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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Black-Box Optimization for Design of Concentrating Solar Power and Photovoltaic Hybrid Systems with Optimal Dispatch Decisions

The hybridization of concentrating solar power (CSP) and photovoltaics (PV) can enable dispatchable renewable electricity generation at a lower price than current stand-alone CSP systems. However, designing a CSP-PV hybrid system can be challenging because of the many degrees of freedom in design that affect the internal and external system interactions and trade-offs. We develop a methodology to determine optimal designs for CSP-PV hybrids by implementing NLopt's derivative-free, or “black-box,” algorithms around pre-existing CSP-PV hybrid simulation software that utilizes the National Renewable Energy Laboratory’s System Advisor Model (SAM); we then employ a dispatch optimization model to determine operational decisions that maximize a plant’s profits. We present optimal designs for CSP-PV hybrid systems dispatching against four time-of-delivery (ToD) pricing structures. NLopt’s algorithms can improve the base case design’s power purchase agreement (PPA) price by 15% to 21%, depending on the ToD pricing structure. In addition, we present the resulting optimal CSP-PV hybrid design’s annual performance metrics, which tend to have capacity factors between 50% and 62%, but are able to generate electricity during the year’s highest-valued periods about 90% of the time. Lastly, we investigate the trade-offs between capacity factor and PPA price using Pareto fronts and demonstrate that, for some ToD pricing structures, the system capacity factor can increase by 20% but at the expense of a 2% increase in PPA price.

black-box↗
DOE OSTI2019

Design Studies for the Optimization of 238 Pu Production in NpO 2 Targets Irradiated at the High Flux Isotope Reactor

Efforts to reestablish a domestic 238 Pu production capability in support of National Aeronautics and Space Administration mission objectives are ongoing throughout the U.S. Department of Energy complex. The Plutonium-238 Supply Project (PSP) was initiated in response to a report published by the National Research Council in 2011 stating that “without a restart of 238 Pu production, it will be impossible for the United States, or any other country, to conduct certain important types of planetary missions after this decade.” The PSP is targeting a sustained, constant production rate of 1.5 kg/year of heat source PuO 2 for several years. Design and optimization studies of 237 Np-bearing targets are underway at Oak Ridge National Laboratory (ORNL). It is anticipated that targets will be irradiated in ORNL’s High Flux Isotope Reactor (HFIR) and in the Advanced Test Reactor (ATR) at Idaho National Laboratory. A variety of target materials, containments, arrangements, and irradiation histories have been analyzed, and the results indicate that a sufficient quantity of 238 Pu can be produced in HFIR and ATR to fulfill the PSP’s constant production rate target. This paper focuses on the design and optimization of new target configurations containing pellets that are (1) ~93% of the theoretical density of NpO 2 , (2) loaded into pins of cladding materials that can be handled as solid waste following postirradiation 238 Pu recovery operations, (3) irradiated in various vertical experiment facility (VXF) locations in the HFIR permanent beryllium reflector, and (4) rotated within and/or moved to another VXF location following each HFIR operational cycle to maximize 238Pu production and minimize peak heat generation rates.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Optimal Protocols in Quantum Annealing and Quantum Approximate Optimization Algorithm Problems

Quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA) are two special cases of the following control problem: apply a combination of two Hamiltonians to minimize the energy of a quantum state. Which is more effective has remained unclear. Here we analytically apply the framework of optimal control theory to show that generically, given a fixed amount of time, the optimal procedure has the pulsed (or “bang-bang”) structure of QAOA at the beginning and end but can have a smooth annealing structure in between. This is in contrast to previous works which have suggested that bang-bang (i.e., QAOA) protocols are ideal. To support this theoretical work, we carry out simulations of various transverse field Ising models, demonstrating that bang-anneal-bang protocols are more common. Futher, the general features identified here provide guideposts for the nascent experimental implementations of quantum optimization algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Data-Driven Modeling and Optimization of Building Energy Consumption: a Case Study

Installing sensors and Building Automation Systems (BAS) allows controlling the facility operations while generating data that can be analyzed for model development. This work focuses on data-driven modeling of the building to optimize energy consumption. The City of Orlando aims to reduce its energy consumption so, they provided us access to their BAS for data and studying the operation of its facilities. We selected a mid-size pilot building to conduct data analysis and modeling. We develop an Application Programming Interface (API) to login to the servers and scrape data. The scraped data contains features ranging from environmental conditions to equipment activity. This dataset is a time series so, it's handled in accordance and analyzed to investigate patterns and relations between data points that help choose parameters for predictive models for building and equipment. Finally, the models are optimized to reduce the energy consumption of the facility.

Grover, Divas↗
DOE OSTI2019

Optimal Voltage Reference for Droop-Based DERs in Distribution Systems

Due to the newly issued standards on distributed energy resources (DERs), the droop-based operation modes (Q-V and P-V droops) become a requirement for all the interconnected DER units at the distribution level. Voltage references of the DER units can be assigned to improve the operational behavior of the distribution system in the smart grid context. This paper presents a novel formulation for optimally setting voltage reference of the droop-based DER units in distribution systems with the objective of minimizing the system ohmic loss. This problem is formulated as a mixed-integer quadratic convex optimization problem in MATLAB and solved by the MATLAB and CPLEX solvers. The IEEE 123-bus system and the EPRI test system Ckt5 are used to validate the proposed formulation. The result obtained from the proposed formulation is compared with the result of the original problem solved by the heuristic method. Based on the numerical study results, system loss can be reduced after the proposed voltage reference assignment. The computational expense of the new formulation is also much lower than that of the heuristic approach.

distributed energy resource integration↗
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 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 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↗
NASA NTRS1974

System design optimization for a Mars-roving vehicle and perturbed-optimal solutions in nonlinear programming

Work in two somewhat distinct areas is presented. First, the optimal system design problem for a Mars-roving vehicle is attacked by creating static system models and a system evaluation function and optimizing via nonlinear programming techniques. The second area concerns the problem of perturbed-optimal solutions. Given an initial perturbation in an element of the solution to a nonlinear programming problem, a linear method is determined to approximate the optimal readjustments of the other elements of the solution. Then, the sensitivity of the Mars rover designs is described by application of this method.

Pavarini, C.↗
NASA NTRS1984

Aircraft configuration optimization including optimized flight profiles

The Flight Optimization System (FLOPS) is an aircraft configuration optimization program developed for use in conceptual design of new aircraft and in the assessment of the impact of advanced technology. The modular makeup of the program is illustrated. It contains modules for preliminary weights estimation, preliminary aerodynamics, detailed mission performance, takeoff and landing, and execution control. An optimization module is used to drive the overall design and in defining optimized profiles in the mission performance. Propulsion data, usually received from engine manufacturers, are used in both the mission performance and the takeoff and landing analyses. Although executed as a single in-core program, the modules are stored separately so that the user may select the appropriate modules (e.g., fighter weights versus transport weights) or leave out modules that are not needed.

Mccullers, L. A.↗
NASA NTRS1989

Structural optimization of framed structures using generalized optimality criteria

The application of a generalized optimality criteria to framed structures is presented. The optimality conditions, Lagrangian multipliers, resizing algorithm, and scaling procedures are all represented as a function of the objective and constraint functions along with their respective gradients. The optimization of two plane frames under multiple loading conditions subject to stress, displacement, generalized stiffness, and side constraints is presented. These results are compared to those found by optimizing the frames using a nonlinear mathematical programming technique.

Kolonay, R. M.↗