Optimized emission in nanorod arrays through quasi-aperiodic inverse design
Not Available
Anderson, P. Duke↗
SEARCH · Engineering Papers
Results for “Design optimization”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A fast reduced model for a shell-and-tube based latent heat thermal energy storage heat exchanger and its application for cost optimal design by nonlinear programming
Numerical simulation of latent heat thermal energy storage (LHTES) systems plays a fundamental role in studying the physical process and guiding the engineering design. Discretization of the PDEs describing the nonlinear solidification/melting process of phase change materials (PCMs) leads to a large-scale complex dynamics system, where the system behavior depends on a set of parameters. In a design setting, repeated model evaluations are required over the set of parameters results in significant computational burden. In this paper, an explicit analytic solution was built for the propagation of the solidification front in a cylindrical coordinate. The analytic solution approach is further employed to develop a low computational reduced model (RM) as a module for a shell-and-tube based LHTES heat exchanger. The levelized Cost of Energy (LCOE) is used as a design metric and the RM model is used to apply system-level constraints in the nonlinear programming formulation that facilitates efficient global optimal design of the PCM properties, flow conditions and tube geometries. The use of LCOE as the design metric prevents over design of the heat transfer rate and also establishes a fair ground for evaluation of different thermal storage technologies and their integrated applications with other systems. Optimal results showed that a higher effectiveness results in a higher LCOE; the velocity of the HTF and the length of the channel are highly correlated with each other; both larger PCM latent energy and conductivity result in lower LCOE.
25 ENERGY STORAGE↗
Swarm Intelligence Based Optimal Design of Local Volt/Var Control Function for Distributed Energy Resources
The increasing penetration of renewable based distributed energy resources (DERs) in distribution network (DN) leads to larger and more frequent voltage variation in distributions network (DN), thus posing challenges on voltage control. Real-time local voltage control method is a promising solution for the above issue. However, the local voltage control function needs to be customized and optimized according to real distribution system condition. In this paper, a swarm intelligence based Volt/Var control optimal design method (SO-VVC) is proposed to optimize the control function. Compared with existing approaches, the proposed method can not only represent the nonlinear behaviour of power flow but is also computation efficient. The performance of the proposed SO-VVC is demonstrated by case studies on a modified IEEE-123 bus system.
Zhang, Zhengfa [University of Tennessee, Knoxville↗
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization
In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.
Owoyele, Opeoluwa↗
Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments
Here, we present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems; (b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsity-enforcing penalty functions such as $\ell_0$, without needing to utilize a continuation procedure or apply a rounding technique; (c) exhibits much lower computational cost than traditional gradient-based relaxation approaches; and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.
97 MATHEMATICS AND COMPUTING↗
Multiphysics Co-Optimization Design and Analysis of Double-Side Cooled Silicon Carbide-Based Power Module: Preprint
With the rapid growth of Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs), much more rigorous design targets have been set for automotive power electronics, including high power density, high reliability, and low cost. Novel power module and inverter technologies based on wide bandgap (WEG) semiconductors have been developed to meet these design targets, while providing optimal power semiconductor operating temperature and promising thermomechanical performance. Compared with conventional cooling techniques which are normally applied only on one side of power module, double-side cooling approach is now believed to be the solution to enable high power density and low thermal resistance of WEG semiconductor-based power electronics. In this work, we develop a three-phase power module that is double-sided cooled using dielectric fluid jet impingement. In each phase, four silicon carbide (SiC) power semiconductors are bonded to copper busbars without electrical insulation layers. A finite element analysis (FEA) model is created for thermal and thermomechanical analysis. Based on FEA modeling results, we select particular dimensions for a parametric study to optimize thermal and mechanical performance. Using a multi-objective genetic algorithm (MOGA)-based optimization method, we have minimized the maximum junction temperature and thermal stresses within the power module. The multiphysics co-optimization approach has enabled an efficient design process of power modules with greatly reduced computational cost, as compared to conventional processes that rely on exhaustive numerical simulations and iterations.
ADVANCED PROPULSION SYSTEMS↗
Materials and Optimized Designs for Human-Machine Interfaces Via Epidermal Electronics
Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). Furthermore, the findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface.
36 MATERIALS SCIENCE↗
WE-AB-BRB-01: Development of a Probe-Format Graphite Calorimeter for Practical Clinical Dosimetry: Numerical Design Optimization, Prototyping, and Experimental Proof-Of-Concept
Purpose: In this work, the feasibility of performing absolute dose to water measurements using a constant temperature graphite probe calorimeter (GPC) in a clinical environment is established. Methods: A numerical design optimization study was conducted by simulating the heat transfer in the GPC resulting from irradiation using a finite element method software package. The choice of device shape, dimensions, and materials was made to minimize the heat loss in the sensitive volume of the GPC. The resulting design, which incorporates a novel aerogel-based thermal insulator, and 15 temperature sensitive resistors capable of both Joule heating and measuring temperature, was constructed in house. A software based process controller was developed to stabilize the temperatures of the GPC’s constituent graphite components to within a few 10’s of µK. This control system enables the GPC to operate in either the quasi-adiabatic or isothermal mode, two well-known, and independent calorimetry techniques. Absorbed dose to water measurements were made using these two methods under standard conditions in a 6 MV 1000 MU/min photon beam and subsequently compared against TG-51 derived values. Results: Compared to an expected dose to water of 76.9 cGy/100 MU, the average GPC-measured doses were 76.5 ± 0.5 and 76.9 ± 0.5 cGy/100 MU for the adiabatic and isothermal modes, respectively. The Monte Carlo calculated graphite to water dose conversion was 1.013, and the adiabatic heat loss correction was 1.003. With an overall uncertainty of about 1%, the most significant contributions were the specific heat capacity (type B, 0.8%) and the repeatability (type A, 0.6%). Conclusion: While the quasi-adiabatic mode of operation had been validated in previous work, this is the first time that the GPC has been successfully used isothermally. This proof-of-concept will serve as the basis for further study into the GPC’s application to small fields and MRI-linac dosimetry. This work has been supported in part by the CREATE Medical Physics Research Training Network of the Natural Sciences and Engineering Research Council (NSERC) grant 432290, NSERC grants RGPIN 298191 & 435608-13, Canadian Institutes of Health Research doctoral scholarship GSD-121793. This work has also been supported by Sun Nuclear Corporation.
60 APPLIED LIFE SCIENCES↗
A novel system for ammonia-based sustainable energy and agriculture: Concept and design optimization
Here this work presents a new vision for sustainable energy supply and agriculture: an ammonia-based sustainable energy agriculture (ABSEA) system. This intensified system uses renewables to produce ammonia both as fertilizer and as fuel for tractors and grain drying, to meet local electrical power demand and to provide predictable, consistent power export to the grid. The conceptual design of this system exploits synergies between ammonia production and energy supply to allow for economic viability, specifically time-varying chemical production as well as the potential of ammonia and hydrogen as energy storage media. A framework for combined optimization of the ABSEA design and its inherently time-varying operating schedule is proposed with a view on further improving economic competitiveness. A case study which considers the use of wind turbines to meet ammonia fertilizer and fuel demands of 40,300 kg/year and an annual hourly average power demand of 985 kW demonstrates the promise of the ABSEA system and the associated design-scheduling optimization framework. The annual net present cost is approximately $\$$75,000, which corresponds to an emissions reduction cost of $\$$17.60/tonCO2. The ABSEA optimal design uses both hydrogen and ammonia, but not a battery, for energy storage, illustrating their efficacy in this synergistic application.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Model based co-simulation platform for integrated building system control and design optimization
Both steady-state and dynamic simulations have been widely used by HVAC&R industry to support product/equipment development for decades. Steady-state simulation focuses on the system mass, energy and momentum balance of an equilibrium state. It is based on high-fidelity components models, and thus is suitable for system and component design optimization. Dynamic simulation studies the system transient response and is generally used for controls development and verification. It usually does not require rigorous component models of high accuracy because 1) the commonly used PID control is feedback control whose control performance evaluation doesn’t require high fidelity system/plant model; 2) high-fidelity dynamic model significantly increases the number of equations and variables and creates tremendous challenge for math solver. For supervisory control, transactive control or optimization of an integrated building system, the HVAC&R equipment is often one of the sub-components to be controlled. High-fidelity equipment models are required for accurately evaluating control strategies. In addition, building equipment manufacturers have developed a lot of high-fidelity steady-state equipment/component models per their expertise. Thus, a platform that can integrate OEM high-fidelity steady-state model with dynamic building simulation and/or electric power system & grid simulation to support the development and verification of supervisory control for integrated building systems is necessary. In this study, ORNL’s heat pump design tool (HPDM) is utilized to develop a co-simulation platform for supervisory control and optimization in integrated building systems. It is based on a model that integrates high-fidelity steady-state simulation equipment models with dynamic building simulation. A practical case of using the proposed co-simulation platform to develop and evaluate the supervisory control and optimization is presented and discussed.
Sun, Jian↗
HFIR LEU High Density Silicide Dispersion Optimized Design Neutronics Analyses with PHAME
A high-fidelity neutronics model of the Oak Ridge National Laboratory High Flux Isotope Reactor (HFIR) with the low-enriched uranium (LEU) high-density silicide dispersion Optimized fuel design was updated and analyzed to generate reactor physics-based metrics to support follow-on thermal hydraulic and transient analyses of this design. The Python HFIR Analysis and Measurement Engine (PHAME) was also updated to enhance the automation capabilities of the framework developed and maintained to perform these reactor physics modeling and simulation efforts. The automated framework significantly increases the efficiency and reproducibility to design and thoroughly analyzes HFIR LEU core designs, changes, and uncertainties. Reactor physics metrics evaluated include but are not limited to fuel depletion, cycle length, fission rate density distributions, axial power peaking factors, kinetics data, reactivity coefficients, control element worths, heat deposition rates, and decay heat. These neutronics results provide essential input to follow-on steady state thermal, thermal hydraulic and reactor transient analyses, which are subject of other reports. The Optimized design operates at 95 MW to maintain HFIR’s current highly enriched uranium core performance level at 85 MW.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Optimal Design of Ozone Bleaching Parameters to Approach Cellulose Nanofibers Extraction from Sugarcane Bagasse Fibers
Cellulose nanofibers (CNFs) were isolated from sugarcane bagasse (SCB) through the combination of bio-refinery, sulfur-free, and totally chlorine free (TCF) chemo-mechanical pretreatments, with a focus on the optimal design of ozone bleaching parameters based on a response surface methodology (RSM). For this purpose, the most effective parameters in ozone bleaching (temperature, time, and pulp consistency) were set between 40 and 85 °C, 60 and 360 min, and 1–5 wt%, respectively. High-performance liquid chromatography (HPLC), Fourier transform infrared spectroscopy (FTIR), Kappa number, and scanning electron microscopy (SEM) were used to chemically and morphologically characterize the SCB fibers. The size distribution and morphology of CNFs were also evaluated by dynamic light scattering (DLS) and transmission electron microscopy (TEM). HPLC analysis revealed that percentage of cellulose increased from 41.5 to 91.39% after chemical pretreatments. FTIR and Kappa number analyses also confirmed the successful isolation of cellulose fibers from the SCB fibers after chemical pretreatments. Furthermore, DLS results showed that the hydrodynamic diameter of the isolated cellulose fibers reduced to 268 nm by dint of ultrasonication. Additionally, TEM images confirmed the isolation of CNFs: the average diameter of cellulose fibers decreased to about 28 nm after mechanical steps and the yield of fibrillation was found to be around 99%. According to the obtained results, the applied chemo-mechanical treatment appears to be promising for green and facile isolation of CNFs.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Optimization design of helium circulator diffuser in HTR-PM - 18488
Circulator is very important in HTR-PM. It needs to supply enough pressure rise to circulate cooled helium in the primary loop of HTR-PM to bring heat out. Its pressure rise requirement is the highest among the worldwide circulators. Diffuser is one main component to produce pressure rise for circulator. And the most particular for HTR-PM is that the helium must flow out from two round tubes after flowing through the diffuser, which is very different from other circulators. This special export structure increases flow loss and decreases diffuser efficiency. So it is necessary to optimize diffuser to satisfy high pressure rise requirement of HTR-PM circulator with the special export structure. Four geometrical variables of diffuser have been studied and optimized. Based on the flow and performance analysis, the optimum structure was chosen. Then the detailed simulation was done by combining it with impeller to obtain performance characteristics of the whole circulator. Finally, the simulation results were compared with experimental results. It was obtained that simulation pressure rise was 201 kPa and experimental result was 210 kPa, which verified that optimization design of diffuser was effective. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Stochastic Design Optimization of Microstructural Features Using Linear Programming for Robust Design
Microstructure design can have a substantial effect on the performance of critical components in numerous aerospace applications. However, the stochastic nature of metallic microstructures leads to deviations in material properties from the design point and alters the performance of these critical components. In this paper, a novel stochastic linear programming (LP) methodology is developed for microstructure design accounting for uncertainties in desired properties. The metallic microstructure is represented using a finite element discretized form of the orientation distribution function (ODF). The inverse LP problem solves the mean values and covariance matrix of the ODFs to maximize the mean values of a property, given the statistical constraints on other properties. The highlight is an analytical uncertainty quantification model via a Gaussian distribution to model propagation of microstructural uncertainties to the properties. Examples illustrate maximization of the yield strength and magnetostriction of a galfenol alloy when constrained by uncertainties in a set of stiffness constants.
42 ENGINEERING↗
SU-F-T-178: Optimized Design of a Diamond Detector Specifically Dedicated to the Dose Distribution Measurements in Clinical Proton Pencil Beams
Purpose: In proton-therapy, pencil beam scanning (PBS) dosimetry presents a real challenge due to the small size of the beam (about 3 to 8 mm in FWHM), the pulsed high dose rate (up to 100 Gy/s) and the proton energy variation (about 30 MeV to 250 MeV). In the framework of French INSERM DEDIPRO project, a specifically dedicated single crystal diamond dosimeter (SCDDo) was developed with the objective of obtaining accurate measurements of the dose distribution in PBS modality. Methods: Monte Carlo simulations with MCNPX were performed. A small proton beam of 5 mm in FWHM was simulated as well as diamond devices with various size, thickness and holder composition. The calculated doses-to-diamond were compared with the doses-to-water in order to reduce the perturbation effects. Monte-Carlo simulations lead to an optimized SCDDo design for small proton beams dosimetry. Following the optimized design, SCDDos were mounted in water-equivalent holders with electrical connection adapted to standard electrometer. First, SCDDos performances (stability, repeatability, signal-to-background ratio…) were evaluated with conventional photon beams. Then, characterizations (dose linearity, dose rate dependence…) with wide proton beams were performed at proton-therapy center (IC-CPO) from Curie Institute (France) with the passive proton delivery technique, in order to confirm dosimetric requirements. Finally, depth-dose distributions were measured in a water tank, for native and modulated Bragg Peaks with the collimator of 12 cm, and compared to a commercial PPC05 parallel-plate ionization chamber reference detector. Lateral-dose profiles were also measured with the collimator of 5 mm, and compared to a commercial SFD diode. Results: The results show that SCDDo design does not disturb the dose distributions. Conclusion: The experimental dose distributions with the SCDDo are in good agreement with the commercial detectors and no energy dependence was observed with this device configuration.
07 ISOTOPE AND RADIATION SOURCES↗
CCM vs. CRM Design Optimization of a Boost-derived Parallel Active Power Decoupler for Microinverter Applications
Single-phase inverter or rectifier systems often make use of an auxiliary active power decoupler (APD) to balance the mismatch between steady DC power and fluctuating AC power. This paper deals with efficiency and size optimization of a parallel boost-type APD circuit for PV microinverter applications. Specifically, design of an eGaNFET-based, 400 W APD circuit, employing planar inductor and operating in either continuous conduction mode (CCM) or critical conduction mode (CRM) is considered. Available design variables including inductance value, inductor core geometry, capacitor voltage, switching frequency, and modulation scheme (CCM vs. CRM) are explored to identify Pareto-optimal configurations, which can achieve low California Energy Commission (CEC) efficiency drop while also reducing the footprint area of the inductor. The theoretical study predicts that the optimal CRM design can achieve 37% reduced inductor size, while operating with similar efficiency drop, compared to the optimal CCM design. Experimental results, obtained using two separate 40 V, 400 W hardware prototypes for CCM and CRM, are presented to verify the analyses.
14 SOLAR ENERGY↗
Pu(IV) quantification via visible–near-infrared absorption spectroscopy: tackling interferences using D-optimal design and partial least squares
Here, this study presents a novel analytical approach for quantifying Pu(IV) in glove box environments using fiber-optic-based visible–near-infrared absorption spectroscopy in combination with partial least squares regression (PLSR) and design of experiments. The method addresses significant challenges posed by overlapping spectral features arising from Nd(III), which is a common fission product impurity, and the speciation variability of Pu(IV) nitrato complexes in HNO 3 concentrations ranging from 2.5 to 11 M. A curated training set consisting of data from 20 samples was developed via D-optimal design to enable robust PLSR model calibration for Pu(IV) using the near-infrared band near 1050 nm. The training set was acquired from samples in cuvettes with a 1-cm path length and was used to build the PLSR model. The robustness of the model was validated with data collected using a dip probe with a 1-cm path length and varying Pu(IV) concentrations. The strong performance of the model indicates good model transfer from cuvette to dip probe and highlights the potential for in situ measurements and online monitoring of reactions in a crystallization reactor vessel. The results demonstrate that this combined spectroscopic and chemometric approach can accurately and simultaneously quantify Pu(IV) and HNO 3 , thereby offering a promising tool for real-time monitoring in process environments.
Actinide↗
Design Optimization of A generic Fissile Solution for Mo99 production using Electron Beam-based Neutron Generator using MCNP+CFD
Molybdenum-99 ( 99 Mo) is a medical isotope used in 80% of all medical imaging procedures today. However, 99 Mo is not directly administered to patients for imaging; its decay product, technetium-99m ( 99 mTc), is a metastable isotope with a half-life of ~6 hours. Such a short half-life does not allow for production, separation, and application to a patient before decaying further. Therefore, the solution is to produce the precursor to 99 mTc, 99 Mo. 99 Mo has a half-life of 66 hours, long enough to produce the material and send it to the desired location before it fully decays. Under National Nuclear Security Administration’s (NNSA) highly enriched uranium (HEU) minimization mission, the Material Management and Minimization (M3) office leads the Molybdenum-99 (Mo-99) program. A major goal of the Mo-99 program is to develop methods of producing 99 Mo using low enriched uranium (LEU) opposed to the traditionally used HEU domestically in the U.S. Internationally, LEU is the new standard for use in research reactors and isotope production facility. In this study, we introduce a generic fissile solution system for 99 Mo production facility meeting the design requirement of 1) LEU usage as fissile material and 2) subcritical fissile system coupled with a horizontal electron beam (E-beam) accelerator. The focus of this research is to optimize a fissile solution configuration for maximum 99 Mo production yield using MCNP model, and ultimately implement the optimized design with computational fluid dynamics (CFD) model to understand thermal hydraulic behavior and solution convection characteristic. The report consists of two parts: 1) isotope yield calculation and criticality analysis with a wide range of parametric test matrix, and 2) thermal hydraulic analysis for solution mixing and cooling assessment.
60 APPLIED LIFE SCIENCES↗