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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Uncertainty amplification due to density/refractive index gradients in background-oriented schlieren experiments

Here, we theoretically analyze the effect of density/refractive index gradients on the measurement precision of background-oriented schlieren (BOS) experiments by deriving the Cramer–Rao lower bound (CRLB) for the 2D centroid estimation process. A model is derived for the diffraction limited image of a dot viewed through a medium containing density gradients that includes the effect of the experimental parameters such as the magnification and f-number. It is shown using the model that nonlinearities in the density gradient field lead to blurring of the dot image. This blurring amplifies the effect of image noise on the centroid estimation process, leading to an increase in the CRLB and a decrease in the measurement precision. The ratio of position uncertainties of a dot in the reference and gradient images is shown to be a function of the ratio of the dot diameters and dot intensities. We termed this parameter the amplification ratio (A F ), and a methodology for reporting position uncertainties in tracking-based BOS measurements is proposed. The theoretical predictions of the dot position estimation variance from the CRLB are compared to ray tracing simulations, and agreement is obtained. The uncertainty amplification is also demonstrated on experimental BOS images of flow induced by a spark discharge, where it is seen that regions of high amplification ratio correspond to regions of density gradients. This analysis elucidates the dependence of the position uncertainty on density and refractive index gradient-induced distortion parameters, provides a methodology for accounting its effect on uncertainty quantification and provides a framework for optimizing experiment design.

42 ENGINEERING↗

DOE OSTI2022

Approximating a linear multiplicative objective in watershed management optimization

Implementing management practices in a cost-efficient manner is critical for regional efforts to reduce the amount of pollutants entering the Chesapeake Bay. We study the problem of selecting a subset of practices that minimizes pollutant load—subject to budgetary and environmental constraints—as simulated in a widely used regulatory watershed model. Mimicking the computation of pollutant load in the regulatory model, we formulate this problem as a continuous optimization model with a linear multiplicative objective function and linear constraints. To lay the groundwork for incorporating additional stakeholder requirements in the future, especially those that would require integer variables, we present and study a continuous linear optimization model that approximates the nonlinear model. The linear model, which requires an exponential number of variables, arises naturally as an alternative model for the same underlying physical process. We examine the theoretical behavior of these optimization models and investigate restrictions of the linear model to handle its large number of variables. Through extensive computational tests on real and randomly generated instances, we demonstrate that the linear model and its restrictions provide optimal solutions close to those of the nonlinear model in practice, despite poor approximation properties in the worst case. We conclude that the linear model—together with our approach to handling its large number of variables—provides a viable framework from which to extend the optimization model to better meet the needs of the Chesapeake Bay watershed management stakeholders.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

Nonlinear spatial equilibration of an externally excited instability wave in a free shear layer

A two-dimensional disturbance evolving from a strictly linear, finite-growth-rate instability wave, with nonlinear effects first becoming important in the critical layer is considered. The analysis is carried out for a general weakly nonparallel mean flow using matched asymptotic expansions. The flow in the critical layer is governed by a nonlinear vorticity equation which includes a spatial-evolution term. As in Goldstein and Hultgren (1988), the critical layer ages into a quasi-equilibrium one and the initial exponential growth of the instability wave is converted into a weak algebraic growth during the roll-up process. This leads to a next stage of evolution where the instability-wave growth is simultaneously affected by mean-flow divergence and nonlinear critical-layer effects and is eventually converted to decay. Expansions for the various streamwise regions of the flow are combined into a single composite formula accounting for both shear-layer spreading and nonlinear critical-layer effects and good agreement with the experimental results of Thomas and Chu (1989) and Freymuth (1966) is demonstrated.

Hultgren, Lennart S.↗

DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Revisiting diffusion-based moisture functions: why do they fail?

Microbial activities in soil are controlled by the balance between physical and biological mechanisms that are currently implied by soil moisture-respiration relationships, but not explicitly represented. Diffusion based moisture functions focus on physical controls, while substrate kinetics based models primarily address biological drivers. How to harmonize these two types of models for robust predictions of soil respiration and other processes has not been addressed. Here we derived a generalizable analytical solution of diffusion-limited nonlinear microbial uptake kinetics, in which microbial metabolic rates were governed by both physical and biological drivers via substrate diffusion and microbial affinity. We proposed a soil trait concept to integrate both physical and biological perspectives into one unified function, and tested it using both laboratory and field based observations from a variety of soils. This approach provides a tractable and scalable approach that incorporates soil heterogeneity and functional complexity in macroscopic model descriptions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A Statistician’s Overview of Physics-Informed Neural Networks for Spatio-Temporal Data

The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that include mechanistic information, such as would be available from partial differential equations, with a strong focus on the quantification of uncertainty associated with data, process, and parameters. Here, we give a brief review of several paradigms and focus our attention on Bayesian implementations given they naturally accommodate uncertainty quantification. We then show that it is straight-forward to include the Bayesian PINN (B-PINN) within the Bayesian hierarchical model (BHM) framework that has long been considered for modeling dynamic spatio-temporal processes. Such a BHM-PINN is illustrated via a simulation study in which a latent nonlinear Burgers’ equation PDE governs the dynamics of Poisson distributed spatio-temporal data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian↗

DOE OSTI2020

Spin-dependent Pomeron and Odderon in elastic proton–proton scattering

We introduce a new model of near-forward elastic proton-(anti)proton scattering at high energy based on the modern formulation of Pomeron and Odderon in terms of Wilson lines and generalized TMDs (GTMDs). We compute the helicity-dependent elastic amplitudes $\phi$1,2,3,4,5 in this model and study their energy dependence from the nonlinear small-x evolution equations. While both Pomeron and Odderon contribute to helicity-flip processes in general, in the forward limit t = 0 only the double helicity-flip amplitude $\phi$2, dominated by the spin-dependent Odderon, survives. This may effect the extraction of the ρ parameter as well as the total cross section in the LHC energy domain and beyond

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Photoemission Induced Plasma Breakdown (Update)

Laser-induced photoemission of electrons offers opportunities to trigger and control plasmas and discharges. However, the underlying mechanisms are not sufficiently characterized to be fully utilized. Photoemission is highly nonlinear, achieved through multiphoton absorption, above threshold ionization, photo-assisted tunneling, etc., where the dominant process depends on the work function of the material, photon energy and associated fields, surface heating, background fields, etc. To characterize the effects of photoemission on breakdown, breakdown experiments were performed and interpreted using a 0D plasma discharge circuit model and quantum model of photoemission.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Towards multi-qudit quantum processor using superconducting RF cavities

Superconducting radio-frequency (SRF) cavities made using high-purity Niobium can reach single photon lifetimes longer than a second with proper treatment [1]. These resonator cavities are thus an excellent choice for storing higher-dimensional quantum states. However, preparing and manipulating quantum states with larger photon numbers require a nonlinear component, like a transmon, with a sufficiently long coherence time. With the advancement in materials processing enabling coherence times for transmons exceeding hundreds of micro-seconds, one can encode and process quantum information using d-levels (qudit) of the resonators. In this talk, we will present preliminary results showing the control of a single-qudit and our progress toward building multi-qudit architecture using multi-mode SRF cavities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1974

Optimum propellant usage for reaction jet systems of space vehicles

The on-off type control for reaction jet systems is proven to be the optimal fuel scheme. However, due to the nonlinear characteristics of this type control, no direct method of solution is known for this optimal process being applied to the attitude control of space vehicles. This paper will discuss the optimization of fuel usage for an attitude control of space vehicles. A computation technique is developed for the calculation of optimal control law. The method of calculus of variations is applied to the estimation of the changes of performance index as well as terminal constraints. Thus an algorithm is obtained by the steepest descent method. A numerical example is given in the paper.

Liu, T. C.↗

NASA NTRS1977

Application of symbolic/numeric matrix solution techniques to the NASTRAN program

The matrix solving algorithm of any finite element algorithm is extremely important since solution of the matrix equations requires a large amount of elapse time due to null calculations and excessive input/output operations. An alternate method of solving the matrix equations is presented. A symbolic processing step followed by numeric solution yields the solution very rapidly and is especially useful for nonlinear problems.

Buturla, E. M.↗

NASA NTRS1980

Surface tension-driven convections

The study of time-dependent flows generated by surface-tension gradients in thin liquid layers is reported. Numerical techniques for dealing with the complete float-zone process were investigated. In particular, finite element methods with fast iterative procedures, using multipoint constraints and nonlinear optimization were examined.

Ostrach, S.↗

NASA NTRS1982

The prediction of long term viscoelastic properties of fiber reinforced plastics

A method for the experimental and analytical accelerated characterization of long term viscoelastic properties is presented. The time-temperature-stress superposition principle (TTSSP) is shown to serve as the basis for determining long term compliance data from short term creep test results. Nonlinear viscoelastic theories are discussed to provide the framework for the mathematically modeling of such a process. A time dependent Tsai-Hill-Zhurkov failure theory is used to determine long term failure properties from short term data. Compliance and failure data are incorporated in an incremental lamination theory to make long term laminate predictions. Comparisons are made between theory and experiment.

Brinson, H. F.↗

NASA NTRS1989

Computational Methods for Structural Mechanics and Dynamics, part 1

The structural analysis methods research has several goals. One goal is to develop analysis methods that are general. This goal of generality leads naturally to finite-element methods, but the research will also include other structural analysis methods. Another goal is that the methods be amenable to error analysis; that is, given a physical problem and a mathematical model of that problem, an analyst would like to know the probable error in predicting a given response quantity. The ultimate objective is to specify the error tolerances and to use automated logic to adjust the mathematical model or solution strategy to obtain that accuracy. A third goal is to develop structural analysis methods that can exploit parallel processing computers. The structural analysis methods research will focus initially on three types of problems: local/global nonlinear stress analysis, nonlinear transient dynamics, and tire modeling.

Stroud, W. Jefferson↗

NASA NTRS1990

Optimal fabrication processes for unidirectional metal-matrix composites: A computational simulation

A method is proposed for optimizing the fabrication process of unidirectional metal matrix composites. The temperature and pressure histories are optimized such that the residual microstresses of the composite at the end of the fabrication process are minimized and the material integrity throughout the process is ensured. The response of the composite during the fabrication is simulated based on a nonlinear micromechanics theory. The optimal fabrication problem is formulated and solved with non-linear programming. Application cases regarding the optimization of the fabrication cool-down phases of unidirectional ultra-high modulus graphite/copper and silicon carbide/titanium composites are presented.

Saravanos, D. A.↗

NASA NTRS1990

Optimal fabrication processes for unidirectional metal-matrix composites - A computational simulation

A method is proposed for optimizing the fabrication process of unidirectional metal matrix composites. The temperature and pressure histories are optimized such that the residual microstresses of the composite at the end of the fabrication process are minimized and the material integrity throughout the process is ensured. The response of the composite during the fabrication is simulated based on a nonlinear micromechanics theory. The optimal fabrication problem is formulated and solved with nonlinear programming. Application cases regarding the optimization of the fabrication cool-down phases of unidirectional ultra-high modulus graphite/copper and silicon carbide/titanium composites are presented.

Saravanos, D. A.↗

NASA NTRS1993

Observability during planetary approach navigation

The objective of the research is to develop an analytic technique to predict the relative navigation capability of different Earth-based radio navigation measurements. In particular, the problem is to determine the relative ability of geocentric range and Doppler measurements to detect the effects of the target planet gravitational attraction on the spacecraft during the planetary approach and near-encounter mission phases. A complete solution to the two-dimensional problem has been developed. Relatively simple analytic formulas are obtained for range and Doppler measurements which describe the observability content of the measurement data along the approach trajectories. An observability measure is defined which is based on the observability matrix for nonlinear systems. The results show good agreement between the analytic observability analysis and the computational batch processing method.

Bishop, Robert H.↗

NASA NTRS1998

Synthesis of Polymers Containing Covalently Bonded NLO Chromophores

Polymers containing covalently bonded nonlinear optical (NLO) chromophores are expected to possess special properties such as greater stability, better mechanical processing, and easier film formation than their non-polymeric equivalent. For the present work, polymethylmethacrylate (PMMA) was selected as the basic polymer unit on which to incorporate different NLO chromophores. The NLO components were variations of DIVA {[2-methoxyphenyl methylidene]-propanedinitrile} which we prepared from vanillin derivatives and malononitrile. These were esterified with methacrylic acid and polymerized either directly or with methyl methacrylate to form homopolymers or copolymers respectively. Characterization of the polymers and NLO property studies are underway.

Denga, Xiao-Hua↗