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DOE OSTI2020

Fourier Analysis-Based Evolutionary Multi-Objective Multiphysics Optimization of Liquid-Cooled Heat Sinks

Optimal heat dissipation in power modules can significantly increase their power density. Removing the generated heat is critical for capturing the benefits of advanced semiconductor materials and improving the reliability of the device operation. This article proposes a design optimization method for liquid-cooled heat sinks that use a Fourier analysis-based tool and an evolutionary optimization algorithm to optimize the heat sink geometry for specified objectives. The optimized heat sink geometry is then compared with state-of-the-art solutions in literature based on finite element analysis of different designs. The proposed methodology can develop complex geometries that outperform the conventional heat sink geometries.

Sahu, Raj↗

DOE OSTI2025

DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION

This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Development of a Modified Embedded Atom Force Field for Zirconium Nitride Using Multi-Objective Evolutionary Optimization

Zirconium nitride (ZrN) exhibits exceptional mechanical, chemical, and electrical properties, which make it attractive for a wide range of technological applications, including wear-resistant coatings, protection from corrosion, cutting/shaping tools, and nuclear breeder reactors. Despite its broad usability, an atomic scale understanding of the superior performance of ZrN, and its response to external stimuli, for example, temperature, applied strain, and so on, is not well understood. This is mainly due to the lack of interatomic potential models that accurately describe the interactions between Zr and N atoms. To address this challenge, we develop a modified embedded atom method (MEAM) interatomic potential for the Zr–N binary system by training against formation enthalpies, lattice parameters, elastic properties, and surface energies of ZrN (and, in some cases, also Zr3N4) obtained from density functional theory (DFT) calculations. The best set of MEAM parameters are determined by employing a multiobjective global optimization scheme driven by genetic algorithms. Our newly developed MEAM potential accurately reproduces structure, thermodynamics, energetic ordering of polymorphs, as well as elastic and surface properties of Zr–N compounds, in excellent agreement with DFT calculations and experiments. As a representative application, we employed molecular dynamics simulations based on this MEAM potential to investigate the atomic scale mechanisms underlying fracture of bulk and nanopillar ZrN under applied uniaxial strains, as well as the impact of strain rate on their mechanical behavior. These simulations indicate that bulk ZrN undergoes brittle fracture irrespective of the strain rate, while ZrN nanopillars show quasi-plasticity owing to amorphization at the crack front. The MEAM potential for Zr–N developed in this work is an invaluable tool to investigate atomic-scale mechanisms underlying the response of ZrN to external stimuli (e.g, temperature, pressure etc.), as well as other interesting phenomena such as precipitation.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations

Predictive Molecular Dynamics simulations of thermal transport require forcefields that can simultaneously reproduce several structural, thermodynamic and vibrational properties of materials like lattice constants, phonon density of states, and specific heat. This requires a multi-objective optimization approach for forcefield parameterization. Existing methodologies for forcefield parameterization use ad-hoc and empirical weighting schemes to convert this into a single-objective optimization problem. Here, we provide and describe software to perform multi-objective optimization of Stillinger–Weber forcefields (SWFF) for two-dimensional layered materials using the recently developed 3rd generation non-dominated sorting genetic algorithm (NSGA-III). NSGA-III converges to the set of optimal forcefields lying on the Pareto front in the multi-dimensional objective space. This set of forcefields is used for uncertainty quantification of computed thermal conductivity due to variability in the forcefield parameters. We demonstrate this new optimization scheme by constructing a SWFF for a representative two-dimensional material, 2H-MoSe 2 and quantifying the uncertainty in their computed thermal conductivity.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the developed hardware can greatly depend on the size of the network that is being evaluated on the chip. Furthermore, the accuracy of a trained SNN that is evaluated on chip can change due to voltage and current variations in the hardware that perturb the learned weights of the network. While efforts are made on the hardware side to minimize those perturbations, a software based strategy to make the deployed networks more resilient can help further alleviate that issue. In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults. We leverage an evolutionary algorithm to train the SNNs and propose a multiobjective fitness function to optimize the size and resiliency of the SNN. We demonstrate that this strategy leads to well-performing, small-sized networks that are more resilient to hardware faults.

Dimovska, Mihaela↗

DOE OSTI2025

Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt

As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.

09 BIOMASS FUELS↗

DOE OSTI2025

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Abstract Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraints on energy requirements of the beam. Using historical accelerator data, we develop a physics-based surrogate model which is differentiable and allows for back-propagation of gradients. The results are evaluated in the form of a Pareto-front with two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.

43 PARTICLE ACCELERATORS↗

DOE OSTI2015

Classifier-Guided Sampling for Complex Energy System Optimization

This report documents the results of a Laboratory Directed Research and Development (LDRD) effort entitled “Classifier-Guided Sampling for Complex Energy System Optimization” that was conducted during FY 2014 and FY 2015. The goal of this project was to develop, implement, and test major improvements to the classifier-guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for performing search and optimization over a set of discrete design variables in the face of one or more objective functions. Existing evolutionary algorithms, such as genetic algorithms, may require a large number of objective function evaluations to identify optimal or near-optimal solutions. Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduces the evaluation count by using a Bayesian network classifier to filter out non-promising candidate designs, prior to evaluation, based on their posterior probabilities. In this project, both the single-objective and multi-objective versions of the CGS are developed and tested on a set of benchmark problems. As a domain-specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗