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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis

Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Smart Inverters, Dumb Risk: Taking Control of IBR Security in the Digital Age

This presentation addresses the security challenges posed by Inverter-Based Resources (IBRs) in the modern energy landscape. The presentation highlights the vulnerabilities and risks associated with IBRs, including the potential for cyber-attacks, the impact of insecure defaults, and the systemic risks posed by supply chain dependencies. Key topics covered include: 1) The increasing digital transformation in energy systems and the associated security risks. 2) Specific vulnerabilities in IBRs, including weak passwords, hardcoded credentials, and insecure web application interfaces. 3) The implications of persistent connectivity and the strategic risks posed by foreign-manufactured components. 4) The role of regulatory frameworks, such as NERC CIP, in addressing these challenges and the limitations of current oversight. 5) Practical solutions for mitigating risks, including secure design practices, vendor risk assessments, and the importance of strong passwords and role-based access control. The presentation underscores the necessity of a comprehensive, system-of-systems approach to securing IBRs, emphasizing the need for collaboration across various stakeholders, including operators, developers, and regulators, to ensure the resilience and security of the energy grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Synthesizing Carbon‐Supported, High‐Loading, Ultra‐Small Pt 3 Ni Nanoparticles via Tuning the Surface Electrostatic Effect

Carbon‐supported nanoparticles (NPs) are widely used as catalysts in fuel cells and electrolyzers. While it is well known that NPs with smaller size and higher loading often lead to better catalytic activity, they remain challenging to synthesize due to the weak control over the surface properties of the support. Herein, a facile approach to synthesize carbon‐supported, high‐loading, and ultra‐small Pt 3 Ni NPs via applying thermal shock on strongly interacted carbon support with metal salt is reported. Specifically, sodium citrate is introduced into the precursor solution and substrate mixture, which induces strong electrostatic effect between metal salts and carbon particles that markedly improves precursor anchoring and dispersion, thereby achieving high particle loading as well as small size and distribution. As a proof‐of‐concept, the synthesis of Pt 3 Ni NPs supported on carbon black with particle size of 1.56 ± 0.36 nm at 30 wt% loading and 1.66 ± 0.56 nm at 40 wt% loading is reported, where the sizes are among the smallest while the loadings are among the highest in the literature. This approach can be readily extended to many compositions and substrates, with tunable particle size and loading, thereby substantially expanding the synthesis space for NP catalysts in various electrochemical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Fast nonlinear iterative solver for an implicit, energy-conserving, asymptotic-preserving charged-particle orbit integrator

Here recently, an asymptotic-preserving (AP) particle orbit integrator has been proposed with remarkable properties including exact energy conservation, the ability to capture of all first-order drifts (including the ∇B-drift), the ability to capture trapped-passing boundaries with parallel velocity extremely close to the critical velocity, and the ability to transition from strongly to weakly magnetized spatial regions. The new AP orbit integrator is implicit, employing a Crank-Nicolson (CN) temporal discretization to ensure exact energy conservation. This, in turn, requires a local nonlinear iteration involving particle velocities and positions, and the local electromagnetic fields, to obtain the new-time solution. Ref. [1] did not attempt to provide an efficient solver for this system, and employed a brute-force GMRES-driven Jacobian-free Newton-Krylov (JFNK) solver to invert the particle orbit equations at every timestep for expediency. While JFNK is robust and reliable, it is also expensive and very intrusive for practical implementations of the method (it requires having the JFNK machinery available and solving a 6 x 6 Jacobian system iteratively once per iteration per particle).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Production of Creep-Resistant FeCrAl-ODS

Four ODS FeCrAl alloys with base alloy composition Fe-16Cr-4Al-2Mo-0.2Y (wt.%) were mechanically alloyed with minor additions of CeO 2 and Y 2 O 3 additions and were successfully extruded into rectangular-shaped bars. Two oxide compositions were explored, which were designated NC with oxide addition of 0.5CeO 2 plus 0.2Yin solution of the base alloy powder and PC with additions of 0.5CeO 2 and 0.7Y 2 O 3 to elevate the O level. The microstructure of both alloys revealed by SEM and EBSD to consist of very small grains with sizes of ~450-520 nm and weak texture components. Sub-size tensile specimens fabricated from both alloys were tensile tested from room temperature to 800ºC. The results for both alloys from room temperature to 400ºC showed very high strengths, but low ductilities, especially uniform strain. From 500ºC to 800ºC, the strengths decreased rapidly, and the ductilities increased significantly. The reason for the rapid decreases in tensile strengths above 500ºC for both alloys is not clear, but will require advanced characterization techniques including atom probe tomography, to determine the size and number density of the nano-size oxide particles, and TEM/EFTEM, for investigating the dispersion of nano-size oxide particles on grain boundaries to assess the Zener grain boundary pinning effect, which will hinder grain coarsening at high temperatures that may enhance the Hall Petch strengthening mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Geothermal District Heating in the United States: 2021 Update

As of 2021, there are 23 geothermal district heating (GDH) systems in the United States. Most are over 30 years old. This paper presents an overview of GDH development in the United States and the performance of GDH systems over time. Calculations of the estimated levelized cost of heat (LCOH) for existing U.S. GDH systems were made using NREL's GEOPHIRES tool. Estimated LCOH for existing U.S. GDH systems ranges from $15 to $105/MWhth. This paper explores other factors in GDH development such as resource and system size, capacity factor, and the role of policy. U.S. GDH utilization and deployment are compared to worldwide trends. Results show that the market for GDH in the United States has been weak over the past 40 years due to the combination of inexpensive fossil fuel alternatives (mostly natural gas), lack of incentives focused on heating/cooling, and other factors. Future opportunities for increased GDH deployment in the United States related to increased demand for low-carbon heating and cooling solutions (driven by decarbonization goals in the residential, commercial and industrial heating/cooling sectors, and particularly aggressive ones on college campuses) are outlined. Lastly, this paper identifies policy mechanisms that have been implemented in other countries to incentivize GDH (such as financial incentives targeting GDH, geothermal risk reduction mechanisms, carbon prices benefiting low-carbon heat production, and others).

geothermal district heating↗
DOE OSTI2023

Transport across interfaces in symmetric orbifolds

We examine how conformal boundaries encode energy transport coefficients — namely transmission and reflection probabilities — of corresponding conformal interfaces in symmetric orbifold theories. These constitute a large class of irrational theories and are closely related to holographic setups. Our central goal is to compare such coefficients at the orbifold point (a field theory calculation) against their values when the orbifold is highly deformed (a gravity calculation) — an approach akin to past AdS/CFT-guided comparisons of physical quantities at strong versus weak coupling. At the orbifold point, we find that the (weighted-average) transport coefficients are simply averages of coefficients in the underlying seed theory. We then focus on the symmetric orbifold of the T 4 sigma model interface CFT dual to type IIB supergravity on the 3d Janus solution. We compare the holographic transmission coefficient, which was found by [1], to that of the orbifold point. We find that the profile of the transmission coefficient substantially increases with the coupling, in contrast to boundary entropy. We also present some related ideas about twisted-sector data encoded by boundary states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Molecular Engineering of Emissive Molecular Qubits Based on Spin-Correlated Radical Pairs

Spin chemistry of photogenerated spin-correlated radical pairs (SCRPs) offers a practical approach to control chemical reactions and molecular emissions by using weak magnetic fields. This capability to harness magnetic field effects (MFEs) paves the way for developing SCRPs-based molecular qubits. Here, in this work, we introduce a new series of donor–chiral bridge–acceptor (D–χ–A) molecules that demonstrate significant MFEs on fluorescence intensity and lifetime in solution at room temperature—critical for quantum sensing. By precisely tuning the donor site through torsional locking, distance extension, and planarization, we achieved remarkable control over key quantum properties, including field-response range and line width. In the most responsive systems, emission lifetimes increased by over 200%, and the total emission intensity was modulated by up to 30%. This level of tunability shows the power of synthetic spin chemistry. The rational design principle of optically addressable SCRP-based molecular systems, presented in this work, represents a major leap toward functional synthetic molecular qubits, advancing the field of molecular quantum technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hydrogen bonds and dispersion forces serving as molecular locks for tailored Group 11 bis(amidine) complexes

A flexible polydentate bis(amidine) ligand LH 2 , LH 2 = {CH 2 NH( t Bu)C=N-2-(6-MePy)} 2 , operates as a molecular lock for various coinage metal fragments and forms the dinuclear complexes [LH 2 (MCl) 2 ], M = Cu (1), Au (2), the coordination polymer [{(LH 2 ) 2 (py) 2 (AgCl) 3 }(py) 3 ] n (3), and the dimesityl-digold complex [LH 2 (AuMes) 2 ] (4) by formal insertion of MR fragments (M = Cu, Ag, Au; R = Cl, Mes) into the N–H∙∙∙N hydrogen bonds of LH 2 in yields of 43–95%. Complexes 1, 2, and 4 adopt C 2 -symmetrical structures in the solid state featuring two interconnected 11-membered rings that are locked by two intramolecular N–H∙∙∙R–M hydrogen bonds. QTAIM analyses of the computational geometry-optimized structures 1a, 2a, and 4a reveal 13, 11, and 22 additional bond critical points, respectively, all of which are related to weak intramolecular attractive interactions, predominantly representing dispersion forces, contributing to the conformational stabilization of the C2-symmetrical stereoisomers in the solid state. Variable-temperature 1 H NMR spectroscopy in combination with DFT calculations indicate a dynamic conformational interconversion between two C 2 -symmetrical ground state structures in solution (ΔG ‡ c = 11.1–13.8 kcal mol –1 ), which is accompanied by the formation of an intermediate possessing C i symmetry that retains the hydrogen bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dirac-Harper Theory for One-Dimensional Moiré Superlattices

In this work, we study a Dirac-Harper model for moiré bilayer superlattices where layer antisymmetric strain periodically modulates the interlayer coupling between two honeycomb lattices in one spatial dimension. Discrete and continuum formulations of this model are analyzed. For a sufficiently long moiré period we find low-energy spectra that host a manifold of weakly dispersive bands arising from a hierarchy of momentum and position-dependent mass inversions. We analyze their charge distributions, mode count, and valley coherence using exact symmetries of the lattice model and approximate symmetries of a four-flavor version of the Jackiw-Rebbi one-dimensional solution.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Pair Density Wave Order from Electron Repulsion

A pair density wave (PDW) is a superconductor whose order parameter is a periodic function of space, without an accompanying spatially uniform component. Since PDWs are not the outcome of a weak-coupling instability of a Fermi liquid, a generic pairing mechanism for PDW order has remained elusive. Here, we describe and solve models having robust PDW phases. To access the intermediate coupling limit, we invoke large-$N$ limits of Fermi liquids with repulsive BCS interactions that admit saddle point solutions. We show that the requirements for long-range PDW order are that the repulsive BCS couplings must be nonmonotonic in space and that their strength must exceed a threshold value. We obtain a phase diagram with both finite temperature transitions to PDW order and a $T = 0$ quantum critical point, where non-Fermi liquid behavior occurs.

2-dimensional systems↗
DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗
DOE OSTI2024

Development of message passing-based graph convolutional networks for classifying cancer pathology reports

Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Data driven methods to recognize patterns in EIC weak-strong simulation

Beam-Beam simulations are currently being studied in preparation for future EIC experiments to study beam-beam effects and, in turn, maximize luminosity. Weak-strong methods are studied for single-particle dynamics during collision. 1 million macro-particles for 1 million turns are typically tracked, corresponding to only 10 seconds in the EIC. The goal of this study is to predict beam properties over the scale of hours. A potential solution focuses on using data-driven methods such as machine learning methods to analyze and extend the insights of the beam properties such as long-term nonlinear effects. This would aid in long-term predictions where results would be more efficiently acquired than a typical tracking simulation. Some limitations such as inaccurate predictions and spatial complexity are also discussed. These methods can then be applied to strong-strong simulations in the future studies.

Accelerator Physics↗
DOE OSTI2025

Quantum AI Based Enhanced Detection of Dementia

Quantum computing has the potential to significantly improve the early detection of Alzheimer's Disease and Related Dementias (ADRD). Quantum-enhanced machine learning can be used to perform an early screening of Alzheimer's disease using brain imaging data based on dataset of MRI scans from both healthy individuals and those diagnosed with Alzheimer's. This study aims to demonstrate the potential of quantum transfer learning to enhance the performance of the classical deep learning model for dementia detection. Using the MRI sagittal images available in the OASIS-2 (64 demented and 72 non-demented subjects between 60 and 96 years), we show how quantum techniques can transform a suboptimal classical model into a more effective solution for dementia detection, highlighting their potential impact on advancing healthcare technology. We begin with a simple classical deep learning model with a significantly smaller number of parameters, which gives suboptimal performance on the problem. Then, we apply different configurations of quantum transfer learning based on the pre-trained weak classifier (Figure 1). We fix the weak classifier's initial convolutional layers at their fixed pre-trained parameters and replace the last set of dense layers with a dressed quantum circuit (DQN), which we train to enhance performance. We performed 4-fold cross-validation for both the classical and the hybrid quantum models and trained them using Pennylane's `default.qubit' simulator and IonQ's Aria-1 simulator (noisy simulation). We showed that with significantly fewer parameters, the quantum transfer learning-based hybrid models showed significant performance enhancement over the base weak classical deep learning model for dementia detection. To classify between a demented and non-demented subject, the accuracy of quantum-based AI methods improved by 6 to 14% compared to classical methods. The sensitivity of the models improved by 4 to 17%. This shows that there are fewer chances of misclassifying demented patients. Figure 2 compares the performance of the hybrid quantum models and their base classical model, and Table 1 summarizes the results. We illustrated that with assistance from quantum machine learning, it is possible to enhance detection for dementia based on brain images. This shows the potential for practical utility of quantum computing in ADRD research.

Bhowmik, Sounak [University of Tennessee, Knoxvill↗
DOE OSTI2023

Multidisciplinary benchmarks of a conservative spectral solver for the nonlinear Boltzmann equation

The Boltzmann equation describes the evolution of the phase-space probability distribution of classical particles under binary collisions. Approximations to it underlie the basis for several scholarly fields, including aerodynamics and plasma physics. While these approximations are appropriate in their respective domains, they can be violated in niche but diverse applications which require direct numerical solution of the original nonlinear Boltzmann equation. An expanded implementation of the Galerkin–Petrov conservative spectral algorithm is employed to study a wide variety of physical problems. Enabled by distributed precomputation, solutions of the spatially homogeneous Boltzmann equation can be achieved in seconds on modern personal hardware, while spatially-inhomogeneous problems are solvable in minutes. Here, several benchmarks are presented focusing on accuracy compared to both analytic theoretical predictions and other Boltzmann solvers. These benchmarks span several physical domains including weakly ionized plasma, gaseous fluids, and atomic-plasma interaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ALESQP: An Augmented Lagrangian Equality-Constrained SQP Method for Optimization with General Constraints

Here we present a new algorithm for infinite-dimensional optimization with general constraints, called ALESQP. In short, ALESQP is an augmented Lagrangian method that penalizes inequality constraints and solves equality-constrained nonlinear optimization subproblems at every iteration. The subproblems are solved using a matrix-free trust-region sequential quadratic programming (SQP) method that takes advantage of iterative, i.e., inexact linear solvers, and is suitable for large-scale applications. A key feature of ALESQP is a constraint decomposition strategy that allows it to exploit problem-specific variable scalings and inner products. We analyze convergence of ALESQP under different assumptions. We show that strong accumulation points are stationary. Consequently, in finite dimensions ALESQP converges to a stationary point. In infinite dimensions we establish that weak accumulation points are feasible in many practical situations. Under additional assumptions we show that weak accumulation points are stationary. We present several infinite-dimensional examples where ALESQP shows remarkable discretization-independent performance in all of its iterative components, requiring a modest number of iterations to meet constraint tolerances at the level of machine precision. Also, we demonstrate a fully matrix-free solution of an infinite-dimensional problem with nonlinear inequality constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fast Solution of Fully Implicit Runge--Kutta and Discontinuous Galerkin in Time for Numerical PDEs, Part I: the Linear Setting

Fully implicit Runge--Kutta (IRK) methods have many desirable properties as time integration schemes in terms of accuracy and stability, but high-order IRK methods are not commonly used in practice with numerical PDEs due to the difficulty of solving the stage equations. This paper introduces a theoretical and algorithmic preconditioning framework for solving the systems of equations that arise from IRK methods applied to linear numerical PDEs (without algebraic constraints). Additionally, this framework also naturally applies to discontinuous Galerkin discretizations in time. Under quite general assumptions on the spatial discretization that yield stable time integration, the preconditioned operator is proven to have condition number bounded by a small, order-one constant, independent of the spatial mesh and time-step size, and with only weak dependence on number of stages/polynomial order; for example, the preconditioned operator for 10th-order Gauss IRK has condition number less than two, independent of the spatial discretization and time step. The new method can be used with arbitrary existing preconditioners for backward Euler-type time-stepping schemes and is amenable to the use of three-term recursion Krylov methods when the underlying spatial discretization is symmetric. The new method is demonstrated to be effective on various high-order finite-difference and finite element discretizations of linear parabolic and hyperbolic problems, demonstrating fast, scalable solution of up to 10th-order accuracy. The new method consistently outperforms existing block preconditioning approaches, and in several cases, the new method can achieve 4th-order accuracy using Gauss integration with roughly half the number of preconditioner applications and wallclock time as required using standard diagonally IRK methods.

97 MATHEMATICS AND COMPUTING↗