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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Intermediate time sub-diffusion and stress relaxation in ring polymer melts

The slow dynamics of non-concatenated ring melts remains a frontier problem in polymer science with implications for many soft material environments including cellular biophysics. Here, in this work, we report large-scale simulations of model ring melts that analyze the monomer and center-of-mass (CM) mean square displacements (MSD) and stress relaxation function on intermediate time and length scales. The degree of dynamical slowing down is characterized by the maximally sub-diffusive fractional time scaling exponents. The data span an exceptionally wide range of ring degrees of polymerization and stiffnesses and are not successfully organized based on the classic measure linear chain entanglement, N/N e . Rather, we find that the crossover degree of polymerization, N D , based on ring macromolecular caging that successfully allows master curves to be constructed for the long-time CM self-diffusion constant also collapses these temporal dynamic scaling exponents. Different properties display different exponents and exhibit one or two regimes of linear variation with the logarithm of N D / N . A distinct crossover of the CM-MSD and stress relaxation exponents emerges at sufficiently large N or stiffness that is not found for the monomer MSD, indicating a novel form of dynamic decoupling. This crossover aligns with the predicted critical degree of polymerization for transitioning from a weak to strong caging regime, indicative of activated transport. The latter may reflect the emergence of an intermolecular collective contribution to stress in analogy with dense soft colloidal matter. Suggestions are made for future theoretical work to address the rich patterns of behavior discovered.

Anomalous diffusion↗
DOE OSTI2025

ESPPU INPUT: C$^3$ within the "Linear Collider Vision"

The Linear Collider Vision calls for a Linear Collider Facility with a physics reach from a Higgs Factory to the TeV-scale with $e^+e^{-}$ collisions. One of the technologies under consideration for the accelerator is a cold-copper distributed-coupling linac capable of achieving high gradient. This technology is being pursued by the C$^3$ collaboration to understand its applicability to future colliders and broader scientific applications. In this input we share the baseline parameters for a C$^3$ Higgs-factory and the energy reach of up to 3 TeV in the 33 km tunnel foreseen under the Linear Collider Vision. Recent results, near-term plans and future R&D needs are highlighted.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Linear in temperature resistivity in the limit of zero temperature from the time reparameterization soft mode

The most puzzling aspect of the ‘strange metal’ behavior of correlated electron compounds is that the linear in temperature resistivity often extends down to low temperatures, lower than natural microscopic energy scales. We consider recently proposed deconfined critical points (or phases) in models of electrons in large dimension lattices with random nearest-neighbor exchange interactions. The criticality is in the class of Sachdev–Ye–Kitaev models, and exhibits a time reparameterization soft mode representing gravity in dual holographic theories. We compute the low temperature resistivity in a large M limit of models with SU(M) spin symmetry, and find that the dominant temperature dependence arises from this soft mode. The resistivity is linear in temperature down to zero temperature at the critical point, with a co-efficient universally proportional to the product of the residual resistivity and the co-efficient of the linear in temperature specific heat. We argue that the time reparameterization soft mode offers a promising and generic mechanism for resolving the strange metal puzzle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

EpitopeVec: linear epitope prediction using deep protein sequence embeddings

Motivation: B-cell epitopes (BCEs) play a pivotal role in the development of peptide vaccines, immuno-diagnostic reagents and antibody production, and thus in infectious disease prevention and diagnostics in general. Experimental methods used to determine BCEs are costly and time-consuming. Therefore, it is essential to develop computational methods for the rapid identification of BCEs. Although several computational methods have been developed for this task, generalizability is still a major concern, where cross-testing of the classifiers trained and tested on different datasets has revealed accuracies of 51–53%. Results: We describe a new method called EpitopeVec, which uses a combination of residue properties, modified antigenicity scales, and protein language model-based representations (protein vectors) as features of peptides for linear BCE predictions. Extensive benchmarking of EpitopeVec and other state-of-the-art methods for linear BCE prediction on several large and small datasets, as well as cross-testing, demonstrated an improvement in the performance of EpitopeVec over other methods in terms of accuracy and area under the curve. As the predictive performance depended on the species origin of the respective antigens (viral, bacterial and eukaryotic), we also trained our method on a large viral dataset to create a dedicated linear viral BCE predictor with improved cross-testing performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A simplified integrated framework for predicting the economic impacts of feedstock variations in a catalytic fast pyrolysis conversion process

Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.

BIOMASS FUELS↗
DOE OSTI2020

DNA Reaction–Diffusion Attractor Patterns

Abstract Living systems can form and recover complex chemical patterns with precisely sized features in the ranges of tens or hundreds of microns. We show how designed reaction–diffusion processes can likewise produce precise patterns, termed attractor patterns, that reform their precise shape after being perturbed. We use oligonucleotide reaction networks, photolithography, and microfluidic delivery to form precisely controlled attractor patterns and study the responses of these patterns to different localized perturbations. Linear and “hill”‐shaped patterns formed and stabilized into shapes and at time scales consistent with reaction–diffusion models. When patterns were perturbed in particular locations with UV light, they reliably reformed their steady‐state profiles. Recovery also occurred after repeated perturbations. By designing the far‐from‐equilibrium dynamics of a chemical system, this study shows how it is possible to design spatial patterns of molecules that are sustained and regenerated by continually evolving towards a specific steady state configuration.

Dorsey, Phillip James↗
DOE OSTI2020

DNA Reaction–Diffusion Attractor Patterns

Living systems can form and recover complex chemical patterns with precisely sized features in the ranges of tens or hundreds of microns. We show how designed reaction-diffusion processes can likewise produce precise patterns, termed attractor patterns, that reform their precise shape after being perturbed. We use oligonucleotide reaction networks, photolithography and microfluidic delivery to form precisely controlled attractor patterns and study the responses of these patterns to different localized perturbations. Linear and ‘hill’-shaped patterns formed and stabilized into shapes and at time scales consistent with reaction-diffusion models. When patterns were perturbed in particular locations with UV light, they reliably reformed their steady state profiles. Recovery also occurred after repeated perturbations. As a result, by designing the far-from-equilibrium dynamics of a chemical system, this study shows how it is possible to design spatial patterns of molecules that are sustained and regenerated by continually evolving towards a specific steady state configuration

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

Droplet breakup by multimodal nonlinear Rayleigh Taylor instability

A droplet impacted by a shock wave will undergo a process of fragmentation due to the development of interfacial hydrodynamic instabilities. The interface experiences variable acceleration and shear that result in the development of both inertial (Rayleigh–Taylor) and shear (Kelvin–Helmholtz) instabilities. These perturbations grow in time and drive the fragmentation and breakup of the deformed droplet. Experiments are performed on nominally 0.86 mm water droplet subjected to a Mach 7.6 detonation wave, resulting in a high Weber number (~36,000) breakup event. Perturbation growth is measured from a series of high-speed (> MHz) shadowgraph images. It is proposed that, given the size of the large-scale perturbations observed in experiments, these instabilities are growing in the non-linear regime and can be described by bubble-merger models for nonlinear mixing. Calculations are performed for the growth rates and size of these instabilities using deformation and external flow models to establish the time-dependent boundary conditions. The concurrence of the measured perturbation widths and the predictions of the simple model lend credence to the theory. This novel approach serves to open a new avenue in the characterization of droplet breakup via hydrodynamic instabilities.

Droplet breakup↗
DOE OSTI2020

A scalable matrix-free spectral element approach for unsteady PDE constrained optimization using PETSc/TAO

In this work, we provide a new approach for the efficient matrix-free application of the transpose of the Jacobian for the spectral element method for the adjoint-based solution of partial differential equation (PDE) constrained optimization. This results in optimizations of nonlinear PDEs using explicit integrators where the integration of the adjoint problem is not more expensive than the forward simulation. Solving PDE constrained optimization problems entails combining expertise from multiple areas, including simulation, computation of derivatives, and optimization. The Portable, Extensible Toolkit for Scientific computation (PETSc) together with its companion package, the Toolkit for Advanced Optimization (TAO), is an integrated numerical software library that contains an algorithmic/software stack for solving linear systems, nonlinear systems, ordinary differential equations, differential algebraic equations, and large-scale optimization problems and, as such, is an ideal tool for performing PDE-constrained optimization. This paper describes an efficient approach in which the software stack provided by PETSc/TAO can be used for large-scale nonlinear time-dependent problems. Time integration can involve a range of high-order methods, both implicit and explicit. The PDE-constrained optimization algorithm used is gradient-based and seamlessly integrated with the simulation of the physical problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Patterned anodes with sub-millimeter spatial resolution for large-area MCP-based photodetector systems

Micro-channel-plate-based photo-detectors are unique in being capable of covering very large areas such as those required in elementary particle and nuclear physics, while providing sub-millimeter space resolution, time resolutions of less than 10 picoseconds for charged particles, and time resolutions of 30 psec–50 psec for single photons. In such systems the electronic channel count is a major cost driver. Incorporating a capacitively-coupled anode allows the use of external pickup electrodes with patterns of individual channels optimized for occupancy, rate, and time/space resolution. The signal pickup antenna can be economically implemented as a printed circuit card with a 2-dimensional array of pads for high-occupancy/high-rate applications such as in particle colliders and medical imaging, or a 1-dimensional array of strips for a lower channel count in low-occupancy/low-rate applications such as large neutrino detectors. In this paper we present pad patterns that enhance signal-sharing between pads to lower the channel count per unit area in large-area systems by factors up to 4, while maintaining spatial resolutions of approximately 100 to 200 μ m for charged particles and 400 to 1000 μ m for single photons. Patterns that use multiple signal layers in the signal-pickup board can lower the channel count even further, moving the scaling behavior in the number of pads versus total area from quadratic to linear.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Gyrokinetic simulation of turbulence and transport in the SPARC tokamak

The turbulence and transport expected in the SPARC tokamak Primary Reference Discharge (PRD) [P. Rodriguez-Fernandez et al., J. Plasma Phys. 86, 865860503 (2020)] have been investigated with the gyrokinetic code CGYRO [J. Candy et al., J. Comput. Phys. 324, 73–93 (2016)]. Linear and nonlinear simulations that focus on ion (kθρs<1.0) and electron-scale (kθρs>1.0) turbulence were used to probe the nature of the turbulence and the resulting transport in the fusion core. It is found that in the SPARC PRD, ion temperature gradient (ITG) turbulence is expected to dominate transport over most of the profile with some potential trapped electron mode impact in the near edge. Stiff turbulence is observed over a part of the plasma core such that SPARC's ion temperature profile will likely be pinned to just above the critical gradient for ITG. The role of electromagnetic turbulence, rotation, and electron-scale turbulence was investigated to provide some insight into the physics required to accurately predict SPARC performance via gyrokinetics. Additionally, predictions of impurity peaking for potential low- and high-Z SPARC first-wall materials are probed using ion-scale simulation. The dominance of low-k turbulence in SPARC provides a potential opportunity for more tractable prediction of plasma profiles using nonlinear gyrokinetics. This work is the first step toward full gyrokinetic profile prediction of SPARC kinetic profiles and the resulting fusion power and plasma gain.

Howard, N. T. (ORCID:0000000287876309)↗
DOE OSTI2025

Symplectic neural network and its application to charged particle dynamics in electromagnetic fields

Recently, machine learning models have shown many successes in various applications in science and technology. In this work, we focus on the charged particle dynamics, with the development of a class of symplectic neural networks, including a linear version, SympMat, and a nonlinear version, HénonNet. Both are designed to preserve the structure of Hamiltonian systems. We show that they can be used to model relevant Hamiltonian systems of interest in plasma physics and astrophysics, for linear and nonlinear charged particle dynamics, with the potential to bridge multi-scale simulations. These symplectic neural networks are adapted to the applications in plasma simulations and particle-wave interaction with parametric dependence and periodicity, where we have investigated their performance and accuracy. In particular, SympMat is shown to outperform the traditional Boris particle pusher down to the sub-gyroperiod scale in the case of charged particles in uniform magnetic fields. HénonNet successfully predicts the hot electron distribution, which is validated against theoretical results. These results highlight the potential of symplectic neural networks as a trajectory integrator for particle-in-cell simulations or a fast surrogate to replace conventional numerical schemes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Mitigating baryonic effects with a theoretical error covariance

ABSTRACT One of the primary sources of uncertainties in modelling the cosmic-shear power spectrum on small scales is the effect of baryonic physics. Accurate cosmology for stage-IV surveys requires knowledge of the matter power spectrum deep in the non-linear regime at the per cent level. Therefore, it is important to develop reliable mitigation techniques to take into account baryonic uncertainties if information from small scales is to be considered in the cosmological analysis. In this work, we develop a new mitigation method for dealing with baryonic physics for the case of the shear angular power spectrum. The method is based on an augmented covariance matrix that incorporates baryonic uncertainties informed by hydrodynamical simulations. We use the results from 13 hydrodynamical simulations and the residual errors arising from a fit to a ΛCDM model using the extended halo model code HMCode to account for baryonic physics. These residual errors are used to model a so-called theoretical error covariance matrix that is added to the original covariance matrix. In order to assess the performance of the method, we use the 2D tomographic shear from four hydrodynamical simulations that have different extremes of baryonic parameters as mock data and run a likelihood analysis comparing the residual bias on Ωm and σ8 of our method and the HMCode for an LSST-like survey. We use different modelling of the theoretical error covariance matrix to test the robustness of the method. We show that it is possible to reduce the bias in the determination of the tested cosmological parameters at the price of a modest decrease in the precision.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Single classical field description of interacting scalar fields

In this report we test the degree to which interacting Bosonic systems can be approximated by a classical field as total occupation number is increased. This is done with our publicly available code repository, QIBS, a new massively parallel solver for these systems. We use a number of toy models well studied in the literature and track when the classical field description admits quantum corrections, called the quantum breaktime. This allows us to test claims in the literature regarding the rate of convergence of these systems to the classical evolution. We test a number of initial conditions, including coherent states, number eigenstates, and field number states. We find that of these initial conditions, only number eigenstates do not converge to the classical evolution as occupation number is increased. We find that systems most similar to scalar field dark matter exhibit a logarithmic enhancement in the quantum breaktime with total occupation number. Systems with contact interactions or with field number state initial conditions, and linear dispersions, exhibit a power law enhancement. Finally, we find that the breaktime scaling depends on both model interactions and initial conditions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Bayesian approach to evaluation of soil biogeochemical models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MFIX-Exa: Performance prediction of multiphase energy conversion devices

MFIX-Exa targets the high-fidelity CFD-DEM model in which particles are unresolved by the fluid grid (typically using a fluid mesh approximately twice the particle diameter) but discrete particle dynamics including collisions are resolved with a simple linear-spring dashpot model. The fluid field is solved with a modern low Mach number formation of a cell-centered, nodal-pressure approximate projection method using a Godunov scheme . Physics capabilities include: complex geometries via embedded boundaries (EBs), open and closed system ideal gas equations of state, species transport and heterogeneous chemi- cal reactions. MFIX-Exa is built on the AMReX software framework, the ECP Block-Structured Adaptive Mesh Refinement (AMR) Co-Design Center, which allows the code to be portable and performant.Further integration into the ECP ecosystem includes HYPRE linear solvers and Ascent for in situ visualization. Current project efforts focus on scaling up realistic simulations to the KPP of the challenge problem: NETs 50kW chemical looping reactor, shakedown and scaling on JSLE and OLCF TDS systems and reaching for any remaining performance improvements.

Musser, Jordan↗
DOE OSTI2024

Solving the Grid Optimization Competition Challenge 3 Problem

The Grid Optimization Competition Challenge 3 Problem posed a multiperiod security-constrained unit commitment problem with base-case AC power flow. The problem formulation includes binary unit commitment decisions, nonlinear AC power flow and balance, dispatchable loads, and linearized contingency real power flow, among other features. This talk will present a modified consensus ADMM algorithm, which splits the problem into mixed-integer linear and nonlinear components, as a heuristic solution method for this large-scale mixed integer nonlinear program. We will present some computational results from the competition for our implementation and reflect on the challenges of participating the grid optimization competition.

AC power flow↗
DOE OSTI2022

An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation

Quantum computing is a promising technology that harnesses the peculiarities of quantum mechanics to deliver computational speedups for some problems that are intractable to solve on a classical computer. Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in terms of chip size and error rates. Shallow quantum circuits with uncomplicated topologies are essential for successful applications in the NISQ era. In this work, based on matrix analysis, we derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions. The depth of the compressed circuits is independent of simulation time and grows linearly with the number of spins. The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond O(10 3 ) spins. The resulting quantum circuits have a simple nearest-neighbor topology, which makes them ideally suited for NISQ devices.

Hamiltonian simulation↗