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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Development and Validation of a SiC Based 50 kW Grid-Connected PV Inverter: Preprint

In this digest a 50kW string PV inverter formed using all SiC semiconductor devices is presented. The inverter design includes additively manufactured power block, symmetrical Y-core inductors for ac side filter, and advanced inverter functionality for grid support. This inverter utilizes the conventional three-phase Voltage Source Inverter topology and optimizes the design for SiC based devices. This paper presents the full functional validation of the designed inverter along with quantification of the efficiency and power density. The performance of the inverter for high dc-bus voltage without multi-level topology to exploit SiC advantages is also presented in this digest. The full paper will include details of inverter design, optimization and further experimental validation.

14 SOLAR ENERGY↗
DOE OSTI2024

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗
DOE OSTI2024

Practical and Optimal Sequential Bayesian Experimental Design for Complex Systems Incorporating Human Experimenter Preferences (Final Scientific/Technical Report)

Experiments are indispensable for developing models of complex systems. Carefully designed experiments can provide substantial savings for these expensive data-acquisition opportunities. However, designs based on heuristics are often suboptimal for systems with multiphysics, nonlinear dynamics, and uncertain and noisy environments. Optimal experimental design, while leveraging predictive models, seeks to systematically quantify and maximize the value of experiments. In this project, we focused on the design of multiple experiments, where current approaches are largely suboptimal: batch-design does not adapt to new data acquired during the experiment campaign (no feedback), and greedy/myopic design ignores future dynamics and consequences (no lookahead). We developed the mathematical framework and computational methods for sequential optimal experimental design (sOED) for complex systems. We enabled tractable model-based sOED in a rigorous manner through novel algorithms based on reinforcement learning, and investigated the effects of human experimenters on the design process. Our methods are fully Bayesian, able to quantify and update uncertainty in a principled manner. The traits aimed by our approach—mathematical rigor and optimality, human effects and uncertainty quantification, computational practicality—are crucial for elevating the standards of artificial intelligence (AI) to support decision-making in scientific domains, and contribute toward trust and realistic adoption of AI in experimental design practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Applicability study of Bayesian optimization in core neutronic design using a toy model

At the Japan Atomic Energy Agency (JAEA), an innovative design approach named ARKADIA (Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant life cycle) for advanced nuclear reactors is currently under development. One task in ARKADIA is to build a system that automatically optimizes core and fuel designs by conducting core neutronic and thermal-hydraulic calculations, fuel integrity evaluations, and plant dynamic analyses. This system will be implemented to automatically find an optimal design that minimizes (or maximizes) objective function defined by core performance while varying the core and fuel design parameters such as fuel pin diameter, core height and diameter. In this study, as the first step of system development, we focused only on core neutronic design and conducted a study of automatic optimization. As the optimization algorithm, Bayesian optimization (BO), an effective method for optimization problems with expensive computational cost of objective function, was utilized. The applicability of BO was studied based on single- and two-objective optimization examples of core neutronic design in a toy model. As a result, in the former, it was shown that BO can give the optimal solution, which matches the reference solution calculated by a brute force calculation well, with a small number of required calculations. Usability on core neutronic designs, where the computational cost per case is high, was confirmed. In the latter, it was found that BO can give a Pareto solutions-set that shows good agreement with the reference solution. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Molecular Level Design Principle behind Optimal Sizes of Photosynthetic LH2 Complex: Taming Disorder through Cooperation of Hydrogen Bonding and Quantum Delocalization

Here we discuss the light harvesting 2 (LH2) antenna complex from purple photosynthetic bacteria is an efficient natural excitation energy carrier with well-known symmetric structure, but the molecular level design principle governing its structure-function relationship is unknown. Our all-atomistic simulations of nonnatural analogues of LH2 as well as those of a natural LH2 suggest that nonnatural sizes of LH2-like complexes could be built. However, stable and consistent hydrogen bonding (HB) between bacteriochlorophyll and the protein is shown to be possible only near naturally occurring sizes, leading to significantly smaller disorder than for nonnatural ones. Extensive quantum calculations of intercomplex exciton transfer dynamics, sampled for a large set of disorder, reveal that taming the negative effect of disorder through a reliable HB as well as quantum delocalization of the exciton is a critical mechanism that makes LH2 highly functional, which also explains why the natural sizes of LH2 are indeed optimal.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Hydrogen Selective Exfoliated Zeolite Membranes

The objective of this project was to develop and evaluate an innovative membrane technology at process conditions that would be representative of Integrated Gasification Combined Cycle (IGCC) advanced power generation with pre-combustion capture of carbon dioxide (CO 2 ). This research focused on hydrogen (H 2 )-selective zeolite membranes that could be utilized to separate conditioned syngas into H 2 -rich and CO 2 -rich components. Both experiments and process design and optimization calculations were performed to evaluate the concept of ultra-thin membranes made from zeolites nanosheets. In this work, efforts in the laboratory were made to tackle two fundamental challenges in application of zeolite membranes in harsh industrial environments, namely, membrane thickness and membrane stability. Conventional zeolite membranes have thicknesses in the micron range, limiting their performance. In this research, we developed a method for fabrication of ultimately thin zeolite membranes based on zeolite nanosheets. A range of layered zeolites (MWW, RWR, NSI structure types) suitable for hydrogen separation was successfully exfoliated to their constituent nanosheets. Further, membranes were made from one of these zeolites, MWW, to demonstrate the potential of this group of materials. Moreover, long-term steam stability of these zeolites (up to 6 months) was investigated in high concentrations of steam (35 mol% and 95 mole%), high pressure (10 barg), and high temperatures (350 °C and 600 °C) relevant to conditions of water-gas-shift and steam methane reforming reactions. It was found that certain nanosheets are stable, and that stability depends on the concentration of structural defects. Additionally, models that represent a water-gas-shift (WGS) membrane reactor equipped with the zeolite membrane were developed for systems studies. These studies had the aim of analyzing the effect of the membrane reactor integration into IGCC plants in terms of performance and economic aspects of the plants. Specifically, simulation and design optimization studies were performed using the developed stand-alone membrane reactor models to identify the membrane selectivity and permeance characteristics necessary to achieve desired targets of CO 2 capture and H 2 recovery, as well as guide the selection of the optimal reactor design that minimizes the membrane cost as a function of its surface area required. The isothermal membrane reactor model was also integrated into IGCC system models using both the MATLAB and Aspen software platforms and techno-economic analyses of the integrated plants have been carried out to evaluate the feasibility of replacing current technologies for pre-combustion capture by the proposed novel approach in terms of satisfying stream constraints and achieving the DOE target goal of 90% CO 2 capture. The results of the performed analyses based on present value of annuity calculations showed break even costs for the membrane reactor within the feasible range for membrane fabrication. However, the predicted membrane performance used in these simulations exceeded the performance achieved experimentally. Therefore, further work is required to improve membrane performance.

01 COAL, LIGNITE, AND PEAT↗
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 OSTI2016

Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers

Here, we present two Monte Carlo algorithms to find the Pareto front of the chemical space of a class of dielectric polymers that is most interesting with respect to optimizing both the bandgap and dielectric constant. Starting with a dataset generated from density functional theory calculations, we used machine learning to construct surrogate models for the bandgaps and dielectric constants of all physically meaningful 4-block polymers (that is, polymer systems with a 4-block repeat unit). We parameterized these machine learning models in such a way that the surrogates built for the 4-block polymers were readily extendable to polymers beyond a 4-block repeat unit. By using translational invariance, chemical intuition, and domain knowledge, we were able to enumerate all possible 4, 6, and 8 block polymers and benchmark our Monte Carlo sampling of the chemical space against the exact enumeration of the surrogate predictions. We obtained exact agreement for the fronts of 4-block polymers and at least a 90% agreement for those of 6 and 8-block polymers. We present fronts for 10-block polymer that are not possible to obtain by direct enumeration. We note that our Monte Carlo methods also return polymers close to the predicted front and a measure of the closeness. Both quantities are useful information for the design and discovery of new polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Design and reliability optimization of a MEMS micro-hotplate for combustion of gaseous fuel

This report will detail the process by which the silicon carbide (SiC) microhotplate devices, manufactured by GE, were imaged using IR microscopy equipment available at Sandia. The images taken were used as inputs to a finite element modeling (FEM) process using the ANSYS software package. The primary goal of this effort was to determine a method to measure the temperature of the microhotplate. Prior attempts to monitor the device's temperature by measuring its resistance had proven to be unreliable due to the nonlinearity of the doped SiC's resistance with temperature. As a result of this thermal modeling and IR imaging, a number of design recommendations were made to facilitate this temperature measurement. The lower heating value (LHV) of gaseous fuels can be measured with a catalyst-coated microhotplate calorimeter. GE created a silicon carbide (SiC) based microhotplate to address high-temperature survivability requirements for the application. The primary goal of this effort was to determine a method to measure the temperature of the microhotplate. Prior attempts to monitor the device's temperature by measuring its resistance had proven to be unreliable due to the non-linearity of the doped SiC's resistance with temperature. In this work, thermal modeling and IR imaging were utilized to determine the operation temperature as a function of parameters such as operation voltage and device sheet resistance. A number of design recommendations were made according to this work.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Effectively using multifidelity optimization for wind turbine design

Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems – with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization – the multifidelity method finds the optimal design using 38 %–58 % of the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

17 WIND ENERGY↗
DOE OSTI2023

Design and additive manufacturing of optimized electrodes for energy storage applications

Supercapacitors exhibit fast charging/discharging ability and have attracted considerable attention within the automotive, aerospace, and telecommunication industries. Porous carbons, prized for their high electrical conductivity and high surface area, have been attractive candidates for supercapacitor electrodes. Moving to thick electrodes is one strategy to further increase energy density due to a higher volume fraction of active material. However, thick electrodes suffer from sluggish charged species transport, which is why thin electrodes are currently favored. In this work, we investigate the use of computational optimization and additive manufacturing to design and fabricate thick porous electrodes with improved performance. Electrode performance was maximized by designing their morphologies via topology optimization and printing by projection micro stereolithography (PμSL) using commercial resin (PR48). The PR48 resin was then pyrolyzed (PR48-P) to create the final conductive electrode. The optimized PR48-P electrodes exhibited 99% improvement in capacitance compared to control electrodes printed with cubic lattice morphologies. To further improve performance, we formulated a resin combining graphene oxide (GO) and trimethylolpropane triacrylate (TMPTA). Electrodes printed with 3 wt% GO in TMPTA exhibited improved capacitance retention after pyrolysis compared to the PR48-P electrodes. Finally, this work demonstrates the benefits of using topology optimization to design electrodes and material development to improve functional properties of 3D printable electrodes.

25 ENERGY STORAGE↗
DOE OSTI2020

Consistent boundary conditions for PDE filter regularization in topology optimization

Abstract Design variables in density-based topology optimization are typically regularized using filtering techniques. In many cases, such as stress optimization, where details at the boundaries are crucially important, the filtering in the vicinity of the design domain boundary needs special attention. One well-known technique, often referred to as “padding,” is to extend the design domain with extra layers of elements to mitigate artificial boundary effects. We discuss an alternative to the padding procedure in the context of PDE filtering. To motivate this augmented PDE filter, we make use of the potential form of the PDE filter which allows us to add penalty terms with a clear physical interpretation. The major advantages of the proposed augmentation compared with the conventional padding is the simplicity of the implementation and the possibility to tune the boundary properties using a scalar parameter. Analytical results in 1D and numerical results in 2D and 3D confirm the suitability of this approach for large-scale topology optimization.

42 ENGINEERING↗
DOE OSTI2013

Optimization of the design and mode of operation of a QD laser for reducing the heat-to-bitrate ratio

Heat dissipation under the high-speed modulation of quantum dot edge-emitting lasers is considered. It is shown that, for a given laser diode, there is a bias current at which the heat-to-bitrate ratio is minimized. Moreover, there exists a certain optimal optical loss of the laser cavity at which the lowest heat-to-bitrate ratio is provided for any design of edge-emitting lasers that can be fabricated from an epitaxial structure. The heat-to-bitrate ratio and the corresponding bitrate are numerically calculated and analytical expressions are derived. It is demonstrated that the heat-to-bitrate ratio of quantum dot edge-emitting lasers can be less than 0.4 pJ/bit at a bitrate exceeding 10 Gbit/s.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Expert‐in‐the‐loop design of integral nuclear data experiments

Abstract Nuclear data are fundamental inputs to radiation transport codes used for reactor design and criticality safety. The design of experiments to reduce nuclear data uncertainty has been a challenge for many years, but advances in the sensitivity calculations of radiation transport codes within the last two decades have made optimal experimental design possible. The design of integral nuclear experiments poses numerous challenges not emphasized in classical optimal design, in particular, constrained design spaces (in both a statistical and engineering sense), severely under‐determined systems, and optimality uncertainty. We present a design pipeline to optimize critical experiments that uses constrained Bayesian optimization within an iterative expert‐in‐the‐loop framework. We show a successfully completed experiment campaign designed with this framework that involved two critical configurations and multiple measurements that targeted compensating errors in 239 Pu nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Experts’ Familiarity Versus Optimality of Visualization Design: How Familiarity Affects Perceived and Objective Task Performance

Experts, cutting across domains like biology, climate science, cyber, energy, etc., frequently use visualizations as the principal medium for making analytical judgments or for presenting the results of their analysis. However, scientists are often skeptical about adopting new visualization methods over familiar ones, although the latter might be perceptually sub-optimal. This is due to the use of the familiarity heuristic, where the perceived cognitive ease in processing familiar representations of information leads scientists to undermine the effect of visualization best practices. Recent studies have shown that this often results in a discrepancy between scientists’ perceived and actual performance quality. It has also been shown that in some cases, participatory design sessions and qualitative and quantitative user studies are able to mitigate the effects of such bias. In this paper, we discuss the potential causes and effects of familiarity related biases with examples from recent studies and reflect on the related research questions.

visualization, climate science, cognitive bias, fa↗
DOE OSTI2020

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials’ design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum–niobium–titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗