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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
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 OSTI2021

Fast multiscale contrast independent preconditioners for linear elastic topology optimization problems

The goal of this work is to present a fast and viable approach for the numerical solution of the high-contrast state problems arising in topology optimization. The optimization process is iterative, and the gradients are obtained by an adjoint analysis, which requires the numerical solution of large high-contrast linear elastic problems with features spanning several length scales. The size of the discretized problems forces the utilization of iterative linear solvers with solution time dependent on the quality of the preconditioner. The lack of clear separation between the scales, as well as the high-contrast, imposes severe challenges on the standard preconditioning techniques. Thus, here we propose new methods for the high-contrast elasticity equation with performance independent of the high-contrast and the multi-scale structure of the elasticity problem. The solvers are based on two-levels domain decomposition techniques with a carefully constructed coarse level to deal with the high-contrast and multi-scale nature of the problem. The construction utilizes spectral equivalence between scalar diffusion and each displacement block of the elasticity problems and, in contrast to previous solutions proposed in the literature, is able to select the appropriate dimension of the coarse space automatically. The new methods inherit the advantages of domain decomposition techniques, such as easy parallelization and scalability. Finally, the presented numerical experiments demonstrate the excellent performance of the proposed methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An optimization-based approach to parameter learning for fractional type nonlocal models

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this study we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Data-driven simultaneous process optimization and adsorbent selection for vacuum pressure swing adsorption

Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. Here, this work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO 2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models

We report lattice-like structures can provide a combination of high stiffness with light weight that is useful in many applications, but a resolved finite element mesh of such structures results in a computationally expensive discretization. This computational expense may be particularly burdensome in many-query applications, such as optimization. We develop a stress-constrained topology optimization method for lattice-like structures that uses component-wise reduced order models as a cheap surrogate, providing accurate computation of stress fields while greatly reducing run time relative to a full order model. We demonstrate the ability of our method to produce large reductions in mass while respecting a constraint on the maximum stress in a pair of test problems. The ROM methodology provides a speedup of about 150x in forward solves compared to full order static condensation and provides a relative error of less than 5% in the relaxed stress.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A direct transcription-based multiple shooting formulation for dynamic optimization

The growing need for fast and efficient solution techniques for solving dynamic optimization problems is driven by a broad spectrum of applications in scheduling and control. We suggest a novel framework for dynamic optimization that utilizes a multiple shooting “backbone” with discrete rather than continuous subproblems, thereby eliminating need for repeated time-integration. A Lagrangian relaxation (LR)-based decomposition scheme is proposed, which dualizes the state continuity requirements between subproblems and enables parallel solution of the problem. We demonstrate the applicability of our method on two case studies: the Van der Pol oscillator and a batch reactor.

42 ENGINEERING↗
DOE OSTI2020

Integration of graphical approaches into optimization-based design of multistage liquid extraction

We propose two optimization models for designing two liquid extraction systems: simple multistage liquid extractors and extractors with extract reflux. Both models are motivated by the concepts of the modified McCabe-Thiele graphical method for multistage extractor design. The operating and equilibrium curves in the McCabe-Thiele method are represented by material balances and piece-wise linearized thermodynamics properties. The use of piece-wise approximations improves computational tractability of both optimization models. In addition, we consider some extensions such as dilute systems, insoluble solvents, and non-ideal stages. In conclusion, the applicability of the proposed models is demonstrated with four illustrative examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Co-training of multiple neural networks for simultaneous optimization and training of physics-informed neural networks for composite curing

This paper introduces a Physics-Informed Neural Network (PINN) technique that co-trains neural networks (NNs) that represent each function in a system of equations to simultaneously solve equations representing an out-of-autoclave (OOA) cure process while conducting optimization in adherence to process requirements. Specifically, this co-training approach benefits from using NNs to represent OOA inputs (air temperature profile) and outputs (part and tool temperature profiles and degree of cure). Production requirements can then be levied on the inputs, such as maximum air temperature and minimum cure cycle, and simultaneously on the outputs, such as degree of cure, maximum part temperature, and part temperature rate limits. The technique is validated with finite element (FE) simulations and physical experiments for curing a Toray T830H-6 K/3900-2D composite panel. Furthermore, this novel approach efficiently models and optimizes the OOA cure process.

Composite curing↗
DOE OSTI2026

Innovations in underground hydrogen storage with multiphysics simulations, optimization, and monitoring: A review

Underground Hydrogen Storage (UHS) is a promising solution for large-scale energy storage and a critical component in advancing low-carbon energy system. Ensuring the safety and efficiency of UHS necessitates a comprehensive understanding of multiphysical interactions driven by cyclic pore fluid pressure fluctuations and coupled physicochemical processes. Here, this review examines the key geomechanical responses in UHS, including rock property variations under cyclic loading, fracture evolution and propagation, reservoir stress sensitivity, and fault stability. It also explores the impact of geochemical and microbial reactions on geomechanical characteristics. We provide an in-depth analysis of Thermal-Hydraulic-Mechanical-Chemical (THMC) coupled numerical simulations, highlighting their potential for future multi-scale modeling. Limitations of current machine learning (ML) approaches in addressing UHS challenges are highlighted, emphasizing the need for innovative ML-based methodologies. Operational strategies for hydrogen injection and production are reviewed, focusing on safety, efficiency, and economic viability. The necessity for multi-objective optimization (MOO) to balance storage efficiency, risk mitigation, and cost-effectiveness is also discussed. Current monitoring technologies are evaluated to ensure safe and efficient UHS operations. Finally, this review identifies critical knowledge gaps and underscores the importance of advancing geomechanical understanding under multiphysics-coupling. We highlight the need for ML-driven multiphysics theories, enhanced modeling techniques, and robust optimization strategies to improve UHS performance. This study serves as a comprehensive reference for future research and the large-scale implementation of UHS systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Surrogate-driven design optimization with uncertainty constraints in Monte Carlo simulations

In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predictions, as their performance depends heavily on the quality of training data. This study investigates the impact of data uncertainty on two multi-objective design problems modelled using Monte Carlo transport simulations: a neutron moderator and an ion-to-neutron converter. For each, a grid search was performed using five different tally uncertainty levels to generate training data for neural network surrogate models. These models were then optimized using NSGA-III. The recovered Pareto-fronts were analyzed across uncertainty levels: in the moderator problem, normalized hypervolume dropped from 0.886 at 1.0% uncertainty to 0.748 at 10% uncertainty, while in the converter problem it remained near 0.50 for all cases. Average simulation times were also compared to evaluate the trade-off between accuracy and computational cost. Results show that the influence of simulation uncertainty is strongly problem-dependent. In the neutron moderator case, higher uncertainties led to exaggerated objective sensitivities and distorted Pareto-fronts, reducing normalized hypervolume. In contrast, the ion-to-neutron converter task was less affected—low-fidelity simulations produced results similar to those from high-fidelity data. These findings suggest that a fixed-fidelity approach is not optimal. Surrogate models can recover the Pareto-front under noisy conditions, and multi-fidelity studies help identify suitable uncertainty levels for each problem to balance efficiency and accuracy.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

A multistage distributionally robust optimization approach to water allocation under climate uncertainty

This paper investigates a Multistage Distributionally Robust Optimization (MDRO) approach to water allocation under climate uncertainty. The MDRO is formed by creating sets of conditional distributions (called conditional ambiguity sets) on a finite scenario tree. The distributions in the conditional ambiguity sets remain close to a nominal conditional distribution according a ø-divergence (e.g., Kullback-Leibler divergence, Hellinger distance, Burg entropy, etc.). Here, the paper discusses a decomposition algorithm to solve the resulting MDRO with ø-divergences, which uses the dual formulation and solves only linear subproblems instead of convex ones. Some properties of the algorithm such as generating feasible policies and valid upper/lower bounds are established. The paper then applies the modeling and solution techniques to allocate water in a rapidly-developing area of Tucson, Arizona. Tucson, like many arid and semi-arid regions around the world, faces considerable uncertainty in its ability to provide water for its citizens in the future. The primary sources of uncertainty in the Tucson region include (1) unpredictable population growth, (2) the availability of water from the Colorado River, and (3) the effects of climate variability on water consumption. This paper integrates forecasts for all these sources of uncertainty into a single optimization model for robust and sustainable water allocation. Then, it uses this model to analyze the value of constructing additional treatment facilities to reduce future water shortages. The results indicate that the MDRO approach can be very valuable for water managers by providing insights to minimize their risks and help them plan for the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A hybrid robust-stochastic optimization approach for day-ahead scheduling of cascaded hydroelectric system in restructured electricity market

Uncertainties arising from complicated natural and market environments pose great challenges for the efficient operation of cascaded hydroelectric systems. To overcome these challenges, this paper studies the day-ahead scheduling of cascaded hydroelectric systems in a restructured electricity market with the presence of uncertainties in electricity price and natural water inflow. To properly model the uncertainty, we consider the unique characteristics of these two types of uncertainties and capture them via the uncertainty set and stochastic scenarios, respectively. Further, a hybrid robust-stochastic optimization model is developed to simultaneously hedge against these two types of uncertainties, which is formulated as a large-scale non-convex optimization problem with mixed integer recourse. After introducing linearization of nonlinear terms, a tailored hybrid decomposition scheme combining Lagrangian relaxation and Dantzig-Wolfe decomposition is adopted to achieve efficient computation of the proposed model. Two real-world cases are conducted to demonstrate the capability and characteristics of the proposed model and algorithms.

13 HYDRO ENERGY↗
DOE OSTI2024

Hydrogen underground storage for grid electricity storage: An optimization study on techno-economic analysis

Here, this study performs a techno-economic analysis of hydrogen underground storage systems for grid electricity storage, evaluating their economic viability at the plant scale using dynamic optimization. It explores the feasibility of various system configurations and revenue models in the context of volatile electricity prices and the necessity for multiple revenue streams. The hypothesis tested is that large-scale hydrogen storage, despite its low round-trip efficiency, can be economically viable with the right mix of revenue streams. This study uses scenario-based analysis to assess the impacts of different system configurations, including engaging in time-shifting arbitrage, ancillary service markets and blending hydrogen with natural gas. Results indicate potential annual net cash flows of up to $\$$1.5 million from ancillary services integration and $\$$5.2 million from natural gas blending, contingent on specific system sizes. The study concludes that hydrogen underground storage for grid electricity storage can be profitable, and emphasizes that proper system design and precise electricity price forecasting are crucial for optimizing system performance and economic returns. This research sets the stage for further investigations into the scalability of hydrogen storage systems and their broader implications for grid electricity storage and energy market dynamics.

25 ENERGY STORAGE↗
DOE OSTI2024

Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability

Despite their crucial role in supplying heat and power to universities, industries, and healthcare facilities, many steam-based district heating systems rely on outdated control methods. Among these, multi-central plant districts are particularly challenging due to the complexities of coordinating multiple plants, optimizing load distributions, and managing system downtime. In response, new operational strategies are developed to enhance the efficiency and sustainability of steam districts while utilizing existing resources. These strategies include reducing plant operational pressure without compromising the reliable supply to buildings and optimizing load allocation across multiple plants. The load allocation considers boiler part-load efficiency, runtime, network losses, and building pressure set points, and is compared with traditional multi-boiler controls. To support this exploration, new dynamic Modelica models are developed. In addition, methods to reduce modeling complexities are incorporated, enhancing their suitability for practical applications. A holistic district-wide analysis using a real university case study demonstrates a 4.7% fuel savings by lowering boiler operational pressure from 900 kPa to 600 kPa, along with a 13.3% reduction in condensation losses across the distribution network. Furthermore, the load allocation approach results in a 13.1% reduction in fuel consumption during peak winter periods and 15.3% during shoulder periods, with corresponding decreases in carbon emissions and fuel costs. This approach can also save maintenance costs by reducing the boiler runtime by 49.6%. In conclusion, this research underscores the benefits of retrofitting aging steam district heating systems, offering immediate operational improvements by enhancing efficiency, meeting regulatory compliance, and extending infrastructure lifespans while delaying costly overhauls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Optimizing mixed cool thermal storage systems across a connected community

A high level of electric demand flexibility must be integrated into our building infrastructure to enable greater renewable energy penetration in the grid. In the U.S., 9% of electricity generated is used to cool buildings in a periodic manner, making this end-use an ideal target for active management through cool thermal energy storage (CTES) technologies. Historic uses for CTES are designed around central chilled water plants, but these systems cool less than 25% of U.S. commercial floorspace. Emerging technologies are under development to serve the many smaller distributed cooling systems, such as rooftop units (RTUs), and have the potential to add CTES to an additional 66% of cooled commercial floorspace. However, these unitary thermal storage systems (UTSS) lack the modeling and analysis tools to evaluate them in the future interactive grid context. Here, this study develops the modeling and optimization tools necessary to simultaneously examine central and distributed ice storage systems within the multi-building, connected community context. An integrated simulation-optimization workflow is created to allow for rapid customized analysis. Results demonstrate the energy and flexibility tradeoffs of various implementations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Neural message-passing for objective-based uncertainty quantification and optimal experimental design

Various real-world scientific applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufficient and the cost of acquiring additional data may be high. In such cases, based on a Bayesian paradigm, we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantification (objective-UQ) based on MOCU (mean objective cost of uncertainty) provides an effective means for quantifying uncertainty in complex systems, the high computational cost of estimating MOCU has been a challenge in applying it to real-world scientific/engineering problems. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to five orders of magnitude, without any visible performance loss compared to the state-of-the-art. The proposed approach applies to general OED tasks, beyond the Kuramoto model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Stochastic DC optimal power flow with reserve saturation

We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, but introduces a model of saturation of generator reserves by assuming that when a generator’s target level hits its limit, it abandons the affine policy and produces at that limit. This is a particularly interesting feature in models where wind power plants, which have uncertain upper generation limits, are scheduled to provide reserves to balance load fluctuations. Here, the resulting model is a nonsmooth nonconvex two-stage stochastic program, and we use a stochastic approximation method to find stationary solutions to a smooth approximation. Computational results on 6-bus and 118-bus test instances demonstrate that by considering the effects of saturation, our model can yield solutions with lower expected generation costs (at the same target line violation probability level) than those obtained from a model that enforces the affine policy to stay within generator limits with high probability.

17 WIND ENERGY↗
DOE OSTI2020

Stochastic multi-period optimal dispatch of energy storage in unbalanced distribution feeders

This report presents a convex, multi-period, AC-feasible Optimal Power Flow (OPF) framework that robustly dispatches flexible demand-side resources in unbalanced distribution feeders against uncertainty in very-short timescale solar Photo-Voltaic (PV) forecasts. This is valuable for power systems with significant behind-the-meter solar PV generation as their operation is affected by uncertainty from forecasts of demand and solar PV generation. The aim of this work is then to ensure the feasibility and reliability of distribution system operation under high solar PV penetration. We develop and present a novel, robust OPF formulation that accounts for both the nonlinear power flow constraints and the uncertainty in forecasts. This is achieved by linearizing an optimal trajectory and using first-order methods to systematically tighten voltage bounds. Case studies on a realistic distribution feeder shows the effectiveness of a receding-horizon implementation.

25 ENERGY STORAGE↗