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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Estimating the Adequacy of a Multi-Objective Optimization

Multi-objective optimization methods can be criticized for lacking a statistically valid measure of the quality and representativeness of a solution. This stance is especially relevant to metaheuristic optimization approaches but can also apply to other methods that typically might only report a small representative subset of a Pareto frontier. Here we present a method to address this deficiency based on random sampling of a solution space to determine, with a specified level of confidence, the fraction of the solution space that is surpassed by an optimization. The Superiority of Multi-Objective Optimization to Random Sampling, or SMORS method, can evaluate quality and representativeness using dominance or other measures, e.g., a spacing measure for high-dimensional spaces. SMORS has been tested in a combinatorial optimization context using a genetic algorithm but could be useful for other optimization methods.

42 ENGINEERING↗
DOE OSTI2016

Optimal Energy Management for Microgrid with Stationary and Mobile Storage

This paper studies energy management in a Microgrid (MG) with solar generation, Battery Energy Management System (BESS) and gridable (V2G) EVs. A two-stage stochastic optimization method is proposed to capture the intermittent solar generation and random EV user behaviors. It is subsequently formulated as a Mixed Integer Linear Programming (MILP) problem. To evalutate the proposed method, real solar generation, loads, BESS and EV data is used in Sample Average Approximation (SAA). Computational results show the correctness of the proposed method as well as steady and tightly bounded optimality gap. Comparisons demonstrate that the proposed stochastic method outperforms its deterministic counterpart at the expense of higher computational cost. It is also observed that moderate number of EVs helps to reduce the overall operational cost of the MG, which sheds light on future EV integration to the smart grid.

Battery Energy Management System (BESS)↗
DOE OSTI2014

Indirect-to-direct band gap transition in relaxed and strained Ge{sub 1−x−y}Si{sub x}Sn{sub y} ternary alloys

Sn-containing group IV semiconductors create the possibility to independently control strain and band gap thus providing a wealth of opportunities to develop an entirely new class of low dimensional systems, heterostructures, and silicon-compatible electronic and optoelectronic devices. With this perspective, this work presents a detailed investigation of the band structure of strained and relaxed Ge{sub 1−x−y}Si{sub x}Sn{sub y} ternary alloys using a semi-empirical second nearest neighbors tight binding method. This method is based on an accurate evaluation of the deformation potential constants of Ge, Si, and α-Sn using a stochastic Monte-Carlo approach as well as a gradient based optimization method. Moreover, a new and efficient differential evolution approach is also developed to accurately reproduce the experimental effective masses and band gaps. Based on this, we elucidated the influence of lattice disorder, strain, and composition on Ge{sub 1−x−y}Si{sub x}Sn{sub y} band gap energy and directness. For 0 ≤ x ≤ 0.4 and 0 ≤ y ≤ 0.2, we found that tensile strain lowers the critical content of Sn needed to achieve a direct band gap semiconductor with the corresponding band gap energies below 0.76 eV. This upper limit decreases to 0.43 eV for direct gap, fully relaxed ternary alloys. The obtained transition to direct band gap is given by y > 0.605 × x + 0.077 and y > 1.364 × x + 0.107 for epitaxially strained and fully relaxed alloys, respectively. The effects of strain, at a fixed composition, on band gap directness were also investigated and discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Multi-objective design of microvascular panels for battery cooling applications

Building on a recently developed optimization method based on an interface-enriched generalized finite element method, multiple objective functions are considered for the optimization of 2D networks of microchannels embedded in battery-cooling panels. The objective functions considered in this study are a differentiable alternative to the maximum temperature (the p-mean of the temperature), the pressure drop and the variance of the temperature. The -constraint method and the normalized normal constraint method are used to generate the pressure-temperature Pareto optimal front of the multi-objective optimization problem. The effects of different operating constraints/conditions such as localization of heat sources, prescribed pump power and imposed flow rate on the optimal designs are investigated. In addition to the topology of the embedded network, the cross sections of the microchannels are also introduced as design parameters to further improve the pressure drop of the designs. The resulting variable-cross-section optimized design is validated with experiment.

energy storage (including batteries and capacitors↗
DOE OSTI2014

SU-E-I-64: X-Ray Coherent Scatter Mammography Simulation

Purpose: Conventional mammography has poor contrast between healthy and cancerous tissues due to the small difference in attenuation properties. Coherent scatter potentially provides more information because interference of coherently scattered radiation depends on the average intermolecular spacing, and can be used to characterize tissue types. However, typical coherent scatter analysis techniques are not compatible with rapid low dose screening modalities. Coherent scatter slot scan imaging is a novel imaging technique which provides new information with higher contrast. In this work a simulation was performed of coherent scatter slot scan imaging to assess its performance and provide system optimization. Methods: In coherent scatter imaging, the coherent scatter is exploited using a conventional slot scan mammography system with anti-scatter grids tilted at the characteristic angle of cancerous tissues. A Monte Carlo simulation was used to simulate the coherent scatter imaging. System optimization was performed across several parameters, including source voltage and filtration, tilt angle, source and grid distances, grid ratio, and shielding geometry. Results: The contrast was high and increased as the grid tilt angle was increased beyond the characteristic angle for the modeled carcinoma, and as the source voltage increased. Source filtration did not have a significant effect on contrast. Increasing grid ratio improved contrast at the expense of decreasing the signal to noise ratio (SNR). As the tumor size is decreased, additional shields to block the fat signal are necessary in order to detect the cancer signal. Conclusion: Coherent scatter analysis using a wide slot setup is promising as an enhancement for screening mammography. This work was supported by National Institutes of Health, # 7 R01EB009715.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Gradient Coding With Iterative Block Leverage Score Sampling

Gradient coding is a method for mitigating straggling servers in a centralized computing network that uses erasure-coding techniques to distributively carry out first-order optimization methods. Randomized numerical linear algebra uses randomization to develop improved algorithms for large-scale linear algebra computations. In this study, we propose a method for distributed optimization that combines gradient coding and randomized numerical linear algebra. The proposed method uses a randomized ℓ 2 -subspace embedding and a gradient coding technique to distribute blocks of data to the computational nodes of a centralized network, and at each iteration the central server only requires a small number of computations to obtain the steepest descent update. The novelty of our approach is that the data is replicated according to importance scores, called block leverage scores, in contrast to most gradient coding approaches that uniformly replicate the data blocks. Furthermore, we do not require a decoding step at each iteration, avoiding a bottleneck in previous gradient coding schemes. We show that our approach results in a valid ℓ 2 -subspace embedding, and that our resulting approximation converges to the optimal solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Online Model-Free Chance-Constrained Distribution System Voltage Control Using DERs

This paper proposes an online data-driven distributed energy resource management system (DERMS) optimization method using chance-constrained formulation to address distribution system voltage regulation. This is achieved via the local sensitivity factor (LSF)-enabled reformulation of the DER control into a linear programming (LP) problem, which is easy and computationally efficient to solve. The LSF is estimated using online measurements and does not need the assumption of node load information. The latter is usually required for existing optimization-based methods but is difficult to obtain in practice. To mitigate measurement uncertainties, a scenario-based chance-constrained formulation is constructed. Compared with other control methods, the results carried out in a realistic distribution system show that the proposed method can effectively eliminate voltage violation issues.

chance-constrained optimization↗
DOE OSTI2023

Online Model-Free Chance-Constrained Distribution System Voltage Control Using DERs: Preprint

This paper proposes an online data-driven distributed energy resource management system (DERMS) optimization method using chance-constrained formulation to address distribution system voltage regulation. This is achieved via the local sensitivity factor (LSF)-enabled reformulation of the DER control into a linear programming (LP) problem, which is easy and computationally efficient to solve. The LSF is estimated using online measurements and does not need the assumption of node load information. The latter is usually required for existing optimization-based methods but is difficult to obtain in practice. To mitigate measurement uncertainties, a scenario-based chance-constrained formulation is constructed. Compared with other control methods, the results carried out in a realistic distribution system show that the proposed method can effectively eliminate voltage violation issues.

chance-constrained optimization↗
DOE OSTI2022

Frhodo: A program for simulating chemical kinetic measurements and optimizing kinetic mechanisms

Chemical kinetic simulations are frequently used to extract kinetic and mechanistic information from experimental data by fitting simulations to the data. For simple systems of consecutive single channel reactions this is a relatively straightforward task. However, as the complexity of the system increases and particularly for systems involving competitive multi-channel reactions manually optimizing a mechanism to obtain the best fits to a range of experimental data quickly becomes a time-intensive and challenging process, especially when rate coefficients have pressure and temperature dependencies. There is considerable potential for exploiting automated optimization methods to rapidly screen reaction mechanisms and optimize the fit by adjusting rate coefficients within well-defined constraints. A new chemical kinetics simulation program, Frhodo, has been developed for this purpose. The program allows either manual simulation of individual experiments or fully automated optimization against a range of experimental data by adjusting user selected reactions within user defined constraints. Frhodo incorporates machine learning-based optimization routines that work by either minimizing standardized residuals or through Bayesian parameter estimation. Frhodo's optimization capabilities are demonstrated by reexamining two previously studied systems of relevance to combustion. Dissociation of diacetyl followed by recombination of methyl radicals is an example of a sequence of single channel, consecutive reactions. Pyrolysis of 2-methyl furan exemplifies multi-channel, unimolecular reactions that are often encountered and have temperature and pressure dependencies in the rate coefficients of the channels and branching ratios between them. For both systems the optimization routines resulted in solutions similar to the original studies. Finally, comments are made on the strengths and limitations of the approach.

0D simulation↗
DOE OSTI2019

Aperture‐Synthesis Radar Imaging With Compressive Sensing for Ionospheric Research

Abstract Inverse methods involving compressive sensing are tested in the application of two‐dimensional aperture‐synthesis imaging of radar backscatter from field‐aligned plasma density irregularities in the ionosphere. We consider basis pursuit denoising, implemented with the fast iterative shrinkage thresholding algorithm, and orthogonal matching pursuit (OMP) with a wavelet basis in the evaluation. These methods are compared with two more conventional optimization methods rooted in entropy maximization (MaxENT) and adaptive beamforming (linearly constrained minimum variance or often “Capon's Method.”) Synthetic data corresponding to an extended ionospheric radar target are considered. We find that MaxENT outperforms the other methods in terms of its ability to recover imagery of an extended target with broad dynamic range. Fast iterative shrinkage thresholding algorithm performs reasonably well but does not reproduce the full dynamic range of the target. It is also the most computationally expensive of the methods tested. OMP is very fast computationally but prone to a high degree of clutter in this application. We also point out that the formulation of MaxENT used here is very similar to OMP in some respects, the difference being that the former reconstructs the logarithm of the image rather than the image itself from basis vectors extracted from the observation matrix. MaxENT could in that regard be considered a form of compressive sensing.

Hysell, D. L.↗
DOE OSTI2024

Hamiltonian switching control of noisy bipartite qubit systems

Abstract We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the quantum approximate optimization algorithm, to mitigate environmental noise on qubits. We demonstrate the control for a central spin coupled to bath spins via isotropic Heisenberg interactions, and then make physical applications to the protection of quantum gates performed on superconducting transmon qubits coupling to environmental two-level-systems (TLSs) through dipole-dipole interactions, as well as on such qubits coupled to both TLSs and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient to optimize the Hamiltonian switching control protocols, using a fidelity objective for specific target quantum gates. We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999 999 999 (nine nines) upon a subsequent optimization by GRadient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.

Physics↗
DOE OSTI2019

Parallel general purpose multiobjective optimization framework with application to electron beam dynamics

Particle accelerators are invaluable tools for research in the basic and applied sciences, such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a nontrivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. The Argonne Wakefield Accelerator facility has some unique challenges resulting from its purpose to carry out advanced accelerator R&D. Individual experiments often have challenging beam requirements, and the physical configuration of the beam lines is often changed to accommodate the variety of supported experiments. The need for rapid deployment of different operational settings further complicates the optimization work that must be done for multiple constraints and challenging operational regimes. One example of this is an independently staged two-beam acceleration experiment which requires the construction of an additional beam line (this is now in progress). The high charge drive beam, well into the space charge regime, must be threaded through small aperture (17.6 mm) decelerating structures. In addition, the bunch length must be sufficiently short to maximize power generation in the decelerator. We propose to tackle this problem by means of multiobjective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame, a fast and scalable software framework is required. In this paper, we present a general-purpose framework for simulation-based multiobjective optimization methods that allows the automatic investigation of optimal sets of machine parameters. Using evolutionary algorithms as the optimizer and opal as the forward solver, validation experiments and results of multiobjective optimization problems in the domain of beam dynamics are presented. Optimized solutions for the new high charge drive beam line found by the framework were used to finish the design of a two beam acceleration experiment. The selected solution along with the associated beam parameters is presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

ParaOpt: Automated Application Parameterization and Optimization for the Cloud

The variety of instance types available on cloud platforms offers enormous flexibility to match the requirements of applications with available resources. However, selecting the most suitable instance type and configuring an application to optimally execute on that instance type can be complicated and time-consuming. For example, application parallelism flags must match available cores and problem sizes must be tuned to match available memory. As the search space of application configurations can be enormous, we propose an automated approach, called ParaOpt, to automatically explore and tune application configurations on arbitrary cloud instances. ParaOpt supports arbitrary applications, enables use of custom optimization methods, and can be configured with different optimization targets such as runtime and cost. We evaluate ParaOpt by optimizing genomics, molecular dynamics, and machine learning applications with four types of optimizers. We show with as few as 15 parameterized executions of an application, representing between 1.2%-26.7% of the search space, that ParaOpt is able to identify the optimal configuration in 32.7% of experiments and a near-optimal configuration in 83.2% of cases. As a result of using near-optimal configurations, ParaOpt reduces overall execution time by up to 85.8% when compared with using the default configuration.

Wu, Chaofeng↗
DOE OSTI2014

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

1st Computational Physics School for Fusion Research (2019 CPS-FR)

The rising number of applications of machine learning and computational statistics in fusion energy research requires flexibility in adopting a growing variety of tools. The Computational Physics School for Fusion Research (CPS-FR) aims at providing young researchers with critical skill sets to deal with modern fusion energy research challenges. The School aims at covering essentials of: Computational Statistics, Machine Learning, Deep Learning and optimization methods, Parallel Programming and HPC. As the first edition of the CPS-FR just concluded, this report highlights its main results and summarizes its contents.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Direct discontinuous Galerkin method and its variations for second order elliptic equations

In this study, we study direct discontinuous Galerkin method (Liu and Yan in SIAM J Numer Anal 47(1):475–698, 2009) and its variations (Liu and Yan in Commun Comput Phys 8(3):541–564, 2010; Vidden and Yan in J Comput Math 31(6):638–662, 2013; Yan in J Sci Comput 54(2–3):663–683, 2013) for 2nd order elliptic problems. A priori error estimate under energy norm is established for all four methods. Optimal error estimate under L 2 norm is obtained for DDG method with interface correction (Liu and Yan in Commun Comput Phys 8(3):541–564, 2010) and symmetric DDG method (Vidden and Yan in J Comput Math 31(6):638–662, 2013). A series of numerical examples are carried out to illustrate the accuracy and capability of the schemes. Numerically we obtain optimal (k+1)th order convergence for DDG method with interface correction and symmetric DDG method on nonuniform and unstructured triangular meshes. An interface problem with discontinuous diffusion coefficients is investigated and optimal (k+1)th order accuracy is obtained. Peak solutions with sharp transitions are captured well. Highly oscillatory wave solutions of Helmholz equation are well resolved.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗