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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantifying particle-scale 3D granular dynamics during rapid compaction from time-resolved in situ 2D x-ray images

Understanding the particle-scale dynamics of granular materials during rapid compaction and flow is of fundamental importance for manufacturing, planetary science, geology, and defense applications. Time-resolved 2D radiography and static 3D x-ray tomography are powerful in situ tools for studying particle-scale dynamics but provide detail only in 2D or with significant time-scale limitations, respectively. Here, we introduce a new method that uses 2D in situ x-ray imaging for determining time-resolved 3D particle-scale dynamics in rapidly compressed granular materials. The method employs initial particle packing structures obtained from x-ray tomography, a 2D x-ray image generation algorithm, and an optimization algorithm. We first describe and validate the method using finite element simulations. Furthermore, we then apply the technique to x-ray phase-contrast images obtained during rapid compaction of granular materials with varying particle morphology and sample thickness. The depth-resolved particle-scale dynamics reveal complex velocity and porosity fields evolving heterogeneously along and perpendicular to the compaction direction. We characterize these features, their fluctuations near the compaction front, and the compaction front thickness. Our technique can be applied to understanding granular dynamics during rapid compaction events, and rearrangements during slower, but non-quasi-static, flows.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cost Drivers for a Tokamak-Based Compact Pilot Plant

A comprehensive systems code that includes a range of physics and engineering considerations along with a simplified costing model has been utilized to evaluate the primary cost drivers for a compact tokamak pilot plant. The systems code has been benchmarked against several tokamak reactor designs and is utilized with sophisticated optimization algorithms to develop optimal solutions for a set of user-specified assumptions and design constraints. Here, in contrast to previous models that have focused on the cost of electricity as the key cost metric, this study uses the estimated capital cost of the facility. The analysis suggests that a pilot plant with the following features may offer potential for a cost-attractive pilot plant: A ~ 3, H 98y2 > 1.5, P net = 200 MW, 𝜏 𝑝⁢𝑢⁢𝑙⁢𝑠⁢𝑒 ~ 1 to 2 h utilizing rare-earth barium copper oxide (REBCO) magnet technology and a plug-bucked central solenoid/toroidal field (CS/TF) magnet support structure. While REBCO magnets offer some advantages relative to Nb 3 Sn magnets in all cases, the most gain is obtained when combined with the plug-bucked CS/TF bucking solution. Pulsed operation reduces capital cost requirements relative to steady-state operation, especially at low confinement. Cost sensitivity studies indicate that there are significant cost uncertainties associated with the achievable confinement quality, tritium breeding capability, attainable thermal efficiency, and achievable neutron wall loading, suggesting that these areas are the most critical areas in reducing the cost risk for a compact tokamak pilot plant. Further cost sensitivity studies indicate that the estimated cost is most sensitive to the underlying cost of the magnetic coils, providing further impetus to better establish cost-effective means for producing fusion magnets.

Compact pilot plant↗
DOE OSTI2022

Reaction Rover

Reaction Rover is a software application designed to enable reaction optimization with little or no human intervention and to provide a flexible platform to facilitate rapid testing, automation, and implementation of new reaction optimization algorithms. Toward these goals, Reaction Rover is designed to be modular, allowing the optimization algorithm, analyzer, and reactor components to be changed for any given process using easily integrated plugins. The Reaction Rover platform is a key development that enables the testing, use, and automation of machine learning (ML) and non-ML optimizers in a variety of laboratory situations.

Windus, Theresa↗
DOE OSTI2021

Enhanced Integer Permutation based Genetic Algorithm for Optimization of Tube-Fin Heat Exchanger Circuitry with Splits and Merges

Tube-fin heat exchangers (HXs) are widely used in air-conditioning and heat pump applications. The performance of these heat exchangers is strongly influenced by the refrigerant circuitry. Studies have proved that by optimizing the refrigerant circuitry, the performance of HXs can be significantly improved. In our previous research, an Integer Permutation based Genetic Algorithm (IPGA) was developed to obtain the optimal circuitry designs. Our previous research showed that IPGA demonstrates superior capability to obtain better refrigerant circuitries with lower computational cost than the other methods in literature. And the optimal circuitry designs obtained from IPGA are manufacturable with the available tooling. However, the IPGA developed previously cannot generate designs with splitting and merging of circuits. To remedy this limitation, a new chromosome which can represent circuitry with splitting and merging of circuits is developed. In addition to the six genetic operators implemented previously, two new genetic operators are developed to generate splits and merges. As a result, the enhanced IPGA can explore the solution space more thoroughly than the previous IPGA. A case study using an evaporator from an A-type indoor unit shows that, given the similar capacity improvements obtained from the enhanced IPGA compared with the previous IPGA, the refrigerant pressure drop reduction obtained from the enhanced IPGA is 26.5% compared against 1.0% pressure drop reduction from the previous IPGA. The benchmark of the enhanced IPGA with other methods in literature demonstrates that the enhanced IPGA can generate circuitry designs with performance superior to those obtained from other methods.

LI, Zhenning↗
DOE OSTI2015

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plans in terms of average delay, number of stops, and vehicular emissions at the network level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Performance of biologically inspired algorithms tuned on TiO 2 nanoparticle benchmark system

Biologically inspired optimization algorithms (BIOA), such as particle swarm optimization (PSO) and differential evolution (DE), are often applied to a specific problem with the tacit assumptions that each algorithm will have a unique set of “ideal” tuning parameters that provide “best” performance, and that, once tuned, two different optimization algorithms ought to exhibit distinct performance. This work tests those assumptions by systematically comparing PSO and DE applied to identify stable and metastable configurations of a benchmark system, a titania dimer Ti 2 O 4 , whose energy is described by a modified Matsui-Akaogi pairwise potential. The hypotheses (H) are formally expressed as: (H1) for both PSO and DE there exists a unique, optimal set of tuning parameters that maximize its performance on a given problem; (H2) when running two different BIOAs, one algorithm must demonstrate superior performance over the other. By identifying and studying an approximate Pareto-optimal set of tuning parameters for DE and PSO, H1 is shown to be strictly false for DE, and tentatively false for PSO, while H2 is shown to be false with respect to certain statistics. Here, we further investigate the correlations between key tuning parameters and algorithm performance. Our conclusion may be implemented to further optimize and develop BIOAs applied to materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

Diao, Xiaoxu (ORCID:0000000346726352)↗
DOE OSTI2025

Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification

Abstract The vitrification of high‐level waste (HLW) by heating a mixture of glass‐forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first‐order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Algorithm-guided experimentation for autonomous AI systems in self-driving laboratories

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. This is done by using the Julia programming language and the Julia packages PowerModels.jl and Manopt.jl.

AC optimal power flow↗
DOE OSTI2014

Topography-based Flood Planning and Optimization Capability Development Report

Globally, water-related disasters are among the most frequent and costly natural hazards. Flooding inflicts catastrophic damage on critical infrastructure and population, resulting in substantial economic and social costs. NISAC is developing LeveeSim, a suite of nonlinear and network optimization models, to predict optimal barrier placement to protect critical regions and infrastructure during flood events. LeveeSim currently includes a high-performance flood model to simulate overland flow, as well as a network optimization model to predict optimal barrier placement during a flood event. The LeveeSim suite models the effects of flooding in predefined regions. By manipulating a domain’s underlying topography, developers altered flood propagation to reduce detrimental effects in areas of interest. This numerical altering of a domain’s topography is analogous to building levees, placing sandbags, etc. To induce optimal changes in topography, NISAC used a novel application of an optimization algorithm to minimize flooding effects in regions of interest. To develop LeveeSim, NISAC constructed and coupled hydrodynamic and optimization algorithms. NISAC first implemented its existing flood modeling software to use massively parallel graphics processing units (GPUs), which allowed for the simulation of larger domains and longer timescales. NISAC then implemented a network optimization model to predict optimal barrier placement based on output from flood simulations. As proof of concept, NISAC developed five simple test scenarios, and optimized topographic solutions were compared with intuitive solutions. Finally, as an early validation example, barrier placement was optimized to protect an arbitrary region in a simulation of the historic Taum Sauk dam breach.

42 ENGINEERING↗
DOE OSTI2020

Parameter Optimization Toolbox for NS-3 network optimization, NS-3 Parameter Optimization Framework [SWR-18-60]

This simulation-based parameter optimization framework is proposed to tune parameters of different types of communication networks using ns-3 to achieve the optimal network performance. It consists of three main components: an ns-3 packet reporting module; a sampler running simulations with all possible parameter sets for the input parameter variables by using a parallel executor at each generation; and a hybrid optimization algorithm for tuning configurable parameters of hybrid designs and application parameter variables. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient descent function to quickly achieve an approximate globally optimum solution. This software is designed to be used in a multi-core processing Linux environment and run over a long duration of time. The execution time varies depending mainly upon the nature of the ns-3 configuration being simulated. This software includes a custom ns-3 QoS measurement application which must be included with the ns-3 source code during installation of the software.

Hasandka, Adarsh↗
DOE OSTI2022

Learning Stochastic Parametric Differentiable Predictive Control Policies

We present a scalable unsupervised learning-based method for obtaining explicit control policies for model predictive control problems for stochastic linear systems with additive uncertainties subject to nonlinear chance constraints. We call the proposed method stochastic parametric differentiable predictive control (SP-DPC), which extends the recently proposed deterministic DPC policy optimization algorithm. We formulate the SP-DPC as a deterministic approximation to the stochastic parametric constrained optimal control problem via independent sampling of the problem's parameters and uncertainties. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the problem's value function is backpropagated through the finite-time closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy. We also provide theoretical probabilistic guarantees on closed-loop stability and chance constraints satisfaction for systems controlled by learned neural policies. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Drgona, Jan↗
DOE OSTI2020

Using a Genetic Algorithm to Optimize Configurations in a Data-Driven Application

Users of highly-configurable software systems often want to optimize a particular objective such as improving a functional outcome or increasing system performance. One approach is to use an evolutionary algorithm. However, many applications today are data-driven, meaning they depend on inputs or data which can be complex and varied. Hence, a search needs to be run (and re-run) for all inputs, making optimization a heavy-weight and potentially impractical process. In this paper, we explore this issue on a data-driven highly-configurable scientific application. We build an exhaustive database containing 3,000 configurations and 10,000 inputs, leading to almost 100 million records as our oracle, and then run a genetic algorithm individually on each of the 10,000 inputs. We ask if (1) a genetic algorithm can find configurations to improve functional objectives; (2) whether patterns of best configurations over all input data emerge; and (3) if we can we use sampling to approximate the results. We find that the original (default) configuration is best only 34% of the time, while clear patterns emerge of other best configurations. Out of 3,000 possible configurations, only 112 distinct configurations achieve the optimal result at least once across all 10,000 inputs, suggesting the potential for lighter weight optimization approaches. We show that sampling of the input data finds similar patterns at a lower cost.

Sinha, Urjoshi↗
DOE OSTI2022

Particle Swarm Optimisation for group structure optimization for radiotherapy shielding

Neutron transport simulations are ubiquitous in nuclear engineering because they allow one to model experimental systems and render a model platform for easy perturbation of experimental designs. In addition, simulations allow one to gain experimental insight without actually having to go through the trouble of building a physical experiment. Neutron transport simulations can be stochastic or deterministic based. Stochastic neutron transport simulations are typically simulated using the Monte Carlo method and yield very accurate solutions but are computationally expensive, while deterministic methods are typically faster but can be less accurate. Here we focus on optimizing the accuracy of deterministic neutron transport simulations for radiotherapy simulations. Deterministic neutron transport requires discretization of angle, energy, and space to appropriately analyze the system one is trying to model. Discretization of energy is challenging because of the highly variable neutron flux at certain neutron energies. Improper discretization of energy in the transport model can lead to erroneous results and therefore inaccurate interpretations of the solution. In this study, we evaluate Particle Swarm Optimization (PSO) as a mechanism for selecting optimal group structures for radiotherapy shielding. We tested the particle swarm optimization algorithm on radiotherapy shielding problems using Los Alamos National Laboratory's (LANL) main deterministic transport code PARTISN. Results show that the optimized energy group structures generated from the optimization algorithm outperformed LANL's standard energy group structures, and therefore demonstrate utility in using PSO to expedite computation times due to the increased accuracy obtained with a smaller but optimized group structure. (authors)

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Optimal band selection for target detection with a LWIR multispectral imager

Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and most consistent detection separation scores on average.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

NEUTRON STAR MASS–RADIUS CONSTRAINTS USING EVOLUTIONARY OPTIMIZATION

The equation of state of cold supra-nuclear-density matter, such as in neutron stars, is an open question in astrophysics. A promising method for constraining the neutron star equation of state is modeling pulse profiles of thermonuclear X-ray burst oscillations from hot spots on accreting neutron stars. The pulse profiles, constructed using spherical and oblate neutron star models, are comparable to what would be observed by a next-generation X-ray timing instrument like ASTROSAT , NICER , or a mission similar to LOFT . In this paper, we showcase the use of an evolutionary optimization algorithm to fit pulse profiles to determine the best-fit masses and radii. By fitting synthetic data, we assess how well the optimization algorithm can recover the input parameters. Multiple Poisson realizations of the synthetic pulse profiles, constructed with 1.6 million counts and no background, were fitted with the Ferret algorithm to analyze both statistical and degeneracy-related uncertainty and to explore how the goodness of fit depends on the input parameters. For the regions of parameter space sampled by our tests, the best-determined parameter is the projected velocity of the spot along the observer’s line of sight, with an accuracy of ≤3% compared to the true value and with ≤5% statistical uncertainty. The next best determined are the mass and radius; for a neutron star with a spin frequency of 600 Hz, the best-fit mass and radius are accurate to ≤5%, with respective uncertainties of ≤7% and ≤10%. The accuracy and precision depend on the observer inclination and spot colatitude, with values of ∼1% achievable in mass and radius if both the inclination and colatitude are ≳60°.

79 ASTRONOMY AND ASTROPHYSICS↗