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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Decomposing Loosely Coupled Mixed-Integer Programs for Optimal Microgrid Design

Microgrids are frequently employed in remote regions, in part because access to a larger electric grid is impossible, difficult, or compromises reliability and independence. Although small microgrids often employ spot generation, in which a diesel generator is attached directly to a load, microgrids that combine these individual loads and augment generators with photovoltaic cells and batteries as a distributed energy system are emerging as a safer, less costly alternative. In this work, we present a model that seeks the minimum-cost microgrid design and ideal dispatched power to support a small remote site for one year with hourly fidelity under a detailed battery model; this mixed-integer nonlinear program (MINLP) is intractable with commercial solvers but loosely coupled with respect to time. A mixed-integer linear program (MIP) approximates the model, and a partitioning scheme linearizes the bilinear terms. We introduce a novel policy for loosely coupled MIPs in which the system reverts to equivalent conditions at regular time intervals; this separates the problem into subproblems that we solve in parallel. We obtain solutions within 5% of optimality in at most six minutes across 14 MIP instances from the literature and solutions within 5% of optimality to the MINLP instances within 20 minutes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Density functional based methods for light harvesting molecules

The goal of the proposal was to computationally study the photoinduced charge transfer excited states of organic molecules. The systems of interest are the large organic supramolecules that have potential for photovoltaic applications. The simulation of the charge transfer excited states in large supramolecular assemblies require quantum mechanical methods that can be applied to systems with hundreds of atoms. Through this project we have implemented a perturbative delta-self-consistent field method that can easily describe the charge transfer excited states with regular density functionals without range separation. The cost of the calculations of an excited state is similar to that of the ground state. The method also allows for calculation of the energy gradients of the excited state. The developed method was applied to study a number of different supramolecular systems containing hundreds of atoms with excellent agreement with experiment. The absorption characteristics and the influence of density functional approximations were also examined. These led to several studies on the variety of organic electron donor and acceptor molecules. Apart from these developments through this project we also improved the code capabilities by implementing new meta-GGA functionals. This was a major step since the code is purely density functional and earlier the functional capabilities were limited only to generalized gradient approximations. A total of 23 peer-reviewed publications in top chemical physics journals resulted from this grant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Regularization prescription for the mixing between nonlocal gluon and quark operators

It is well known that in the study of mixing between nonlocal gluon and quark bilinear operators there exists an ambiguity when relating coordinate space and momentum space results. In this work, we show that this ambiguity is due to the lack of a proper regularization prescription of the singularity that arises when the separation between the gluon/quark fields approaches zero. We then demonstrate that dimensional regularization resolves this issue and yields consistent results in both coordinate and momentum space. This prescription is also compatible with lattice extractions of parton distributions from nonlocal operators.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Mesofluidic Oil-Water Separation

Separating flowing multiphase solutions in process at industrial scale is challenging. A rapid continuous approach is desired. These mesofluidic oil-water separation tests were motivated by reports by Dijkshoorn et al [1] who reported separating droplets and particles using displacement with regularly spaced channels using a cross-flow microsieve.. Droplets, including deformable particles, larger than the channel migrate in one direction and thereby deplete in the other direction. Test flowrates were motivated by Burns et al. [4] who showed that separation could occur under laminar conditions, and Pease et al [8, 9] who extended the range to turbulent flow. Testing was conducted using two two-component oil-water mixtures at room temperature. Analysis showed separation of both mineral oils from their oil water mixtures at flowrates from 1-2 L/min and concentrations from 2-10 wt% oil in water.

cloud height, effective cleaning radius, effective↗
DOE OSTI2025

Mesofluidic Inline Separation for Produced Water Treatment

Mesofluidic inline separation developed by PNNL represents an opportunity to remove a key barrier in the treatment of produced water, namely suspended solids that clog downstream operations. The US alone produces over 800 billion gallons of produced water each year, most of which is reinjected underground (but not into the aquifers) as waste. The impact from treating and reusing even a fraction of this wastewater is immense as aquifers in the Midwest and elsewhere dry. The work described herein is essential to leveraging the opportunity to improve produced water quality (to allow for beneficial use) by combining mesofluidic inline separators with reverse osmosis systems. Providing water for agricultural and industrial uses in the American Southwest and Midwest by allowing the reuse of petroleum produced water may be critical to the long-term economics of the region. This is especially true as drought conditions are rapidly lowering aquifer levels in these regions. The incumbent technology for desalination is reverse osmosis (RO) due to its ability to treat a wide range of feedwaters and technological maturity. However, suspended solids cause RO (and other dissolved solids removal technologies) to become clogged and loose performance. A common strategy is to use prefilters upstream of RO systems, but these membrane-based filters easily clog and require regular maintenance. Unlike membrane filters, mesofluidic inline separators provide removal of suspended solids with much lower pressure drops than conventional systems filtration systems, permitting substantially higher flowrates. Although the amount of produced water from petroleum operations is vast, only a small fraction of it is reused or turned into potable water, for example, because of the lack of technologies to remove both dissolved and suspended solids at a significant throughput. This project shows that mesofluidic inline separators coupled with a commercial dissolved solids removal technology (RO) are positioned to do exactly that. Mesofluidic inline separators fit within commercial piping and are tunable for particle sizes of interest as described below. Indeed, mesofluidic inline separators have remarkably smaller footprints than competing filtration technologies and are easily transportable from jobsite to jobsite. This technology has no moving parts so that solids removal can be accomplished at much lower operating cost.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A phase-field model of quasi-brittle fracture for pressurized cracks: Application to UO 2 high-burnup microstructure fragmentation

Here, in this paper, we present a phase-field model of quasi-brittle fracture with pressurized cracks, with dedicated applications for polycrystalline materials. The model is formulated as a minimization problem within the variational framework. The external work done by pressure on the crack surfaces is included in the objective function. Several careful modeling choices lead to a regularization-length-independent critical strength. The model is constructed to give a softening response with an underlying linear traction-separation law. The pressure-dependent softening response and the regularization of the prescribed pressure are demonstrated with a (quasi) one-dimensional numerical analysis. In a two-dimensional numerical analysis under plane strain assumptions, the critical stress corresponding to crack propagation (as predicted by our quasi-brittle fracture model) is compared with linear elastic fracture mechanics (LEFM) analytical solutions. Our model is further utilized to simulate fission-gas-induced fragmentation of the UO 2 high-burnup structure (HBS). Simulation results show that pressurized bubbles can cause crack nucleation and propagation, and that the bubble size and the surrounding external pressure affect the critical pressure corresponding to crack nucleation. Simulations of a partial HBS at different recrystallization stages show that different grain structures (due to recrystallization) also influence crack paths and fragmentation morphology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding Gas Absorption in Multivalent Ionic Liquids via Solute-Solvent Interaction Analyses

Solute-solvent interactions are often evaluated to understand and explain gas solubilities. This information is regularly used to interpret gas solubility and separation within ionic liquids (ILs). Here, we use several different quantum chemical and previous molecular dynamics simulation results to show that strong solute-solvent interactions neither lead to high solubility nor to high selectivity, with respect to other solutes. Instead, the solute-solvent interaction strength with different isolated IL ions is affected by the electrostatic extremes. Also, the solute-solvent interaction with different IL molecules is strongly correlated to the molecular volumes or polarizabilities (within a series of same-anion based ILs).

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

Physics of beam-driven ion cyclotron emission in the large plasma device

Abstract Ion cyclotron emission (ICE) is widely observed from spatially localised minority energetic ion populations in toroidal magnetically confined fusion (MCF) plasmas, both tokamaks and stellarators. Its spectral structure is typically regular with narrow suprathermal peaks, whose frequency separation matches a local energetic ion cyclotron frequency. Here we report the first computational (fully nonlinear self-consistent kinetic particle-in-cell code) and analytical (linear magnetoacoustic cyclotron instability (MCI)) studies of ICE observations from cylindrical plasmas contained in the Large Plasma Device (LAPD). Because LAPD is cylindrical, the plasma physics giving rise to the observed ICE spectrum necessarily excludes toroidal effects. Our approach, previously successful for toroidal plasma ICE, assumes slab geometry and hence is well adapted to LAPD. ICE from LAPD is strongly electrostatic, as distinct from electromagnetic, and is driven by 15 keV beam ions for which the ratio of perpendicular speed to the local Alfven speed, v ⊥ / v A , is 0.15, lower than in MCF plasmas from which beam-driven ICE has previously been observed. Our results are in good agreement with these observations. There is congruence between simulated ICE spectra, obtained in the saturated nonlinear regime of our computations, and observed ICE spectra; the underlying physics is essentially the same as in toroidal plasmas; and there is alignment with linear analytical theory where appropriate. The present work establishes a mapping from the cylindrical LAPD ICE observations to toroidal MCF ICE observations. The LAPD spectra are instances of beam-driven MCI-type ICE in its sub-Alfvenic, predominantly electrostatic manifestation, which has precedents in MCF stretching back to the 1990s. An interesting corollary is that, for many purposes, ICE in toroidal MCF plasmas ‘might as well’ be occurring in a cylinder.

Samant, O. (ORCID:0000000226055363)↗
DOE OSTI2021

Structure of Diisobutylene Maleic Acid Copolymer (DIBMA) and Its Lipid Particle as a “Stealth” Membrane-Mimetic for Membrane Protein Research

The study of membrane proteins remains challenging, especially in a native membrane environment. Recently, major progress has been made using maleic acid copolymers, such as styrene maleic acid, to purify membrane proteins and study them directly with native lipids associated with the membrane. Additional maleic acid copolymers, such as diisobutylene maleic acid (DIBMA) membrane-mimetic systems, are being developed and found to have improved spectroscopic properties and pH stability. We studied DIBMA and its lipid particles in solution to better understand its assembly, without and with the lipids, to provide an insight regarding how to use it in solution for better membrane extraction. Using small-angle neutron and X-ray scattering (SANS/SAXS), we show that DIBMA organizes into structures of different size scales at various concentrations and ionic strengths. The polymer performed reasonably well under most solvent conditions except in very low concentrations and high-salt conditions that could result in limited interaction with lipids. To explore DIBMA lipid particles as a suitable membrane-mimetic system for neutron scattering studies of membrane proteins, we measured and determined the contrast-matching point of DIBMA to be ~12% (v/v) D 2 O — similar to that of most protiated lipid molecules but distinct from that of regular protiated proteins — providing a natural contrast for separating their neutron scattering signals. Using SANS contrast variation, we demonstrated that the scattering from the whole lipid particle can be annihilated. Further, we determined that a well-defined lipid nanodisc structure with DIBMA was contrast-matched. These results demonstrate that the DIBMA lipid particle is an outstanding “stealth” membrane-mimetic for membrane proteins. The results provide a structural framework for understanding the organization and assembly process of the polymer itself and the lipid molecules. Such an understanding is imperative for structural techniques such as cryo-electron microscopy, nuclear magnetic resonance, small-angle scattering, and other biophysical techniques.

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

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Operator evolution from the similarity renormalization group and the Magnus expansion

The Magnus expansion is an efficient alternative to solving similarity renormalization group (SRG) flow equations with high-order, memory-intensive ordinary differential equation solvers. The numerical simplifications it offers for operator evolution are particularly valuable for in-medium SRG calculations, though challenges remain for difficult problems involving intruder states. Here we test the Magnus approach in an analogous but more accessible situation, which is the free-space SRG treatment of the spurious bound states arising from a leading-order chiral effective field theory (EFT) potential with very high cutoffs. In this work, we show that the Magnus expansion passes these tests and then use the investigations as a springboard to address various aspects of operator evolution that have renewed relevance in the context of the scale and scheme dependence of nuclear processes. These aspects include SRG operator flow with band- versus block-diagonal generators, universality for chiral EFT Hamiltonians and associated operators with different regularization schemes, and the impact of factorization arising from scale separation. Implications for short-range correlations physics and the possibilities for reconciling high- and low-resolution treatments of nuclear structure and reactions are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Factorization at subleading power and endpoint-divergent convolutions in h → γγ decay

t is by now well known that, at subleading power in scale ratios, factorization theorems for high-energy cross sections and decay amplitudes contain endpoint-divergent convolution integrals. The presence of endpoint divergences hints at a violation of simple scale separation. At the technical level, they indicate an unexpected failure of dimensional regularization and the $\overline{MS}$ subtraction scheme. In this work, we start a detailed discussion of factorization at subleading power within the framework of soft-collinear effective theory. As a concrete example, we factorize the decay amplitude for the radiative Higgs-boson decay h → γγ mediated by a b-quark loop, for which endpoint-divergent convolution integrals require both dimensional and rapidity regulators. We derive a factorization theorem for the decay amplitude in terms of bare Wilson coefficients and operator matrix elements. We show that endpoint divergences caused by rapidity divergences cancel to all orders of perturbation theory, while endpoint divergences that are regularized dimensionally can be removed by rearranging the terms in the factorization theorem. We use our result to resum the leading double-logarithmic corrections of order $α^n_sln^{2n+2}(-M^2_h/m^2_b)$ to the decay amplitude to all orders of perturbation theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Dislocation content of grain boundary phase junctions and its relation to grain boundary excess properties

In this work, we analyze the dislocation content of grain boundary (GB) phase junctions, i.e., line defects separating two different GB phases coexisting on the same GB plane. While regular GB disconnections have been characterized for a variety of interfaces, GB phase junctions formed by GBs with different structures and different numbers of excess atoms have not been previously studied. We apply a general Burgers circuit analysis to calculate the Burgers vectors b of junctions in two Σ5 Cu boundaries previously simulated with molecular dynamics. The Burgers vectors of these junctions cannot be described by the displacement shift complete (DSC) lattice alone. We show that, in general, the normal component of b is not equal to the difference in the GB excess volumes but contains another contribution from the numbers of GB atoms per unit area ΔN* required to transform one GB phase into another. In the boundaries studied, the latter component dominates and even changes the sign of b. We derive expressions for the normal and tangential components of b in terms of the DSC lattice vectors and the non-DSC part due to ΔN* and additional GB excess properties, including excess volume and shears. These expressions provide a connection between GB phase transformations driven by the GB free energy difference and the motion of GB junctions under applied normal and shear stresses. The proposed analysis quantifies b and therefore makes it possible to calculate the elastic part of the energy of these defects, evaluate their contribution to the nucleation barrier during GB phase transformations, and treat elastic interactions with other defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Construction of meta-GGA functionals through restoration of exact constraint adherence to regularized SCAN functionals

The strongly constrained and appropriately normed (SCAN) meta-GGA exchange–correlation functional is constructed as a chemical environment-determined interpolation between two separate energy densities: one describes single-orbital electron densities accurately and another describes slowly varying densities accurately. To conserve constraints known for the exact exchange–correlation functional, the derivatives of this interpolation vanish in the slowly varying limit. While theoretically convenient, this choice introduces numerical challenges that degrade the functional’s efficiency. We have recently reported a modification to the SCAN meta-GGA, termed restored-regularized-SCAN (r 2 SCAN) , that introduces two regularizations into SCAN, which improve its numerical performance at the expense of not recovering the fourth order term of the slowly varying density gradient expansion for exchange. Here, we show the derivation of a progression of density functional approximations [regularized SCAN (rSCAN), r++SCAN, r 2 SCAN, and r 4 SCAN] with increasing adherence to exact conditions while maintaining a smooth interpolation. The greater smoothness of r 2 SCAN seems to lead to better general accuracy than the additional exact constraint of SCAN or r 4 SCAN does.

, correlation energy↗
DOE OSTI2026

Quantum Ising model on (2+1)-dimensional anti–de Sitter space using tensor networks

We study the quantum Ising model on (2+1)-dimensional anti-de Sitter space using matrix product states (MPS) and matrix product operators (MPOs). We explore the bulk phase diagram of the theory on regular tessellations of hyperbolic space with coordination number seven and find disordered and ordered phases separated by a phase transition. We find that the boundary-boundary spin correlation function exhibits power law scaling deep in the disordered phase of the Ising model consistent with holography. At the critical point, we find the boundary entanglement entropy scales logarithmically with subsystem size but away from this, we see a linear scaling. In comparison, the full system exhibits a volume law scaling, which is expected in chaotic and/or highly connected systems. We also measure out of time ordered correlators (OTOCs) to explore the scrambling behavior of the theory.

Quantum spin models↗
DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗