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Results for “optimality”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Reman Co-Design: A Combined Design and Remanufacturing Optimization Framework for the Sustainable Design of High-Value Components

Remanufacturing is a process that returns end-of-life equipment to as-new conditions and offers numerous environmental and economic benefits. To fully capitalize on remanufacturing, its synergistic interactions with design must be fully realized and addressed during the design stage. Although this fact is widely recognized in the literature, most of the current studies focus primarily either on the design or remanufacturing aspects of design for remanufacturing (DfRem). In an effort to offer a more integrated DfRem approach than those reported in the literature, we propose a new combined design and remanufacturing optimization (reman co-design) framework that takes a holistic approach by leveraging the intricate interplay between design and remanufacturing. Here, the aim of this formulation is to identify the optimal decisions that maximize the benefits of remanufacturing throughout the entire lifespan of a product. To showcase the utility of the new formulation, we are using a case study of a hydraulic manifold, (re)manufactured by John Deere. Using this industry example, we compare the results of reman co-design to the ones from a decoupled remanufacturing design approach. Results reveal that remanufacturing benefits are better realized and improved upon when using the developed reman co-design approach.

design for X↗
DOE OSTI2019

A Structured Quasi-Newton Algorithm for Optimizing with Incomplete Hessian Information

We present a structured quasi-Newton algorithm for unconstrained optimization problems that have unavailable second-order derivatives or Hessian terms. We provide a formal derivation of the well-known Broyden--Fletcher--Goldfarb--Shanno (BFGS) secant update formula that approximates only the missing Hessian terms, and we propose a linesearch quasi-Newton algorithm based on a modification of Wolfe conditions that converges to first-order optimality conditions. We also analyze the local convergence properties of the structured BFGS algorithm and show that it achieves superlinear convergence under the standard assumptions used by quasi-Newton methods using secant updates. In conclusion, we provide a thorough study of the practical performance of the algorithm on the CUTEr suite of test problems and show that our structured BFGS-based quasi-Newton algorithm outperforms the unstructured counterpart(s).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization

We develop a new mathematical framework to study the optimal design of air electrode microstructures for lithium-oxygen (Li-O2) batteries. It can eectively reduce the number of expensive experiments for testing dierent air-electrodes, thereby minimizing the cost in the design of Li-O2 batteries. The design parameters to characterize an air-electrode microstructure include the porosity, surface-to-volume ratio, and parameters associated with the pore-size distribution. A surrogate model (also known as response surface) for discharge capacity is rst constructed as a function of these design parameters. The surrogate model is accurate and easy to evaluate such that an optimization can be performed based on it. In particular, a Gaussian process regression method, co-kriging, is employed due to its accuracy and eciency in predicting high-dimensional responses from a combination of multidelity data. Specically, a small amount of data from high-delity simulations are combined with a large number of data obtained from computationally ecient low-delity simulations. The high-delity simulation is based on a multiscale modeling approach that couples the microscale (pore-scale) and macroscale (device-scale) models. Whereas, the low-delity simulation is based on an empirical macroscale model. The constructed response surface provides quantitative understanding and prediction about how air electrode microstructures aect the discharge performance of Li-O2 batteries. The succeeding sensitivity analysis via Sobol indices and optimization via genetic algorithm ultimately oer a reliable guidance on the optimal design of air electrode microstructures. The proposed mathematical framework can be generalized to investigate other new energy storage techniques and materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Optimization of Marine Energy Conversion Systems Through Modeling, Optimization, and CHIL Validation

The work aims to achieve optimal tidal energy conversion through a comprehensive approach of modeling, optimization, and control hardware-in-the-loop (CHIL) validation. By developing accurate models and employing optimization techniques, it seeks to identify efficient system configurations and control strategies. HIL validation will ensure the performance and reliability of the optimized tidal energy conversion system. The preparation of the present manual has been supported by the U.S. Department of Energy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

LINAC OPTICS OPTIMIZATION WITH MULTI-OBJECTIVE OPTIMIZATION

The beamline design of recirculating linacs requires special attention to avoid beam instabilities due to RF wakefields. A proposed high-energy, multi-pass energy recovery demonstration at CEBAF uses a low beam current. Stronger focusing at lower energies is necessary to avoid beam breakup(BBU) instabilities, even with this small beam current. The CEBAF linac optics optimization balances over-focusing at higher energies and beta excursions at lower energies. Using proper mathematical expressions, linac optics optimization can be achieved with evolutionary algorithms. Here, we present the optimization process of North Linac optics using multi-objective optimization.

Neththikumara, I.↗
DOE OSTI2025

Optimizing collimator positions using bayesian optimization in the Fermilab MI-8 transfer line

Collimators are used to minimize losses and to remove particles that would otherwise get lost downstream and irradiate the machine. Finding the optimal jaw positions is time consuming and with the upstream beam properties changing, the collimation settings would need to be readjusted each time. Therefore, a method to optimize collimator positions and to operate them at full capacity in a short time is required for loss control downstream. A study of collimator positions was conducted and a machine learning (ML) model was developed to predict optimal collimator positions. Bayesian Optimization (BO) was used to calculate new jaw positions from the ML model. The results of BO and usage of ML for better performance of the collimation system are presented in this paper.

Babacan, Betiay [Fermilab]↗
DOE OSTI2026

Online multi-objective Bayesian optimization of injection efficiency and beam lifetime with skew quadrupoles at NSLS-II

At NSLS-II, the vertical emittance of electron beam is typically blown up to ~30 pm with a coupling wave to increase beam lifetime during user operation. As more and more insertion devices are added to the storage ring, injection efficiency to the ring drops noticeably in certain machine states, apparently due to degraded dynamic apertures. To help alleviate this issue, we have recently performed online multi-objective Bayesian optimization to increase injection efficiency while maintaining beam lifetime, by adjusting the strengths of 15 skew quadrupoles in non-dispersive sections. We report the results of this optimization effort.

Hidaka, Yoshiteru [Brookhaven]↗
DOE OSTI2016

RAPID OPTIMAL SPH PARTICLE DISTRIBUTIONS IN SPHERICAL GEOMETRIES FOR CREATING ASTROPHYSICAL INITIAL CONDITIONS

Creating spherical initial conditions in smoothed particle hydrodynamics simulations that are spherically conformal is a difficult task. Here, we describe two algorithmic methods for evenly distributing points on surfaces that when paired can be used to build three-dimensional spherical objects with optimal equipartition of volume between particles, commensurate with an arbitrary radial density function. We demonstrate the efficacy of our method against stretched lattice arrangements on the metrics of hydrodynamic stability, spherical conformity, and the harmonic power distribution of gravitational settling oscillations. We further demonstrate how our method is highly optimized for simulating multi-material spheres, such as planets with core–mantle boundaries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling

We introduce a novel semi-supervised learning approach, named Teacher-Student Bayesian Optimization (TSBO ), integrating the teacher-student paradigm into BO to minimize expensive labeled data queries for the first time. TSBO incorporates a teacher model, an unlabeled data sampler, and a student model. The student is trained on unlabeled data locations generated by the sampler, with pseudo labels predicted by the teacher. The interplay between these three components implements a unique selective regularization to the teacher in the form of student feedback. This scheme enables the teacher to predict high-quality pseudo labels, enhancing the generalization of the GP surrogate model in the search space. To fully exploit TSBO , we propose two optimized unlabeled data samplers to construct effective student feedback that well aligns with the objective of Bayesian optimization. Furthermore, we quantify and leverage the uncertainty of the teacher-student model for the provision of reliable feedback to the teacher in the presence of risky pseudo-label predictions. TSBO demonstrates significantly improved sample-efficiency in several global optimization tasks under tight labeled data budgets. The implementation is available at https://github.com/reminiscenty/TSBO-Official.

Yin, Yuxuan↗
DOE OSTI2025

Optimal sizing of battery energy storage systems for peak shaving and demand response using a degradation-aware Bayesian Optimization-Mixed-Integer Linear Programming framework

The increasing integration of renewable energy and rising electricity demand highlight the importance of battery energy storage systems for peak shaving and demand response. Unlike prior approaches that overlook operational impacts on degradation, this study proposes a Bayesian Optimization–Mixed Integer Linear Programming framework for optimal battery energy storage system sizing. In this framework, Mixed Integer Linear Programming determines short-term scheduling while a calibrated electrochemical model iteratively evaluates degradation. The central hypothesis is that the framework can efficiently identify optimal sizes that yield realistic and economically robust outcomes. The method is tested across three scenarios: peak shaving, peak shaving with energy-reduction demand response, and peak shaving with power-reduction demand response. Results show that the framework converge to the optimum within 20 iterations out of 150 possible sizes. Under baseline conditions, the framework consistently selects the smallest feasible system, minimizing unnecessary degradation costs from oversized storage. Sensitivity analyses reveal that larger systems are favored as demand rates or incentives increase. Comparisons of demand response programs indicate that power-reduction demand response offers greater economic benefits than energy-reduction demand response, although demand savings from peak shaving remain the dominant contributor to overall performance. This study demonstrates that the proposed framework balances computational tractability with degradation fidelity, identifies critical economic thresholds for investment, and offers a practical, flexible tool to guide industrial stakeholders in cost-effective battery energy storage system deployment.

Batteries↗
DOE OSTI2013

2nd World Congress on Integrated Computational Materials Engineering, Part II: Process Optimization: Multi-Objective Optimization of Microstructure in Wrought Magnesium Alloys.

The microstructural features that govern the mechanical properties of wrought magnesium alloys include grain size, crystallographic texture, and twinning. Several processes based on shear deformation have been developed that promote grain refinement, weakening of the basal texture, as well as the shift of the peak intensity away from the center of the basal pole figure — features that promote room temperature ductility in Mg alloys. At ORNL, we are currently exploring the concept of introducing nano-twins within sub-micron grains as a possible mechanism for simultaneously improving strength and ductility by exploiting a potential dislocation glide along the twin-matrix interface — a mechanism that was originally proposed for face-centered cubic materials. Specifically, we have developed an integrated modeling and optimization framework in order to identify the combinations of grain size, texture and twin spacing that can maximize strength-ductility combinations. A micromechanical model that relates microstructure to material strength is coupled with a failure model that relates ductility to a critical shear strain and a critical hydrostatic stress. The micro-mechanical model is combined with an optimization tool based on genetic algorithm. A multi-objective optimization technique is used to explore the strength-ductility space in a systematic fashion and identify optimum combinations of the microstructural parameters that will simultaneously maximize the strength-ductility in the alloy.

crystal plasticity↗
DOE OSTI2018

A new hybrid algorithm combining a new chaos optimization approach with gradient descent for high dimensional optimization problems

Chaos optimization algorithm (COA) is a recently developed method for global optimization based on chaos theory. It has the features of easy implementation, short execution time and robust mechanisms of escaping from local optima compared with the existing stochastic searching algorithm. By integrating the gradient descent method with a new COA approach using new strategies including symmetrization and levelling, a novel hybrid method, called SCGD, is proposed in this paper. The main idea is to combine the good ability of exploitation of the gradient descent with the high ability of exploration of COA. The purpose is to avoid being trapped into local optima and to improve convergence in large space and high-dimension optimization problems. The simulation results based on a set of 50 benchmark functions with diverse properties and different dimensions show the efficiency and the abilities of the proposed method in finding the global optima compared with the existing methods.

97 MATHEMATICS AND COMPUTING↗