SEARCH · Engineering Papers

Results for “Differentiable physics”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Advancing Molecular Weight Determination of Lignin by Multi-Angle Light Scattering

Due to the complexity and recalcitrance of lignin, its chemical characterization is a key factor preventing the valorization of this abundant material. Multi-angle light scattering (MALS) is becoming a sought-after technique for absolute molecular weight (MW) determination of polymers and proteins. Lignin is a suitable candidate for MW determination via MALS, yet further investigation is required to confirm its absolute MW values and molecular size. Studies aiming to break down lignin into a variety of renewable products will benefit greatly from a simple and reliable determination method like MALS. Recent pioneering studies, discussed in this review, addressed several key challenges in lignin’s MW characterization. Nevertheless, some lignin-specific issues still need to be considered for in-depth characterization. This study explores how MALS instrumentation manages the complexities of determining lignin’s MW, e.g., with simultaneous fractionation and fluorescence interference mitigation. Additionally, we rationalize the importance of a more detailed light scattering analysis for lignin characterization, including aspects like the second virial coefficient and radius of gyration.

differential refractive index increment↗

DOE OSTI2025

Status of the PrimEx $\eta$ experiment at Jefferson Lab

The GlueX detector in the experimental Hall D at Jefferson Lab offers a unique opportunity to perform a measurement of the decay width of eta mesons through the Primakoff effect. The experiment complements the physics program at Jefferson Lab on measuring the decay width of light pseudoscalar mesons via the Primakoff process. The goal of the experiment is to measure differential cross sections of eta mesons at forward angles using a beam of tagged photons incident on a liquid 4He target, which will be used for the extraction of the decay width. This measurement is vital for understanding fundamental properties like the ratios of the light quark masses and the eta - eta' mixing angle, and will provide an important test of chiral symmetry breaking in QCD. Our experimental results will help reduce uncertainties on partial widths of all other eta decays. The experiment collected data during three physics runs between 2019 and 2022. We will give an overview of the PrimEx eta experiment and the current status of our data analyses. We will also discuss the feasibility of conducting future Primakoff measurements in light of the recent upgrade of the GlueX forward calorimeter and the potential accelerator energy upgrade to 22 GeV.

Somov, Alexander↗

DOE OSTI2021

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

Time- and rate-dependent material functions in non-Newtonian fluids in response to different deformation fields pose a challenge in integrating different constitutive models into conventional computational fluid dynamic platforms. Considering their relevance in many industrial and natural settings alike, robust data-driven frameworks that enable accurate modeling of these complex fluids are of great interest. The main goal is to solve the coupled Partial Differential Equations (PDEs) consisting of the constitutive equations that relate the shear stress to the deformation and fully capture the behavior of the fluid under various flow protocols with different boundary conditions. Here, in this work, we present non-Newtonian physics-informed neural networks (nn-PINNs) for solving systems of coupled PDEs adopted for complex fluid flow modeling. The proposed nn-PINN method is employed to solve the constitutive models in conjunction with conservation of mass and momentum by benefiting from Automatic Differentiation (AD) in neural networks, hence avoiding the mesh generation step. nn-PINNs are tested for a number of different complex fluids with different constitutive models and for several flow protocols. These include a range of Generalized Newtonian Fluid (GNF) empirical constitutive models, as well as some phenomenological models with memory effects and thixotropic timescales. nn-PINNs are found to obtain the correct solution of complex fluids in spatiotemporal domains with good accuracy compared to the ground truth solution. We also present applications of nn-PINNs for complex fluid modeling problems with unknown boundary conditions on the surface, and show that our approach can successfully recover the velocity and stress fields across the domain, including the boundaries, given some sparse velocity measurements.

42 ENGINEERING↗

DOE OSTI2023

Correspondence between open bosonic systems and stochastic differential equations

Bosonic mean-field theories can approximate the dynamics of systems of $n$ bosons provided that $n \gg 1$. Here, we show that there can also be an exact correspondence at finite $n$ when the bosonic system is generalized to include interactions with the environment and the mean-field theory is replaced by a stochastic differential equation. When the $n \to \infty$ limit is taken, the stochastic terms in this differential equation vanish, and a mean-field theory is recovered. Besides providing insight into the differences between the behavior of finite quantum systems and their classical limits given by $n \to \infty$, the developed mathematics can provide a basis for quantum algorithms that solve some stochastic nonlinear differential equations. We discuss conditions on the efficiency of these quantum algorithms, with a focus on the possibility for the complexity to be polynomial in the log of the stochastic system size. A particular system with the form of a stochastic discrete nonlinear Schrödinger equation is analyzed in more detail.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Minijet quenching in a concurrent jet + hydro evolution and the nonequilibrium quark-gluon plasma

Minijets, created by perturbative hard QCD collisions at moderate energies, can represent a significant portion of the total multiplicity of a heavy-ion collision event. Since their transverse momenta are initially larger than the typical saturation scale describing the bulk of the equilibrating quark-gluon plasma (QGP), they ought to be described through the physics of parton energy loss. Indeed, their typical stopping distances are larger than the usual hydrodynamization time, so they do not in general hydrodynamize at the same pace than the bulk of the collision. Therefore, in general minijets cannot be described solely by a unique preequilibrium stage that bridges the initial, overoccupied glasma state, with the hydrodynamical evolution. In this work we make use of a new concurrent minijet+hydrodynamic framework in which the properties of the hydrodynamically evolving QGP are modified due to the injection of energy and momentum from the minijets. We study the system for different choices of the minimum transverse momentum associated with minijet production. In order to achieve a realistic description of charged particle multiplicity, the amount of entropy associated to the low- x initial state needs to be reduced. Moreover, the fact that the injected momentum from the randomly oriented minijets is not correlated with the spatial gradients of the system reduces overall flow, and the value of the QGP transport coefficients needs to be reduced accordingly in order to describe the measured flow coefficients in experiments. They are, in effect, an important new source of fluctuations, resulting in a spikier, notably modified hydrodynamical evolution when compared to the scenario in which the presence of minijets is ignored. We avow that their abundance makes it necessary to include their physics in holistic descriptions of heavy-ion collisions. We discuss the impact of the minijets on a number of observables, such as p T spectra and p T -differential flow v n for a wide range of centrality classes. In contrast to elliptic, triangular or quadrangular flow, here we find that directed flow, v 1 , has the strongest potential to discriminate between different minijet production rates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Rindler fluids from gravitational shockwaves

We study a correspondence between gravitational shockwave geometry and its fluid description near a Rindler horizon in Minkowski spacetime. Utilizing the Petrov classification that describes algebraic symmetries for Lorentzian spaces, we establish an explicit mapping between a potential fluid and the shockwave metric perturbation, where the Einstein equation for the shockwave geometry is equivalent to the incompressibility condition of the fluid, augmented by a shockwave source. Then we consider an Ansatz of a stochastic quantum source for the potential fluid, which has the physical interpretation of shockwaves created by vacuum energy fluctuations. Under such circumstance, the Einstein equation, or equivalently, the incompressibility condition for the fluid, becomes a stochastic differential equation. By smearing the quantum source on a stretched horizon in a Lorentz invariant manner with a Planckian width (similarly to the membrane paradigm), we integrate fluctuations near the Rindler horizon to find an accumulated effect of the variance in the round-trip time of a photon traversing the horizon of a causal diamond.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗

DOE OSTI2025

InAs sidewall tunnel diodes enabled by surface states

Negative differential resistance (NDR), where the device current decreases with increasing bias voltage, is a representative phenomenon where quantum mechanics induces counterintuitive physical behavior and offers promising applications such as high-frequency oscillators, amplifiers, and multilevel logic circuits. While the NDR behavior has been extensively studied in various materials and devices, the role of surface properties in NDR, particularly in InAs-based diodes, remains underexplored. In this work, we report the observation of NDR in vertically structured InAs p + n diodes that exhibit a peak-valley current ratio of ∼6, which is suitably high for applications. Circumference-normalized current–voltage characterization revealed that the NDR originates from band-to-band tunneling between the valence band of p + -InAs and the conduction band of an n + -InAs surface, where the n + surface is due to surface states on otherwise n − -InAs. In addition, by comparing devices with various surface passivation methods (without intentional passivation, benzocyclobutene polymer, and silicon nitride), we found that the surface termination significantly affects the NDR characteristics. We present an equivalent circuit model to explain the observed device behavior. These findings offer insights into surface-enabled NDR phenomena and present new knobs for engineering NDR devices.

Circuit theorems↗

DOE OSTI2023

Differential charge radii: Proton-neutron interaction effects

The analysis of self-consistency and proton-neutron interaction effects in the buildup of differential charge radii were carried out in covariant density functional theoretical calculations without pairing interaction on the example of selected configurations in the Pb isotopes. The proton-neutron interaction of neutron(s) added to the neutron 𝑁 = 126 core and the protons forming the 𝑍 = 82 proton core is responsible for a major contribution to the buildup of differential charge radii. It depends on the products of proton and neutron wave functions and thus on their nodal structure. This interaction leads to a redistribution of single-particle density of occupied proton states which in turn modifies the charge radii. Furthermore, the microscopic origin of this redistribution and its consequences for differential charge radii were investigated for the first time. Self-consistency effects affecting the shape of proton potential, total proton densities and the energies of the single-particle proton states provide only minor contribution to differential charge radii.

190 ≤ A ≤ 219↗

DOE OSTI2024

Holographic odderon at TOTEM?

We consider the contribution of the Odderon to diffractive p p and p p ¯ elastic scattering at large center of mass energy. We identify the Odderon and Pomeron with the Reggeized 1 ± − and 2 + + glueballs in the bulk, respectively. We use for the gravity dual description the repulsive wall model, to account for the proper Gribov diffusion for off-forward scattering. The eikonalized and unitarized amplitudes exhibit a vanishingly small rho-parameter, and a slope parameter fixed by twice the closed string slope. The results for the differential and total cross sections are compared to the empirical results reported recently by the TOTEM collaboration. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2022

Latent Space Dynamics Identification

LaSDI is a data-driven physical simulation software that forms a latent space for a given high-fidelity model and discovers a set of ordinary differential equations for the latent space dynamics. It allows a fast and accurate solution process, which is useful for multi-query decision making applications, such as design optimization and uncertainty quantification. The performance of the LaSDI framework is demonstrated on four different problems, i.e., 1D and 2D Burgers equations, nonlinear heat conduction, and radial advection problems. Both linear and nonlinear compression techniques, such as neural network and proper orthogonal decomposition, are used to form a latent space. A concept of local dynamics identification procedure is introduced to enable a parametric model, which enhances the accuracy level over a given parameter space.

Fries, William↗

DOE OSTI2026

Design of digital acquisition for beam current monitor

As a part of the Proton Improvement Plan – II (PIP-II) at Fermilab, instrumentation systems are being modernized to take advantage of the higher speeds and ease of use offered by standardized embedded systems like MicroTCA. A rear-transition module (RTM) is being designed to interface with said embedded systems. In each of the four identical channels on the RTM, the differential signal from an alternating-current current transformer (ACCT) transimpedance amplifier will again be amplified by a differential operation-amplifier, then filtered by a low-pass topology. The conditioned signal is then digitized at a maximum of 10MS/s by an analog to digital converter (ADC) integrated circuit. After digitization, the ADC passes the data to an off the shelf AdvancedMC (AMC) Xilinx FPGA module using low voltage differential signals. This paper will describe the simulation of analog circuitry for signal conditioning, simulation of digital signal integrity based on physical design as well as verification of design characteristics critical to signal integrity. This work aims to create a methodology that can be applied to future RTMs requiring application of high-speed digital design principles.

White, R.Turner [Fermilab]↗

DOE OSTI2024

A Comprehensive Analysis of PINNs for Power System Transient Stability

The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face limitations due to their time-consuming and computationally demanding nature. This paper introduces physics-informed Neural Networks (PINNs) as a promising solution for these challenges, especially in scenarios with limited data availability and the need for high computational speed. PINNs offer a novel approach for complex power systems by incorporating additional equations and adapting to various system scales, from a single bus to multi-bus networks. Our study presents the first comprehensive evaluation of physics-informed Neural Networks (PINNs) in the context of power system transient stability, addressing various grid complexities. Additionally, we introduce a novel approach for adjusting loss weights to improve the adaptability of PINNs to diverse systems. Our experimental findings reveal that PINNs can be efficiently scaled while maintaining high accuracy. Furthermore, these results suggest that PINNs significantly outperform the traditional ode45 method in terms of efficiency, especially as the system size increases, showcasing a progressive speed advantage over ode45.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Design of Digital Acquisition for Beam Current Monitor

As a part of the Proton Improvement Plan II (PIP-II) at Fermilab, instrumentation systems are being modernized to take advantage of the higher speeds and ease of use offered by standardized embedded systems like MicroTCA. A rear-transition module (RTM) is being designed to interface with said embedded systems. In each of the four identical channels on the RTM, the differential signal from an alternating-current current transformer (ACCT) transimpedance amplifier will again be amplified by a differential operation-amplifier, then filtered by a low-pass topology. The conditioned signal is then digitized at a maximum of 10MS/s by an analog to digital converter (ADC) integrated circuit. After digitization, the ADC passes the data to an off the shelf AdvancedMC (AMC) Xilinx FPGA module using low voltage differential signals. This paper will describe the simulation of analog circuitry for signal conditioning, simulation of digital signal integrity based on physical design as well as verification of design characteristics critical to signal integrity. This work aims to create a methodology that can be applied to future RTMs requiring application of high-speed digital design principles.

White, Turner [Fermilab] (ORCID:0009000348159256)↗

DOE OSTI2023

NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training

This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed framework uses the non-dominated sorting genetic algorithm (NSGA-II) to enable traditional stochastic gradient optimization algorithms (e.g., ADAM) to escape local minima effectively. Additionally, the NSGA-II algorithm enables satisfying the initial and boundary conditions encoded into the loss function during physics-informed training precisely. We demonstrate the effectiveness of our framework by applying NSGA-PINN to several ordinary and partial differential equation problems. In particular, we show that the proposed framework can handle challenging inverse problems with noisy data.

Lu, Binghang (ORCID:0009000160016632)↗

DOE OSTI2024

A Comprehensive Analysis of PINNs for Power System Transient Stability

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Measurement of the Medium Energy NuMI Flux Using the Low-$\nu$ and High-$\nu$ Methods at MINERvA

As we continue to improve our experimental capabilities, the measurements we make of accelerator-based neutrino phenomena are becoming increasingly limited by systematic, rather than statistical uncertainties. Among these are uncertainties corresponding to our understanding of the neutrino flux and to the models that we use to simulate neutrino interactions. These already are significant for currently-running neutrino experiments, and will become more important for next-generation neutrino experiments such as DUNE. Dedicated efforts at experiments such as MINERvA are pursuing improved comprehensive modeling of neutrino cross sections and improving our understanding of how to precisely measure neutrino fluxes. This thesis discusses these themes in the context of MINERvA, a neutrino scattering experiment which was located on-axis in the NuMI beamline at Fermilab. A pair of \textit{in situ} measurements of the flux are presented which utilize Medium Energy $\nu_{\mu}$ data colle cted at MINERvA between 2013 and 2017. The low-$\nu$ technique is used to make a direct measurement of the flux from low-inelasticity events, and that flux is used to measure the total charged-current inclusive cross section. The novel ``high-$\nu$'' technique is used to make a second direct measurement of the flux from a complementary selection of high-inelasticity events, and that flux is used to measure a double-differential cross section of charged-current quasielastic-like events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images

Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.

Koch, James V.↗