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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

OPTIMIZATION OF A NUCLEAR VESSEL OUTLET FOR INCIDENT MONITORING

Classical nuclear core fluidic design techniques require improvement to better align with modern technological innovations. The US Department of Energy’s Office of Nuclear Energy (DOE-NE) Transformational Challenge Reactor (TCR) program is deploying additive manufacturing and advanced modeling and simulation to reimagine these designs. With the aid of modern computing power, computerized design optimization can be implemented to remove unwanted pressure drop while simultaneously optimizing flow structures, resulting in new opportunities to enable advanced instrumentation and monitoring capabilities.Previous development of geometric specifications for the TCR pressure vessel’s outlet plenum used design optimization to (1) limit pressure losses below 3.5 kPa (~0.5 psi) and (2) create a fluidic plane in which the temperature variation would not exceed ±5°C. This significant limit of the allowable pressure drop stems from the overarching goal of the TCR program to apply cutting edge techniques and unconventional thinking to demonstrate potential opportunities in additive manufacturing (AM).This paper expands the previous work by optimizing thermowell locations for robust measurements by explicitly modeling them and the resulting flow impacts. Additionally, a single core coolant channel was chosen to represent an event that causes an increased bulk flow temperature increase of 100°C.High fidelity unsteady Reynolds-averaged Navier-Stokes (URANS) simulations of the conjugate heat transfer problem were run in Siemen’s Star-CCM+ for this study. Next, the bulk flow temperature of a single coolant channel was increased by 100°C and was allowed to converge again. Finally, statistical analysis using a sequential probability ratio test (SPRT) was used to determine the elapsed time the thermocouples took to discover the increased bulk flow temperature.

See, Nate↗
DOE OSTI2017

Optimizing Biorefinery Design and Operations via Linear Programming Models

The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for maximizing the potential benefits of biomass utilization for production of fuels, chemicals and power.

09 BIOMASS FUELS↗
DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Application of Box-Behnken design in optimizing product properties of supercritical methanol co -liquefaction of rice straw and linear low-density polyethylene

We report co-processing of plastic and biomass wastes to produce high-quality fuel has been attracting considerable interest in energy recovery. This work employed Box-Behnken Design (BBD) to optimize yield and properties of oil from supercritical methanol (scMeOH) co-liquefaction of rice straw (RS) and linear low-density polyethylene (LLDPE). Three independent variables of reaction temperature (270, 300, and 330°C), holding time (60, 90, and 120 mins), and RS/LLDPE mass ratio (0.15, 0.50, and 0.85) were selected for experiments. A maximum oil yield of 34.17 wt% was achieved at 303°C, 96 mins, and RS/LLDPE ratio of 0.59 according to BBD. Temperature and RS/LLDPE ratio dominated the oil yield and synergistic effects during co-liquefaction, whereas time was an insignificant factor. Optimum oil from co-liquefaction contained substantial hydrocarbons content of 32.91% compared to that from single RS-derived oil of 7.77%. Solid product obtained at the optimum condition could be used as solid fuel, due to its relatively high HHV of 43.08 MJ/kg compared to the other solid fuels (13.90–32.14 MJ/kg). This work provides a deep understanding of the co-processing of solid biomass and plastic wastes.

09 BIOMASS FUELS↗
DOE OSTI2014

TH-E-BRF-01: Exploiting Tumor Shrinkage in Split-Course Radiotherapy

Purpose: In split-course radiotherapy, a patient is treated in several stages separated by weeks or months. This regimen has been motivated by radiobiological considerations. However, using modern image-guidance, it also provides an approach to reduce normal tissue dose by exploiting tumor shrinkage. In this work, we consider the optimal design of split-course treatments, motivated by the clinical management of large liver tumors for which normal liver dose constraints prohibit the administration of an ablative radiation dose in a single treatment. Methods: We introduce a dynamic tumor model that incorporates three factors: radiation induced cell kill, tumor shrinkage, and tumor cell repopulation. The design of splitcourse radiotherapy is formulated as a mathematical optimization problem in which the total dose to the liver is minimized, subject to delivering the prescribed dose to the tumor. Based on the model, we gain insight into the optimal administration of radiation over time, i.e. the optimal treatment gaps and dose levels. Results: We analyze treatments consisting of two stages in detail. The analysis confirms the intuition that the second stage should be delivered just before the tumor size reaches a minimum and repopulation overcompensates shrinking. Furthermore, it was found that, for a large range of model parameters, approximately one third of the dose should be delivered in the first stage. The projected benefit of split-course treatments in terms of liver sparing depends on model assumptions. However, the model predicts large liver dose reductions by more than a factor of two for plausible model parameters. Conclusion: The analysis of the tumor model suggests that substantial reduction in normal tissue dose can be achieved by exploiting tumor shrinkage via an optimal design of multi-stage treatments. This suggests taking a fresh look at split-course radiotherapy for selected disease sites where substantial tumor regression translates into reduced target volumes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Annual report for DOE VTO

Carbon fiber (CF)/polymer composites are a transformative class of high-performance, lightweight material, where high aspect-ratio CFs reinforce a polymer matrix and exceed the strength of steel alloys at a fraction of the density. Despite the advantages of such a class of material, the broader implementation of CF composites in a range of automotive, aerospace, and energy applications is hindered by limitations of current manufacturing methods. These current techniques (e.g., hand lay-up, wet filament winding) are costly and impose severe limitations on fiber placement, orientation, and angle, and thus a composite’s ultimate properties. Today’s CF composites are expensive to manufacture, limited in form factor, and utilize costly and sub-optimal continuous filament CF. Advanced additive manufacturing (AM) processes, combined with computational design optimization and new approaches to resin development, offer alternative design and manufacturing paradigms that have the realistic potential to lift these constraints. Such integrated AM approaches could thus help to realize the full potential of CF composite materials. One relevant application of CF composite materials where manufacturing constraints limit the cost-benefit ratio is in the manufacture of high-performance composite pressure vessels for onboard compressed natural gas (CNG) storage. Current CNG storage vessels (Types 3–5) are made from load-bearing filament-wound carbon-fiber composite and are ~3.5 times as expensive as an all-metallic Type-1 vessel. This cost is invariably tied to the complex and labor-intensive nature of conventional filament winding processes and the large volumes of expensive high tensile-strength CF tow feedstock required in manufacture. Our proposed approach to CNG storage vessel manufacture is based on a combination of AM technologies for CF composite printing and design optimization tools that were pioneered at Lawrence Livermore National Laboratory (LLNL), with advances in resin/composite formulation enabled by chemical and nano-material modification. Through the successful development of this technology, LLNL seeks to demonstrate the capability for advanced CNG storage vessel manufacture at reduced cost with no reduction in performance versus the most advanced, extant Type-5 designs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A General Framework for Process Synthesis, Integration, and Intensification

Process synthesis, integration, and intensification are the three pillars of process design. Current synthesis and integration methods are able to find optimal design targets and process configurations when all the alternatives are known beforehand. Process intensification, on the other hand, combines multiple physicochemical phenomena and exploits their interactions to create innovative designs. Often times, these designs are not known beforehand, and a phenomena-level representation of chemical processes are required to identify them. This disconnection between the three paradigms limits the ability to systematically discover optimal design pathways. We demonstrate that the building block representation, originally proposed in our earlier work on process intensification, has the potential to bridge this gap. Depending on the attributes assigned to the interior and the boundaries of these two-dimensional abstract building blocks, they can represent various intensified or isolated phenomena at the lowest level, various tasks at the equipment level, and various unit operations at the flowsheet level. Here, this common multiscale representation enables an mixed-integer nonlinear optimization-based single framework for the sequential or simultaneous synthesis, integration, and intensification of chemical processes. Such a general framework is critical to reduce the risk of eliminating potential intensification pathways and candidate flowsheets at the conceptual design stage. The framework is demonstrated using a case study on an ethylene glycol process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Concentric Ring Gas Atomization Die Design for Optimized Particle Production

In partnership with Linde plc (formerly Praxair, Inc.), Ames Laboratory will further develop and commercialize its concentric-ring high pressure gas atomization (CR-HPGA) gas-die technology with a goal to improve the precision of metal powder production in a desired size range and quality. The successful commercialization of this technology could reduce production cost of metal powders and improve reliability of this industrial process, increasing specialty alloy powder availability for new applications and adoption into additive manufacturing and multiple materials sectors. The project scope is to [1] utilize AMES compressible gas flow and melt break-up models to explore a wide swath of parameter space available for the CR-HPGA technology, identifying the most promising gas-die designs for fabrication, [2] verify and optimize the new gas-die design, fabricate it and select preferred operating parameters by gas-only flow imaging and aspiration pressure measurements, down-selecting gas compositions and atomization parameters for full scale testing, [3] perform pilot-scale atomization trials with Al and/or Cu alloys of a new CR-HPGA gas-die at selected parameters, [4] evaluate gas-die performance by comparing resulting powder size distribution and powder quality attributes with equivalent powders made by conventional close-coupled HPGA technology, and [5] assess (through Praxair partnership) reduction in operational costs of new CR-HPGA gas-die through the use of an inert gas recovery/recycling system. In partnership with Praxair, Inc., the team, led by Ames Laboratory senior metallurgist Iver Anderson, intend to demonstrate the performance of an optimized CR-HPGA gas-die design in a pilot scale atomizer and better understand the design and operational controls to increase overall benefits for precision metal powder production.

42 ENGINEERING↗
DOE OSTI2017

A FODO racetrack ring for nuSTORM: design and optimization

Here, the goal of nuSTORM is to provide well-defined neutrino beams for precise measurements of neutrino cross-sections and oscillations. The nuSTORM decay ring is a compact racetrack storage ring with a circumference of ~ 480 m that incorporates large aperture (60 cm diameter) magnets. There are many challenges in the design. In order to incorporate the Orbit Combination section (OCS), used for injecting the pion beam into the ring, a dispersion suppressor is needed adjacent to the OCS . Concurrently, in order to maximize the number of useful muon decays, strong bending dipoles are needed in the arcs to minimize the arc length. These dipoles create strong chromatic effects, which need to be corrected by nonlinear sextupole elements in the ring. In this paper, a FODO racetrack ring design and its optimization using sextupolar fields via both a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm will be discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Solvent Entropy Contributions to Catalytic Activity in Designed and Optimized Kemp Eliminases

This work reports on the role of solvation for enzymatic catalysis in two distinct, artificially designed Kemp Eliminases, KE07 and KE70, and mutated variants that were optimized by laboratory directed evolution. Using a spatially resolved analysis of hydration patterns, intermolecular vibrations, and local solvent entropies, we identify distinct classes of hydration water and follow their changes upon substrate binding and transition state formation for the designed KE07 and KE70 enzymes and their evolved variants. We observe that differences in hydration of the enzymatic systems are concentrated in the active site and undergo significant changes during substrate recruitment. For KE07, directed evolution reduces variations in the hydration of the polar catalytic center upon substrate binding, preserving strong protein-water interactions, while the evolved enzyme variant of KE70 features a more hydrophobic reaction center for which the expulsion of low-entropy water molecules upon substrate binding is substantially enhanced. While our analysis suggests a system-dependent role of solvation for the substrate binding process, we identify more subtle changes in solvation for the transition state formation, which are less affected by directed evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

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

Design and optimization of supersonic turbines for detonation combustors

Detonation-based engines offer a potential surge in efficiency for compact thermal power systems. However, these cycles require ad-hoc components adapted to the high outlet velocity from the detonation combustors. This paper presents the design methodology of turbine stages suitable for supersonic inlet conditions and provides a detailed analysis of optimized turbine geometries. A reduced-order solver examines the supersonic blade rows’ functional design space, quantifies the turbine’s non-isentropic performance, and budgets the turbine loss for different optimized leading-edge designs and chord to pitch ratios. The shock-wave interactions were identified as the predominant contributor to turbine losses, and optimal pitch-chord ratios were determined for various inlet Mach numbers. Finally, with this tool, the specific-power output for a wide range of design configurations was computed; and the metal angle that ensures flow starting and maximizes power extraction was calculated. The detailed numerical study describes the flow interactions in a supersonic turbine and offers new correlations to guide the design of future supersonic turbines.

42 ENGINEERING↗
DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗
DOE OSTI2014

The design and optimization of two low frequency energy harvesters employing 3C-SiC/AlN/Mo composite layers

This paper presents the design and simulation of twocantilever-based energy harvesters that employs cubic silicon carbide on silicon (3C-SiC-on-Si) wafer as the base material and bottom electrode. Aluminum Nitride (AlN) is employed as the piezoelectric/middle layer due to its excellent material properties and high stability in varying temperature and harsh environment. Molybdenum (Mo) serves as the top layer/electrode. The thickness of the structural layers are optimized through MATLAB and also analyzed via Finite Element Analysis using Intellisuite. Two designs are proposed at low resonant frequency, one with conventional cantilever beam, the other being a T-shaped cantilever beam. Both structures are simulated and their performances are compared.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Selling Demand Response Using Options

Wholesale electricity markets in many jurisdictions use a two-settlement structure: a day-ahead market for bulk power transactions and a real-time market for fine-grain supply-demand balancing. This paper explores trading demand response assets within this two-settlement market structure. Here, we consider two approaches for trading demand response assets: (a) an intermediate spot market with contingent pricing, and (b) an over-the-counter options contract. In the first case, we characterize the competitive equilibrium of the spot market, and show that it is socially optimal. Economic orthodoxy advocates spot markets, but these require expensive infrastructure and regulatory blessing. In the second case, we characterize competitive equilibria and compare its efficiency with the idealized spot market. Options contract are private bilateral over-the-counter transactions and do not require regulatory approval. We show that the optimal social welfare is, in general, not supported. We then design optimal option prices that minimize the social welfare gap. This optimal design serves to approximate the ideal spot market for demand response using options with modest loss of efficiency. Our results are validated through numerical simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Models and Simulation Environment for the Design and Optimization of Hybrid Energy Systems

The energy landscape in the U.S. is changing with proposed federal regulations limiting carbon dioxide emissions and with government credits for 'clean' energy technologies. This shift in the market conditions, especially in deregulated markets, has already resulted in the closing of coal power plants. Previously these coal plants served to balance variation in load. The resulting loss of electricity production is being made up in part with increased renewable generation. This set of governmental policy changes potentially presents expanded opportunities for both renewable energy and nuclear energy to become significant energy sources in the future. Potential synergies exist that can reduce greenhouse gas (GHG) emissions and provide affordable, sustainable energy. Combining multiple energy production, conversion, storage, and consumption facilities can leverage these potential synergies. This research focuses on integrating nuclear power plants within systems with increased renewable penetration, referred to as a nuclear renewable hybrid energy system (NRHES). Industrial-scale nuclear renewable hybrid energy systems have the potential to provide grid services such as dispatchable generation, storage, ancillary services, and power regulation. The problem of designing, analyzing, and optimizing the NHRES is being undertaken by a multi-laboratory team. The effort requires the collaboration of experts across engineering disciplines with methods and results to be shared by these experts. To facilitate collaborations a common system modeling and analysis language is adopted along with open-source libraries and interface standards that support data exchange among the models. Modelica was selected as the common language based on its prior use by team members at each of Idaho National Laboratory, Oak Ridge National Laboratory, and Argonne National Laboratory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Preliminary design and optimization of a G-band extended interaction oscillator based on a pseudospark-sourced electron beam

The design and simulation of a G-band extended interaction oscillator (EIO) driven by a pseudospark-sourced electron beam is presented. The characteristic of the EIO and the pseudospark-based electron beam were studied to enhance the performance of the newly proposed device. The beam-wave interaction of the EIO can be optimized by choosing a suitable pseudospark discharging voltage and by widening the operating voltage region of the EIO circuit. Simulation results show that a peak power of over 240 W can be achieved at G-band using a pseudospark discharge voltage of 41 kV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗