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At least 451 records · Page 25

All sources NASA NTRS DOE OSTI

NASA NTRS2016

Optimization of Turbine Engine Cycle Analysis with Analytic Derivatives

A new engine cycle analysis tool, called Pycycle, was built using the OpenMDAO framework. Pycycle provides analytic derivatives allowing for an efficient use of gradient-based optimization methods on engine cycle models, without requiring the use of finite difference derivative approximation methods. To demonstrate this, a gradient-based design optimization was performed on a turbofan engine model. Results demonstrate very favorable performance compared to an optimization of an identical model using finite-difference approximated derivatives.

OpenMDAO↗

NASA NTRS2019

Using Graph Coloring to Compute Total Derivatives More Efficiently in OpenMDAO

When they are applicable, gradient based optimization algorithms are the most efficient way to solve design optimization problems. Although gradient based methods are generally efficient, they can be made significantly more so through the usage of analytic techniques to compute the necessary total derivatives. The traditional forward (direct) and reverse (adjoint) analytic techniques have computational costs that scale linearly with the number of design variables and the number of constraints, respectively. In this work, we present an application of a graph coloring algorithm to the analytic techniques for computing total derivative Jacobians in order to achieve much better computational scaling than the pure analytic methods can provide alone. A detailed theoretical explanation of how coloring algorithms interact with analytic derivative methods is presented that illustrates specific types of sparsity patterns that must be present in total derivative Jacobians in order for this coloring technique to be effective. The new technique has been implemented as a feature in the OpenMDAO framework and the implementation is demonstrated on two example problems. The performance on the example problems up to 50% reduction in compute cost for optimizations with bi-directional coloring compared to traditional constraint aggregation. Additionally, the results show how coloring technique alleviates some of the numerical difficulties that constraint aggregation can cause, leading to the ability to solve larger problems. It is expected that the new method will have wide applicability to multidisciplinary optimization problems, and that its availability in OpenMDAO will offer significant computational savings for users without the need for them to implement the coloring algorithm themselves.

Trajectory↗

NASA NTRS2019

Design and Modeling of the Off-Axis Parabolic Deformable (OPD) Mirror Laboratory

Coronagraph-equipped direct imaging missions need an active wavefront control system to cancel out the optical aberrations that degrade the performance of the coronagraphs. A fast steering mirror is used to control Line-of-Sight (LoS) pointing error caused by the telescope jitter. In addition to controlling other low-order aberrations such as astigmatism and coma, high stroke, high actuator density deformable mirrors (DMs) are also used to control the electric field at the required high spatial frequencies. We are designing a testbed to verify a different deformable architecture, where the powered optic in the optical train are controllable and have lower actuator count compared to the existing DMs with flat nominal surfaces. This simplifies the packaging issue for space missions and reduces both cost and risk of having the entire coronagraph instrument's performance depending on one or two high-actuator count DMs. The testbed would also be capable of testing different low-order wavefront sensing algorithms, which focuses in the near-term on a new adaptive Kalman filtering and gradient decent method to estimate the harmonic LoS errors that affect space telescopes. In long run, we would test different machine learning techniques to estimate low-order aberrations and non-linear algorithms for digging the region of high contrast called the dark holes (DH).

Subedi, Hari↗

NASA NTRS2019

Design and Modeling of the Off-Axis Parabolic Deformable Mirror Laboratory

Communication↗

NASA NTRSDate not supplied

TPSAS-NF1676L-21838-DND

Correlation relationships between particle microphysical parameters and optical data are investigated. The results show that surface-area concentrations and extinction coefficients are linearly correlated with a correlation coefficient above 0.99 for arbitrary particle size distribution. The correlation relationships that we obtained can be used as constraints in our inversion of optical lidar data. Simulation studies demonstrate a significant stabilization of aerosol microphysical data products if we apply the gradient correlation method (GCM) in our traditional regularization technique.

Alexei Kolgotin↗

NASA NTRSDate not supplied

Detached Melt and Vapor Growth of InI in SUBSA hardware

Indium iodide (InI) single crystals, grown by the Bridgman process, have shown a significant promise as room temperature detector material for X-rays and γ-rays due to the large, close to ideal, bandgap of Eg = 2 eV, and high electrical resistivity. Yet the transport properties of the charge and the detector performance remain inadequate. The Bridgman growth of semiconductor crystals in microgravity have typically resulted in partial dewetting and Marangoni convection, which disturbed the diffusion-controlled growth and produced nonuniform dopant distribution. However, unconfined or partially confined solidification drastically reduced the number of stress-induced defects compared to the confined growth in crucibles on Earth.The goal of the present investigation was to explore melt growth of a heavy metal halide in microgravity, focusing on dewetting, in order to improve the crystalline perfection, i.e., to reduce the number of stress-related defects. InI was chosen because it is non-toxic and has a low melting point of only 365 C (~150 C below the melting point of InSb, used in the SUBSA experiments in 2002). Furthermore, InI melts and evaporates congruently, making it suitable for both growth by directional solidification and growth from the vapor phase. In preparation for the flight experiments, ground-based experiments were conducted in the SUBSA ground unit, consisting of 4 melt growth and 2 vapor growth experiments. The crystals were grown by a gradient freeze method by lowering the setpoint of the furnace. The transparent section of the SUBSA furnace did not allow observations of the solid-liquid interface. Contrary to our expectations, the possible occurrence of dewetting during growth could not be observed. We will present i) the ampoule design and preparation, ii) the experiments conducted in the SUBSA ground unit, iii) the images obtained during the microgravity experiments and iv) the results of the characterization of the grown crystals and detector fabrication.

Crystal Growth↗

DOE OSTI2020

Multi-Level Optimal Power Flow Solver in Large Distribution Networks: Preprint

Solving optimal power flow (OPF) problem for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution networks of tree topology with deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multi-level implementation of the primal-dual gradient algorithm for solving the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verifies that the proposed algorithm can significantly improve computational speed without compromising any optimality.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2020

Apparatus, methods and system for temperature gradient aging with in-situ electrical monitoring

An apparatus for making in-situ electrical measurements of a sample while the sample is subjected to a temperature gradient between a first side of the sample and a second side of the sample. The apparatus comprises a first heater and a second heater that are positioned in contact with the first side of the sample and the second side of the sample, respectively. The first heater and the second heater comprise respective faces that each have electrical contacts embedded therein. Electrical measurements pertaining to the sample can be made by way of the electrical contacts embedded in the faces. A temperature at either side of the sample can be monitored by way of thermocouples positioned inside the heaters in proximity to the faces of the heaters.

Reyes, Karla Rosa↗

DOE OSTI2024

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation: Preprint

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

distribution system operator↗

NASA NTRS1991

Comparing direct and iterative equation solvers in a large structural analysis software system

Two direct Choleski equation solvers and two iterative preconditioned conjugate gradient (PCG) equation solvers used in a large structural analysis software system are described. The two direct solvers are implementations of the Choleski method for variable-band matrix storage and sparse matrix storage. The two iterative PCG solvers include the Jacobi conjugate gradient method and an incomplete Choleski conjugate gradient method. The performance of the direct and iterative solvers is compared by solving several representative structural analysis problems. Some key factors affecting the performance of the iterative solvers relative to the direct solvers are identified.

Poole, E. L.↗

DOE OSTI2023

Optically pumped gradient magnetometer

A method is provided for sensing a magnetic field in a magnetic gradiometer of the kind in which pump light and light constituting an optical carrier traverse first and second atomic vapor cells that contain host atoms and that are separated from each other by a known distance. According to such method, the host atoms are prepared in a coherent superposition of two quantum states that differ in energy by an amount that is sensitive to an ambient magnetic field. Modulation of the optical carrier in the respective cells gives rise to sidebands that interfere to generate a beat frequency indicative of the magnetic field gradient. The host atoms are prepared at least in a mode that allows measurement of ambient magnetic field components perpendicular to the axis of the pump light. In such mode, the host atoms are spin-polarized by pump light while subjected to a controlled magnetic field directed parallel to the pump beam, and then the controlled magnetic field is adiabatically extinguished.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Deformation accommodating periodic computational domain for a uniform velocity gradient

Many multiscale methods for granular materials use periodic computational domains to consider particle scale interactions and then calculate the stress to drive the continuum scale calculations. For problems involving large material deformations, the computation domain often needs to be reinitialized because of the distortion, causing the loss of the history information of the system. This work introduces an algorithm to accommodate a large deformation of the material while maintaining the computational domain cuboid to avoid the domain reinitialization during the computation. The algorithm uses a rotating frame of reference, in which the velocity gradient can be represented by an upper triangular matrix. The deformation caused by the upper triangular matrix is treated by the image system implied in the periodicity to maintain the computational domain cuboid. The effect from the rotation of the reference frame is considered using the inertial forces. Finally, simulations of simple and pure shear motions are carried out to illustrate the algorithm.

42 ENGINEERING↗

DOE OSTI2026

Comparative analysis of plasticity-based GND density estimation methods in crystal plasticity finite element models

In crystal plasticity finite element (CPFE) simulations, accurately quantifying geometrically necessary dislocations (GNDs) is critical for capturing strain gradients in polycrystals. We compare different methods for quantifying GNDs, all of which originate from the Nye tensor, which is computed as the curl of the plastic deformation gradient. The projection technique directly decomposes the Nye tensor onto individual screw and edge dislocation components to compute GNDs. This approach requires converting a nine-component Nye tensor into densities for a larger number of dislocation systems, a fundamentally underdetermined (non-unique) process, which is resolved using L2 minimization. In contrast, when employing CPFE analysis, one could directly compute dislocation densities on each slip system using shear gradients. Projection and slip gradient methods are compared with respect to their prediction of GNDs with changing grain size, strain, and grain neighborhoods, including multigrain junctions. Although these techniques match analytical GND densities for single slip, single crystal deformation, and are consistent with anticipated overall GND trends, we find that the GND densities from projection techniques are significantly lower than those predicted from CPFE-based slip gradients in polycrystals. A suggested improvement of only using the active dislocation systems in the projection technique almost entirely resolved this mismatch.

Crystal plasticity↗

DOE OSTI2021

A diagrammatic approach for automatically deriving analytical gradients of tensor hyper-contracted electronic structure methods

In this work, we introduce a diagrammatic approach to facilitate the automatic derivation of analytical nuclear gradients for tensor hyper-contraction (THC) based electronic structure methods. The automatically derived gradients are guaranteed to have the same scaling in terms of both operation count and memory footprint as the underlying energy calculations, and the computation of a gradient is roughly three times as costly as the underlying energy. The new diagrammatic approach enables the first cubic scaling implementation of nuclear derivatives for THC tensors fitted in molecular orbital basis (MO-THC). Furthermore, application of this new approach to THC-MP2 analytical gradients leads to an implementation, which is at least four times faster than the previously reported, manually derived implementation. Finally, we apply the new approach to the 14 tensor contraction patterns appearing in the supporting subspace formulation of multireference perturbation theory, laying the foundation for developments of analytical nuclear gradients and nonadiabatic coupling vectors for multi-state CASPT2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1983

A comparison of methods for computing the sigma-coordinate pressure gradient force for flow over sloped terrain in a hybrid theta-sigma model

In connection with the employment of the sigma coordinates introduced by Phillips (1957), problems can arise regarding an accurate finite-difference computation of the pressure gradient force. Over steeply sloped terrain, the calculation of the sigma-coordinate pressure gradient force involves computing the difference between two large terms of opposite sign which results in large truncation error. To reduce the truncation error, several finite-difference methods have been designed and implemented. The present investigation has the objective to provide another method of computing the sigma-coordinate pressure gradient force. Phillips' method is applied for the elimination of a hydrostatic component to a flux formulation. The new technique is compared with four other methods for computing the pressure gradient force. The work is motivated by the desire to use an isentropic and sigma-coordinate hybrid model for experiments designed to study flow near mountainous terrain.

Johnson, D. R.↗

DOE OSTI2022

Volt-VAR Optimization in Distribution Networks Using Twin Delayed Deep Reinforcement Learning

Modern distribution grids are undergoing new challenges due to the stochastic nature of distributed energy resources (DERs). High penetration of DERs has a significant impact on Volt-VAR profile and system power losses. This work proposes a deep reinforcement learning (DRL)-based Volt-VAR optimization approach for improving voltage profile and reducing system power loss under high penetration of distributed energy resources, such as battery energy storage and solar photovoltaic units in distribution grids. The twin delayed deep deterministic policy gradient (TD3) method-based DRL agent is proposed to configure optimal set-points of reactive power outputs of fast responding smart inverters. The agent schedules the reactive power of inverters according to their physical capabilities, such as minimum allowed power factor, e.g., 0.9 leading/lagging. The reward function of the proposed DRL scheme is designed carefully to ensure a proper voltage profile of the grids with effective scheduling of reactive power outputs from inverters. The performance of the proposed model is verified on modified IEEE 34- and 123-bus systems and compared with base case with no reactive supply by inverters, and local droop Volt-VAR control approach. The results show that the proposed method performs better than the local droop control and deep deterministic policy gradient (DDPG)-based DRL method for reducing voltage fluctuation and minimizing power loss.

Hossain, Rakib↗

DOE OSTI2025

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks

Uncertainty quantification (UQ) plays a pivotal role in scientific machine learning, especially when surrogate models are used to approximate complex systems. Although multilayer perceptions (MLPs) are commonly employed as surrogates, they often suffer from overfitting due to their large number of parameters. Kolmogorov-Arnold networks (KANs) offer an alternative solution with fewer parameters. However, gradient-based inference methods, such as Hamiltonian Monte Carlo (HMC), may result in computational inefficiency when applied to KANs, especially for large-scale datasets, due to the high cost of back-propagation. To address these challenges, we propose a novel approach, combining the dropout Tikhonov ensemble Kalman inversion (DTEKI) with Chebyshev KANs. This gradient-free method effectively mitigates overfitting and enhances numerical stability. In addition, we incorporate the active subspace method to reduce the parameter-space dimensionality, allowing us to improve the accuracy of predictions and obtain more reliable uncertainty estimates. Extensive experiments demonstrate the efficacy of our approach in various test cases, including scenarios with large datasets and high noise levels. Our results show that the new method achieves comparable or better accuracy, much higher efficiency as well as stability compared to HMC, in addition to scalability. Moreover, by leveraging the low-dimensional parameter subspace, our method preserves prediction accuracy while substantially reducing further the computational cost.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1979

Utilization of nuclear methods for materials analysis and the determination of concentration gradients

The various types of nuclear chemical analysis methods are discussed. The possibilities of analysis through activation and direct observation of nuclear reactions are described. Such methods make it possible to analyze trace elements and impurities with selectivity, accuracy, and a high degree of sensitivity. Such methods are used in measuring major elements present in materials which are available for analysis only in small quantities. These methods are well suited to superficial analyses and to determination of concentration gradients; provided the nature and energy of the incident particles are chosen judiciously. Typical examples of steels, pure iron and refractory metals are illustrated.

Darras, R.↗