Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “numerical differentiation”

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.

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

At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A fully Sinc-Galerkin method for Euler-Bernoulli beam models

A fully Sinc-Galerkin method in both space and time is presented for fourth-order time-dependent partial differential equations with fixed and cantilever boundary conditions. The Sinc discretizations for the second-order temporal problem and the fourth-order spatial problems are presented. Alternate formulations for variable parameter fourth-order problems are given which prove to be especially useful when applying the forward techniques to parameter recovery problems. The discrete system which corresponds to the time-dependent partial differential equations of interest are then formulated. Computational issues are discussed and a robust and efficient algorithm for solving the resulting matrix system is outlined. Numerical results which highlight the method are given for problems with both analytic and singular solutions as well as fixed and cantilever boundary conditions.

Smith, R. C.↗
NASA NTRS1995

Sensitivity analysis of the non-linear dynamic viscoplastic response of 2-d structures with respect to material parameters

A computational procedure is presented for evaluating the sensitivity coefficients of the viscoplastic response of structures subjected to dynamic loading. A state of plane stress is assumed to exist in the structure, a velocity strain-Cauchy stress formulation is used, and the geometric non-linearities arising from large strains are incorporated. The Jaumann rate is used as a frame indifferent stress rate. The material model is chosen to be isothermal viscoplasticity, and an associated flow rule is used with a von Mises effective stress. The equations of motion emanating from a finite element semi-discretization are integrated using an explicit central difference scheme with an implicit stress update. The sensitivity coefficients are evaluated using a direct differentiation approach. Since the domain of integration is the current configuration, the sensitivity coefficients of the spatial derivatives of the shape functions must be included. Numerical results are presented for a thin plate with a central cutout subjected to an in-plane compressive loading. The sensitivity coefficients are generated by evaluating the derivatives of the response quantities with respect to Young's modulus, and two of the material parameters characterizing the viscoplastic response. Time histories of the response and sensitivity coefficients, and spatial distributions at selected times are presented.

Kulkarni, Makarand↗
NASA NTRS1999

Measurement of Temperature Fluctuations and Microscopic Growth Rates in a Silicon Floating Zone Under Microgravity

USA Several microgravity experiments on sounding rockets and the Space Shuttle have shown that time-dependent thermocapillary (Marangoni) convection is the major cause for the formation of dopant striations in floating-zone grown semiconductor crystals, at least in small-scale systems not employing RF heating. To quantify this correlation, a silicon floating-zone experiment was performed during the TEXUS36 flight (February 7, 1998) in the monoellipsoid mirror furnace TEM02-ELLI. During the experiment, temperature fluctuations in the silicon melt zone and the microscopic growth rate were simultaneously measured. Temperature fluctuations of 0.5 C - 0.7 C with main frequencies between 0.1 Hz and 0.3 Hz were detectable. The microscopic growth rate fluctuated considerably around the average growth rate of 1 mm/min: rates from 4 mm/min to negative values (backmelting) were observed. Dopant striations are clearly visible in the Sb-doped crystal. They were characterized by Spreading Resistance measurements and Differential Interference Contrast microscopy. The frequencies associated with the dopant inhomogeneities correspond quite well with those of the temperature fluctuations and microscopic growth rates. 3D numerical simulations were performed to predict the optimum position of the temperature sensor, to evaluate characteristic temperature amplitudes and frequencies, and to give insight into the instability mechanisms of Marangoni convection in this configuration. The simulations were in good agreement with the experimental values, showing temperature fluctuations with frequencies ? 0.25 Hz and amplitudes up to 1.8 C at a position equivalent to that of the sensor tip in the experiment.

Croell, Arne↗
NASA NTRS1999

Measurement of Temperature Fluctuations and Microscopic Growth Rates in a Silicon Floating Zone on TEXUS36

Several pg experiments on sounding rockets and the Space Shuttle have shown that time-dependent thermocapillary (Marangoni) convection is the major cause for the formation of dopant striations in floating-zone grown semiconductor crystals, at least in small-scale systems not employing RF heating. To quantify this correlation, a silicon floating-zone experiment was performed during the TEXUS36 flight (February 7, 1 998) in the monoellipsoid mirror furnace TEM02-ELLI. During the experiment, temperature fluctuations in the silicon melt zone and the microscopic growth rate were simultaneously measured. Temperature fluctuations of 0.5 C - 0.7 C with main frequencies between 0.1 Hz and 0.3Hz were detectable. The microscopic growth rate fluctuated considerably around the average growth rate of 1 mm/min: rates from 4mm/min to negative values (backmelting) were observed. Dopant striations are clearly visible in the Sb-doped crystal. They were characterized by Spreading Resistance measurements and Differential Interference Contrast microscopy. The frequencies associated with the dopant inhomogeneities correspond quite well with those of the temperature fluctuations and microscopic growth rates. 3D numerical simulations were performed to predict the optimum position of the temperature sensor, to evaluate characteristic temperature amplitudes and frequencies, and to give insight into the instability mechanisms of Marangoni convection in this configuration. The simulations were in good agreement with the experimental values, showing temperature fluctuations with frequencies? 0.25 Hz and amplitudes up to 1.8 C at a position equivalent to that of the sensor tip in the experiment.

Croell, Arne↗
NASA NTRS2002

Cross-Section Parameterizations for Pion and Nucleon Production From Negative Pion-Proton Collisions

Ranft has provided parameterizations of Lorentz invariant differential cross sections for pion and nucleon production in pion-proton collisions that are compared to some recent data. The Ranft parameterizations are then numerically integrated to form spectral and total cross sections. These numerical integrations are further parameterized to provide formula for spectral and total cross sections suitable for use in radiation transport codes. The reactions analyzed are for charged pions in the initial state and both charged and neutral pions in the final state.

Norbury, John W.↗
NASA NTRS1974

A numerical calculation of outward propagation of solar disturbances

The responses of the solar atmosphere due to an outward propagation shock are examined by employing the Lax-Wendroff method to solve the set of nonlinear partial differential equations in the model of the solar atmosphere. It is found that this theoretical model can be used to explain the solar phenomena of surge and spray. A criterion to discriminate the surge and spray is established and detailed information concerning the density, velocity, and temperature distribution with respect to the height and time is presented. The complete computer program is also included.

Wu, S. T.↗
DOE OSTI2025

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations

The Fokker-Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. In addition, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton-Jacobi-Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing score matching or Score-PINN to acquire the score function; and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying trade-offs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein-Uhlenbeck processes, geometric Brownian motion, and Brownian motion with varying eigenspace. We also test various distributions, including Gaussian, Log-normal, Laplace, and Cauchy distributions. The numerical results demonstrate the score-based SDE solver’s stability, speed, and performance across different experimental settings, solidifying its potential as a solution to CoD for high-dimensional FP equations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

A new unified approach for analyzing wing-body-tail configurations with control surfaces

A general theory for steady and unsteady, subsonic and supersonic potential aerodynamics for complex configurations is presented. Special attention is given to the theoretical formulation and the corresponding numerical implementation for coplanar interfering surfaces. Applying the Green's function method to the equation of the velocity potential and discretizing the spatial problem by using the finite-element technique, yields a set of differential-delay equations in time relating the potential to the normal wash. For fully unsteady flow, the motion is assumed to consist of constant subsonic or supersonic speed for time t less than or equal to 0 (steady state) and of small perturbations around the steady state for time t greater than 0; the solution is obtained in the Laplace domain. From the potential, the aerodynamic pressure and the generalized forces are evaluated. The program SOUSSA (Steady, Oscillatory and Unsteady Subsonic and Supersonic Aerodynamics) is briefly described. Numerical results obtained with SOUSSA are presented.

Tseng, K.↗
DOE OSTI2021

Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it possible to synthesize solutions to a wide range of nonlinear stochastic boundary value problems described by partial differential equations (PDEs) for which a stochastic computational model (SCM) is available and which depend on a vector-valued random control parameter. The cost of a single numerical evaluation of this SCM is assumed to be such that only a limited number of points can be computed for constructing the training dataset (small data). Each point of the training dataset is made up of realizations from a vector-valued stochastic process (the stochastic solution) and the associated random control parameter on which it depends. The presented PLoM constrained by PDE allows for generating a large number of learned realizations of the stochastic process and its corresponding random control parameter. These learned realizations are generated so as to minimize the vector-valued random residual of the PDE in the mean-square sense. Appropriate novel methods are developed to solve this challenging problem. Three applications are presented. The first one is a simple uncertain nonlinear dynamical system with a nonstationary stochastic excitation. The second one concerns the 2D nonlinear unsteady Navier–Stokes equations for incompressible flows in which the Reynolds number is the random control parameter. Here, the last one deals with the nonlinear dynamics of a 3D elastic structure with uncertainties. The results obtained make it possible to validate the PLoM constrained by stochastic PDE but also provide further validation of the PLoM without constraint.

Machine learning↗
NASA NTRS1977

Numerical turbulence modeling

The underlying bases and developments in two techniques of detailed turbulence modeling are described where the flow is treated in the Eulerian sense, and one technique where the Lagrangian motions of vortices are followed. First, a technique is described for solving the single-point statistically averaged conservation equations. The Reynolds stresses that appear in these equations are evaluated by solving supplemental differential equations which contain terms that are modeled. A sequence of increasingly complex, but also increasingly general, modeling equations is described and computations based on these equations are compared with experimental data. The hierarchy of models described terminates with equations for the individual components of the Reynolds stress tensor. The second Eulerian technique approach to turbulence modeling is the direct numerical simulation of turbulent fields. In this approach, all three dimensional eddies between a predetermined range of sizes are computed in time within a specified volume of flow. Present day computers require a tradeoff between the size of the volume that can be considered and the degree of resolution of the turbulent eddies. Techniques of modeling the smallest eddies are described that permit enlarging the volume, or Reynolds number, that can be considered.

Rubesin, M. W.↗
NASA NTRS1988

Numerical simulation of axisymmetric turbulent flow in combustors and diffusors

A method for predicting turbulent flow in combustors and diffusers is developed. The Navier-Stokes equations, incorporating a turbulence kappa-epsilon model equation, were solved in a nonorthogonal curvilinear coordinate system. The solution applied the finite volume method to discretize the differential equations and utilized the SIMPLE algorithm iteratively to solve the differenced equations. A zonal grid method, wherein the flow field was divided into several subsections, was developed. This approach permitted different computational schemes to be used in the various zones. In addition, grid generation was made a more simple task. However, treatment of the zonal boundaries required special handling. Boundary overlap and interpolating techniques were used and an adjustment of the flow variables was required to assure conservation of mass, momentum and energy fluxes. The numerical accuracy was assessed using different finite differencing methods, i.e., hybrid, quadratic upwind and skew upwind, to represent the convection terms. Flows in different geometries of combustors and diffusers were simulated and results compared with experimental data and good agreement was obtained.

Yung, Chain Nan↗
DOE OSTI2021

Vacuum magnetic fields with exact quasisymmetry near a flux surface. Part 1. Solutions near an axisymmetric surface

While several results have pointed to the existence of exactly quasisymmetric fields on a surface (Garren & Boozer, Phys. Fluids B, vol. 3, 1991, pp. 2805–2821; 2822–2834; Plunk & Helander, J. Plasma Phys. , vol. 84, 2018, 905840205), we have obtained the first such solutions using a vacuum surface expansion formalism. We obtain a single nonlinear parabolic partial differential equation for a function $\eta$ such the field strength satisfies $B = B(\eta )$ . Closed-form solutions are obtained in cylindrical, slab and isodynamic geometries. Numerical solutions of the full nonlinear equations in general axisymmetric toroidal geometry are obtained, resulting in a class of quasihelical local vacuum equilibria near an axisymmetric surface. The analytic models provide additional insight into general features of the nonlinear solutions, such as localization of the surface perturbations on the inboard side. The local solutions thus obtained can be continued globally only for special initial surfaces.

Physics↗
DOE OSTI2026

Taylor approximation variance reduction for approximation errors in PDE-constrained Bayesian inverse problems

In numerous applications, surrogate models are used as a replacement for accurate parameter-to-observable mappings when solving large-scale inverse problems governed by partial differential equations (PDEs). The surrogate model may be a computationally cheaper alternative to the accurate parameter-to-observable mappings and/or may ignore additional unknowns or sources of uncertainty. The Bayesian approximation error (BAE) approach provides a means to account for the induced uncertainties and approximation errors, i.e. the errors between the accurate parameter-to-observable mapping and the surrogate. The statistics of these errors are, however, in general unknown a priori, and are thus calculated using Monte Carlo sampling. Although the sampling is typically carried out offline, i.e. before considering the data, the process can still represent a computational bottleneck. In this work, we develop a scalable computational approach for reducing the costs associated with the sampling stage of the BAE approach. Specifically, we consider the Taylor expansion of the accurate and surrogate forward models with respect to the uncertain parameter fields either as a control variate for variance reduction or as a means to directly and efficiently approximate the mean and covariance of the approximation errors. We propose efficient methods for evaluating the expressions for the mean and covariance of the Taylor approximations based on linear(-ized) PDE solves. Furthermore, the proposed approach is independent of the dimension of the uncertain parameter, depending instead on the intrinsic dimension of the data, ensuring scalability to high-dimensional problems. The potential benefits of the proposed approach are demonstrated for two high-dimensional inverse problems governed by PDE examples, namely for the estimation of a distributed Robin boundary coefficient in a linear diffusion problem, and for a coefficient estimation problem governed by a nonlinear diffusion problem.

Bayesian approximation error↗
DOE OSTI2024

Binary Quantum Control Optimization with Uncertain Hamiltonians

Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneous quantum ensembles raise the need for high-quality quantum controls under uncertainties. In this paper, we consider a stochastic discrete optimization formulation of a discretized binary optimal quantum control problem involving Hamiltonians with predictable uncertainties. We propose a sample-based reformulation that optimizes both risk-neutral and risk-averse measurements of control policies, and solve these with two gradient-based algorithms using sum-up-rounding approaches. Furthermore, we discuss the differentiability of the objective function and prove upper bounds of the gaps between the optimal solutions to binary control problems and their continuous relaxations. We conduct numerical simulations on various sized problem instances based on two applications of quantum pulse optimization; we evaluate different strategies to mitigate the impact of uncertainties in quantum systems. In conclusion, we demonstrate that the controls of our stochastic optimization model achieve significantly higher quality and robustness compared with the controls of a deterministic model.

conditional value-at-risk (CVaR)↗
NASA NTRS1986

New integration techniques for chemical kinetic rate equations. I - Efficiency comparison

A comparison of the efficiency of several recently developed numerical techniques for solving chemical kinetic rate equations is presented. The solution procedures examined include two general-purpose codes, EPISODE and LSODE, developed as multipurpose differential equation solvers, and three specialzed codes, CHEMEQ, CREK1D, and GCKP84, developed specifically for chemical kinetics. The efficiency comparison is made by applying these codes to two practical combustion kinetics problems. Both problems describe adiabatic, constant-pressure, gas-phase chemical reactions and include all three combustion regimes: induction, heat release, and equilibration. The comparison shows that LSODE is the fastest routine currently available for solving chemical kinetic rate equations. An important finding is that an iterative solution of the algebraic enthalpy conservation equation for temperature can be significantly faster than evaluation of the temperature by integration of its time derivative. Significant increases in computational speed are realized by updating the reaction rate constants only when the temperature change exceeds an amount Delta-T that is problem dependent. An approximate expression for the automatic evaluation of Delta-T is presented and is shown to result in increased computational speed.

Radhakrishnan, K.↗
NASA NTRS1988

Extensions of the CSCM methodology for nonequilibrium reacting gas flows

The paper describes the extension of the CSCM flux difference splitting to produce conservative unconditionally stable implicit upwind methods for the extended equation set. The CSCM-S single data level relaxation algorithm is argued to be particularly advantageous for efficiently solving the set of equations. Attention is given to issues of alternative state variable representations and to complementary thermodynamic relations that parameterize and need to be effectively coupled with the solution procedure for the partial differential equations. Within the framework of iteration, consideration is given to more efficient approximate solution procedures than the full block coupled LU decomposition. Results of early numerical experiments are given to illustrate the developments.

Lombard, C. K.↗
NASA NTRS1992

A multi-layer discrete-ordinate method for vector radiative transfer in a vertically-inhomogeneous, emitting and scattering atmosphere. I - Theory. II - Application

A theory is developed for discretizing the vector integro-differential radiative transfer equation including both solar and thermal radiation. A complete solution and boundary equations are obtained using the discrete-ordinate method. An efficient numerical procedure is presented for calculating the phase matrix and achieving computational stability. With natural light used as a beam source, the Stokes parameters from the model proposed here are compared with the analytical solutions of Chandrasekhar (1960) for a Rayleigh scattering atmosphere. The model is then applied to microwave frequencies with a thermal source, and the brightness temperatures are compared with those from Stamnes'(1988) radiative transfer model.

Weng, Fuzhong↗
NASA NTRS1992

Interaction of photons in a canopy of finite-dimensional leaves

Neutral particle interaction for photons traveling in media consisting of finite-dimensional scattering centers is investigated. A leaf canopy, a typical example of such media, is idealized as a binary medium consisting of gaps (voids) and regions with phytoelements (turbid phytomedium). The leaf canopy is represented by a combination of all possible open oriented spheres. The extinction coefficient at any phase-space location in a leaf canopy is the product of the extinction coefficient in the turbid phytomedium and the probability of absence gaps at that location. An expression for the differential scattering coefficient is derived using the same approach. The effect of canopy parameters and direction of photon travel on the extinction coefficient is illustrated by numerical examples.

Knyazikhin, Yuri V.↗