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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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Guest Editorial: Optimal Design of Electric Machines
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Design Optimization of Asymmetric Salient Permanent Magnet Synchronous Machines
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MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging
Here, this work extends our previous research entitled "MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization" that was originally published at SC19 conference (Hidayetoglu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11Kx11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.
Performance Evaluation and Optimal Design Analysis of Continuous-Operation Solar-Driven Cooling Absorption Systems With Thermal Energy Storage
A solar absorption cooling system consisting of a flat plate collector, thermal energy storage tank, and absorption chiller is analyzed in this work. A dimensionless model is developed from the energy balance on each component and the chiller's characteristic performance curves. The model is used to determine the interaction and influence of different parameters such as tank size, solar collector area, chiller size, cooling load, cooling temperature, heat loss, and mass flow rates on the performance. From the analysis, smaller solar collector areas are required for lower cooling loads and smaller tank volumes. A specific cooling load of 1 x 10^-5 will require a specific solar collector area between two and six times larger, depending on the initial tank temperature, than the area required for a baseline system that considers typical commercial design and operation parameters. A similar behavior was observed for the specific tank volume. For the baseline system, the minimum specific area of the collector of 9.57 is achieved for an initial tank temperature of 1.19. For a cooling load of 1 x 10^-5, the optimum initial tank temperature will be 1.11 that results in a minimum specific solar collector area of 25.26. A specific tank volume of 4 x 10^-4 will also have an optimum initial tank temperature of 1.11 that minimizes the specific solar collector area to a value of 28.18. The approach and analysis in this work can be used to determine design parameters for solar absorption cooling systems based on a proper relation among system's dimensions to achieve optimum operation.
Design optimization of a confocal X-ray fluorescence imaging capability for XFM and SRX at NSLS-II
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Design Optimization and Fabrication of Mechanical Metamaterials for Elastic Wave Control.
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A Workflow for Combining Multiple MCNP Models and Debugging Geometries using Geomwriter and FLAIR
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LEU+ BWR Lattice Design Optimization of Using SCALE/Polaris
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Understanding search behavior via search landscape analysis in design optimization of optical structures
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Objective-trait-bias metaheuristics for design optimization of optical structures
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Inverse design optimization for efficient coupling of an electrically injected optical antenna-LED to a single-mode waveguide
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Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership
The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.
Effect of Tip-Speed Constraints on the Optimized Design of a Wind Turbine
This study investigates the effect of tip-velocity constraints on system levelized cost of energy (LCOE). The results indicate that a change in maximum tip speed from 80 to 100~m/s could produce a 32% decrease in gearbox weight (a 33% reduction in cost) which would result in an overall reduction of 1%-9% in system LCOE depending on the design approach. Three 100~m/s design cases were considered including a low tip-speed ratio/high-solidity rotor design, a high tip-speed ratio/ low-solidity rotor design, and finally a flexible blade design in which a high tip-speed ratio was used along with removing the tip deflection constraint on the rotor design. In all three cases, the significant reduction in gearbox weight caused by the higher tip-speed and lower overall gear ratio was counterbalanced by increased weights for the rotor and/or other drivetrain components and the tower. As a result, the increased costs of either the rotor or drivetrain components offset the overall reduction in turbine costs from down-sizing the gearbox. Other system costs were not significantly affected, whereas energy production was slightly reduced in the 100~m/s case low tip-speed ratio case and increased in the high tip-speed ratio case. This resulted in system cost of energy reductions moving from the 80~m/s design to the 100~m/s designs of 1.2% for the low tip-speed ratio, 4.6% for the high tip-speed ratio, and 9.5% for the final flexible case (the latter result is optimistic because the impact of deflection of the flexible blade on power production was not modeled). Overall, the results demonstrate that there is a trade-off in system design between the maximum tip velocity and the overall wind plant cost of energy, and there are many trade-offs within the overall system in designing a turbine for a high maximum tip velocity.
Optimal Design and Control of Qubits
Research interest in developing computing systems that represent logic states using quantum mechanical observables has only increased in the few decades since its inception. While quantum computers, with Josephson junction based qubits, have now been commercially available in the last three years, there is also significant research initiative to develop scalable quantum computers with so-called donor qubits. B.E. Kane first published on a device implementation of a silicon-based quantum computer in 1998, which sparked a wave of follow-on advances due to the attractive nature of silicon-based computing[7]. Nearly all commercial computing systems using classical binary logic are fabricated using a silicon substrate and it is inarguably the most mature material system for semiconductor devices, so that coupling classical and quantum bits on a single substrate is possible. The process of growing and processing silicon crystals into wafers is extremely robust and leads to minimal impurities or structural defects.
Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration
This article is concerned with the approximation of high-dimensional functions by kernel-based methods. Motivated by uncertainty quantification, which often necessitates the construction of approximations that are accurate with respect to a probability density function of random variables, we aim at minimizing the approximation error with respect to a weighted $L^p$-norm. We present a greedy procedure for designing computer experiments based upon a weighted modification of the pivoted Cholesky factorization. The method successively generates nested samples with the goal of minimizing error in regions of high probability. Numerical experiments validate that this new importance sampling strategy is superior to other sampling approaches, especially when used with non-product probability density functions. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data.