition offers an alternative proof, a counterexample, and an extension to previous works. Additionally, the condition highlights the potential advantage of a specific distance metric for out-of-sample performance, as exemplified in a numerical study on a facility location problem with demand uncertainty. Furthermore, cutting-plane-based algorithms, equipped with a unified scenario generation framework, are proposed for addressing both unbounded support and second-stage dual feasible regions, with a finite convergence proof under less stringent assumptions.

Wasserstein↗
DOE OSTI2026

Laplace Transform–Based Quantum Eigenvalue Transformation via Linear Combination of Hamiltonian Simulation

Eigenvalue transformations, which include solving time-dependent differential equations as a special case, have a wide range of applications in scientific and engineering computation. While quantum algorithms for singular value transformations are well studied, eigenvalue transformations are distinct, especially for nonnormal matrices. Here, we propose an efficient quantum algorithm for performing a class of eigenvalue transformations that can be expressed as a certain type of matrix Laplace transformation. This allows us to significantly extend the recently developed linear combination of Hamiltonian simulation method [D. An, J.-P. Liu, and L. Lin, Phys. Rev. Lett., 131 (2023), 150603; D. An, A. M. Childs, and L. Lin, Commun. Math. Phys. 407, 19 (2026)] to represent a wider class of eigenvalue transformations, such as powers of the matrix inverse, 𝐴 −𝑘 , and the exponential of the matrix inverse, 𝑒 −𝐴 −1 . The latter can be interpreted as the solution of a mass-matrix differential equation of the form form 𝐴⁢𝑢′⁡⁡(𝑡) =−𝑢⁡(𝑡). We demonstrate that our eigenvalue transformation approach can solve this problem without explicitly inverting 𝐴, thereby reducing the computational complexity.

Laplace transform↗
DOE OSTI2025

SAM Linear Fresnel Model, Project B (CRADA Final Report)

Objective performance and economic modeling of solar thermal plants is of keen interest to many EPRI funders. One solar thermal technology that is not currently available to model in any non-vendor, non-proprietary tool is linear Fresnel. This technology has garnered enough interest from EPRI funders to merit investing in a tool to objectively model its performance. Early in 2010, EPRI performed a comparison of modeling solar thermal power plants using the IPSEPRO, CNRS and Solar Advisor (SAM) tools. After completing this effort, EPRI decided to adopt NREL’s Solar Advisor Model as its default modeling tool based in part on user friendliness, flexibility, number of technologies covered, integrated financial model and ease of running sensitivities. Furthermore, it was recognized that NREL continues to invest considerable time and resources into improving capabilities and functionality of the model.

14 SOLAR ENERGY↗
DOE OSTI2025

Dynamic Modeling, Trajectory Optimization, and Linear Control of Cable-Driven Parallel Robots for Automated Panelized Building Retrofits

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment.

CDPR↗
DOE OSTI2024

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Understanding the Role of Hydroxyl Functionalization in Linear Poly(Ethylenimine) for Oxidation‐Resistant Direct Air Capture of CO 2

Aminopolymer-based adsorbents are a prominent class of materials being used for direct air capture of CO 2 at the industrial scale. However, improving their working lifetime, specifically by increasing their resilience to oxidative degradation, remains an ongoing challenge. Toward this end, functionalization of aminopolymers with non-amine functionalities such as hydroxyls has emerged in recent years as a promising strategy toward improving adsorbent lifetime. Although there is a growing body of work demonstrating the effectiveness of this approach and investigating the origin of this improved stability, studies to date have primarily focused on branched aminopolymer systems such as branched poly(ethylenimine). In this work, hydroxyl-functionalized linear poly(ethylenimine) is used to continue to probe the underlying protective mechanism of this strategy. A combination of thermogravimetric analysis, NMR relaxometry, differential scanning calorimetry, and computational simulations is used to better understand the relationship between the extent of chemical functionalization, physical properties, and adsorbent performance.

amine-based adsorbent↗