THESEUS: An Optimal Design and Downselection Framework for Energy Storage Technologies
The talk presents development of a software framework for optimal design and downselction of energy storage technologies.
25 ENERGY STORAGE↗
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.
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Ocean power technology design optimization
For this study, the National Renewable Energy Laboratory and Ocean Power Technologies (OPT) conducted a collaborative code validation and design optimization study for OPT's PowerBuoy wave energy converter (WEC). NREL utilized WEC-Sim, an open-source WEC simulator, to compare four design variations of OPT's PowerBuoy. As an input to the WEC-Sim models, viscous drag coefficients for the PowerBuoy floats were first evaluated using computational fluid dynamics. The resulting WEC-Sim PowerBuoy models were then validated with experimental power output and fatigue load data provided by OPT. The validated WEC-Sim models were then used to simulate the power performance and loads for operational conditions, extreme conditions, and directional waves, for each of the four PowerBuoy design variations, assuming the wave environment of Humboldt Bay, California. And finally, ratios of power-to-weight, power-to-fatigue-load, power-to-maximum-extreme-load, power-to-water-plane-area, and power-to-wetted-surface-area were used to make a final comparison of the potential PowerBuoy WEC designs. Lastly, the design comparison methodologies developed and presented in this study are applicable to other WEC devices and may be useful as a framework for future WEC design development projects.
16 TIDAL AND WAVE POWER↗
Optimal design of a single-mode trench-assisted homogeneous multicore fibre with a minimum crosstalk level
We have proposed an optimal design of five different core structures (St1 – St5) for a seven-core trench-assisted multicore fibre, whose core radius and relative core/cladding refractive index difference Δ{sub 1} are optimal for single-mode propagation. The crosstalk performance and its reduction (in dB) with respect to variations in fibre design parameters, such as cladding/trench (Δ{sub 2}) depth and trench width W{sub t} are investigated. It is shown that some fibre operational parameters, like, fibre bending radius, operating wavelength and fibre length, affect the crosstalk performance. It is found that the crosstalk and its reduction amount are extremely dependent on the values of Δ{sub 1} and Δ{sub 2}. The dispersion characteristics of the cores are investigated for all the proposed core structures and the observed dispersion is found to be about – 6 ps nm{sup −1} km{sup −1} at an operating wavelength of 1550 nm. An ultralow crosstalk (less than –100 dB per 100 km) can be achieved mainly with two proposed core structures (St4 and St5) having a suitably higher values of Δ{sub 1} along with a small core radius for single-mode propagation. The multicore fibre design with many cores (up to 55) is demonstrated to ensure a high transmission capacity. (optical fibres)
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites
With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. Here, this study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.
36 MATERIALS SCIENCE↗
Optimal design of 3D borehole seismic arrays for microearthquake monitoring in anisotropic media during stimulations in the EGS collab project
Multiple U.S. national laboratories, universities and industrial collaborators are conducting collaborative research under the EGS Collab project supported by the U.S. Department of Energy, to understand the fracture creation and imaging during fracturing in enhanced geothermal systems. Microearthquake hypocenter locations and focal mechanisms are used to monitor hydraulic fracturing growth and characterization at the EGS Collab experimental site at the Sanford Underground Research Facility using seismic receivers in multiple monitoring wells. In this study, we develop a methodology for optimal design a 3D borehole seismic array for cost-effective seismic monitoring in anisotropic media using not only the relationship between receiver distributions and standard deviation errors of microearthquake hypocenter locations, but also that between receiver distributions and focal-mechanism inversion errors. Our results indicate that microearthquake hypocenter locations and focal mechanisms can be reasonably well reconstructed for the EGS Collab Experiment I using six monitoring wells, including four fracture-parallel monitoring wells and two orthogonal wells. Lastly, eight seismic receivers evenly distributed in four parallel monitoring wells or twelve receivers in all six monitoring wells are required for hypocenter location, and twelve receivers evenly distributed in six wells or sixteen receivers in four wells are needed for focal-mechanism inversion.
58 GEOSCIENCES↗
Optimal design of multi-stage vacuum membrane distillation and integration with supercritical water desalination for improved zero liquid discharge desalination
Herein this paper proposes a novel concept for the optimal design of multi-stage vacuum membrane distillation (VMD). Generally, a multi-stage VMD is designed with an equal temperature difference between each stage. However, such a design is energy inefficient and increases VMD area. An analytical methodology for calculating the optimal stage temperature is proposed. By selecting the optimal stage temperature, the energy efficiency and required membrane area can be potentially improved by 16% and 30%, respectively. The proposed concept applies to all heat sources, including latent, sensible, and waste heat. To illustrate the method, multi-stage VMD is integrated with the waste heat from a supercritical water desalination (SCWD) system to achieve zero liquid discharge. SCWD is an energy-intensive process, requires high-quality thermal heat (>450 °C), and exhibits high waste heat rejection. The integrated VMD-SCWD approach is approximately 50% more energy-efficient and 35% more cost-efficient than the standalone SCWD system. Compared to the commercially used brine concentrator and crystallizer, the multi-stage VMD-SCWD system is more energy efficient for feed concentrations >5%. VMD-SCWD system is 30% cheaper, due to less expensive membrane distillation modules compared to a brine concentrator. The proposed design concept can replace the brine concentrator and crystallizer as an improved alternative for a zero-liquid discharge desalination system.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Optimized emission in nanorod arrays through quasi-aperiodic inverse design
Not Available
Anderson, P. Duke↗
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↗
A dedicated cone-beam CT system for musculoskeletal extremities imaging: Design, optimization, and initial performance characterization
Purpose: This paper reports on the design and initial imaging performance of a dedicated cone-beam CT (CBCT) system for musculoskeletal (MSK) extremities. The system complements conventional CT and MR and offers a variety of potential clinical and logistical advantages that are likely to be of benefit to diagnosis, treatment planning, and assessment of therapy response in MSK radiology, orthopaedic surgery, and rheumatology. Methods: The scanner design incorporated a host of clinical requirements (e.g., ability to scan the weight-bearing knee in a natural stance) and was guided by theoretical and experimental analysis of image quality and dose. Such criteria identified the following basic scanner components and system configuration: a flat-panel detector (FPD, Varian 3030+, 0.194 mm pixels); and a low-power, fixed anode x-ray source with 0.5 mm focal spot (SourceRay XRS-125-7K-P, 0.875 kW) mounted on a retractable C-arm allowing for two scanning orientations with the capability for side entry, viz. a standing configuration for imaging of weight-bearing lower extremities and a sitting configuration for imaging of tensioned upper extremity and unloaded lower extremity. Theoretical modeling employed cascaded systems analysis of modulation transfer function (MTF) and detective quantum efficiency (DQE) computed as a function of system geometry, kVp and filtration, dose, source power, etc. Physical experimentation utilized an imaging bench simulating the scanner geometry for verification of theoretical results and investigation of other factors, such as antiscatter grid selection and 3D image quality in phantom and cadaver, including qualitative comparison to conventional CT. Results: Theoretical modeling and benchtop experimentation confirmed the basic suitability of the FPD and x-ray source mentioned above. Clinical requirements combined with analysis of MTF and DQE yielded the following system geometry: a {approx}55 cm source-to-detector distance; 1.3 magnification; a 20 cm diameter bore (20 x 20 x 20 cm{sup 3} field of view); total acquisition arc of {approx}240 deg. The system MTF declines to 50% at {approx}1.3 mm{sup -1} and to 10% at {approx}2.7 mm{sup -1}, consistent with sub-millimeter spatial resolution. Analysis of DQE suggested a nominal technique of 90 kVp (+0.3 mm Cu added filtration) to provide high imaging performance from {approx}500 projections at less than {approx}0.5 kW power, implying {approx}6.4 mGy (0.064 mSv) for low-dose protocols and {approx}15 mGy (0.15 mSv) for high-quality protocols. The experimental studies show improved image uniformity and contrast-to-noise ratio (without increase in dose) through incorporation of a custom 10:1 GR antiscatter grid. Cadaver images demonstrate exquisite bone detail, visualization of articular morphology, and soft-tissue visibility comparable to diagnostic CT (10-20 HU contrast resolution). Conclusions: The results indicate that the proposed system will deliver volumetric images of the extremities with soft-tissue contrast resolution comparable to diagnostic CT and improved spatial resolution at potentially reduced dose. Cascaded systems analysis provided a useful basis for system design and optimization without costly repeated experimentation. A combined process of design specification, image quality analysis, clinical feedback, and revision yielded a prototype that is now awaiting clinical pilot studies. Potential advantages of the proposed system include reduced space and cost, imaging of load-bearing extremities, and combined volumetric imaging with real-time fluoroscopy and digital radiography.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
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↗