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At least 451 records · Page 25

Optimization of laser-driven cylindrical implosions on the OMEGA laser

Laser-driven cylindrical implosions were conducted on the OMEGA laser as part of the laser-driven mini-MagLIF (Magnetized Liner Inertial Fusion) Campaign. Gated x-ray images were analyzed to infer shell trajectories and study the energy coupling in these implosions. Two-dimensional and three-dimensional HYDRA simulations were performed and post-processed to produce synthetic x-ray self-emission images for comparison. An analysis technique, which could be applied to both experimental and simulated x-ray images, was developed to characterize the shape and uniformity of the implosion. The analysis leads to a measurement of the average implosion velocity and axial implosion length, which can then be used to optimize the beam pointing and energy balance for future experiments. Discrepancies between simulation results and experiments allude to important physical processes that are not accounted for in the simulations. In 2-D simulations, the laser beam’s azimuthal angle of incidence is not included because the $\phi$-direction is not simulated, thus energy absorption is over-predicted. The 3-D simulation results are more consistent with the experiments, but the simulations do not include a calculation of crossbeam energy transfer or non-local thermal transport, which affect the energy coupled to the implosion. Finally, by appropriately adjusting the simulated energy balance and flux limit, the simulations can accurately model the experiments, which have achieved uniform implosions over a 700-μm-long region at velocities of approximately 200 km=s.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices

The Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices (Project) investigated, analyzed and modeled advanced turbine control schemes with the objective of increasing the energy harvested by hydrokinetic turbines in turbulent flow. Ocean Renewable Power Company (ORPC) implemented and validated a feedforward controller to increase power capture; and applied and tested the controls on ORPC’s RivGen® Power Systems in Igiugig, Alaska. Assessments of performance improvements were made for the RivGen® in the Igiugig environment and for ORPC’s TidGen® Power System in a reference tidal environment. Annualized Energy Production (AEP) and Levelized Cost of Energy (LCOE) improvements associated with implementation of the recommended control methodology were made for the TidGen® Power System in the DOE reference tidal environment. System Performance Advancement (SPA) goals were selected for the project. SPA targets were to improve Power to Weight Ratio (PWR) and system Availability, with the intention of reducing Levelized Cost of Electricity (LCOE). This project focused primarily reducing in PWR. Reductions in PWR of 25.5% were achieved. Reductions of 20.3% in LCOE were achieved. This project evaluated four types of controllers which were tested in simulation, emulation, a laboratory flume, and the field. The adaptive Kω2 controller performs similarly to the non-adaptive version of the same controller and may be useful in tidal channels where the mean velocity is continually evolving. Trends in simulation were largely verified through experiments, which also provided the opportunity to test assumptions about turbine responsiveness and control resilience to varying scales of turbulence. Laboratory experiments provided an essential stepping stone between simulation and implementation on a field-scale turbine. Experiments also demonstrated that using “energy loss” as a metric to differentiate between well-designed controllers operating at an optimal tip-speed ratio set-point is difficult, which anticipated the outcome from field experiments. The clear message is that the feedforward Kω2 controller out-performs the feedback controllers in almost all aspects and modes of evaluation. The controllers proved a substantial improvement over the baseline performance of the TidGen® turbine, in terms of energy capture. The effects of noise-contaminated angular velocity signals were investigated and validated by simulation as an explanation for the performance limitations observed for TidGen® turbine operations in Eastport, Maine. Measurements of loads performed as part of the laboratory testing indicate that there are limited differences in average axial thrust force between control architectures. This suggests that none of the control strategies are likely to substantially affect loads on the turbine support structure. Velocity measurements during the ORPC RivGen® turbine deployment at Igiugig, Alaska, in 2014 were used to assess the variability of the river flow. Results suggest that the river flow is approximately steady, in the mean sense, at any particular location in the river, with random turbulent fluctuations that are around 10% of the mean flow. The mean flow in the center channel of the river is 2.5 m/s, with reductions near the riverbanks and in the shallows. Spectral analysis and lagged correlation results indicate that temporal fluctuations at a given point are dominated by large scale fluctuations, such that measurements at the turbine location are just as useful for inflow control implementation as upstream measurements. At this site, and likely at many other river sites, flow is generally steady at a given location, but flow varies dramatically between locations, particularly laterally across the river. The primary result is that a lateral change in position of a few meters results in changes to flow speed that far exceed the turbulence fluctuations at any given location. The turbulence is dominated by long time scales. Following final system tests, the RivGen® device was submerged and each evaluated controller was tested across a range of gain/set point values and filter configurations for a minimum of 5 minutes, with longer runs attempted for well-performing cases. In addition to testing controllers during the 2015 deployment season, LGL Alaska Research Associates Inc. (LGL) performed a fish monitoring study in compliance with Alaska Department of Fish and Game fisheries habitat permit for the project. During the season, LGL reviewed 10% of 179 one-hour blocks of footage (6 minutes on the hour) and documented a total of over 1200 fish in the vicinity of the RivGen® device, including over 800 salmon smolt and over 350 adult salmon. No evidence of adverse effects including passage delay by upstream migrating salmon was noted. This is an important result for future deployments and has a direct impact on commercial system designs.

16 TIDAL AND WAVE POWER↗

Experimental Optimization of a Free-to-Rotate Wing for Small UAS

This paper discusses an experimental investigation conducted to optimize a free-to-rotate wing for use on a small unmanned aircraft system (UAS). Although free-to-rotate wings have been used for decades on various small UAS and small manned aircraft, little is known about how to optimize these unusual wings for a specific application. The paper discusses some of the design rationale of the basic wing. In addition, three main parameters were selected for "optimization", wing camber, wing pivot location, and wing center of gravity (c.g.) location. A small apparatus was constructed to enable some simple experimental analysis of these parameters. A design-of-experiment series of tests were first conducted to discern which of the main optimization parameters were most likely to have the greatest impact on the outputs of interest, namely, some measure of "stability", some measure of the lift being generated at the neutral position, and how quickly the wing "recovers" from an upset. A second set of tests were conducted to develop a response-surface numerical representation of these outputs as functions of the three primary inputs. The response surface numerical representations are then used to develop an "optimum" within the trade space investigated. The results of the optimization are then tested experimentally to validate the predictions.

Logan, Michael J.↗

CFD-Guided Combustion System Optimization of a Gasoline Range Fuel in a Heavy-Duty Compression Ignition Engine Using Automatic Piston Geometry Generation and a Supercomputer

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NO x within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions. The second DoE campaign was conducted to optimize injector spray patterns, fuel injection strategies and in-cylinder swirl motion for the best performing piston bowl designs from the first DoE campaign. This comprehensive optimization study was performed on a supercomputer, Mira, to accelerate the development of an optimized fuel-efficiency focused design. Compared to the production combustion system in the baseline engine, the new combustion recipe from this study showed significantly improved closed-cycle fuel efficiency across key engine operating points while meeting the engine-out NO x targets. Optimized piston bowl designs and injector spray patterns were predicted to provide enhanced in-cylinder air utilization and more rapid mixing-controlled combustion, thereby leading to a fuel efficiency improvement. In addition, shifting the engine thermal boundary conditions toward leaner operation was also key to the improved fuel efficiency.

33 ADVANCED PROPULSION SYSTEMS↗

HB650 Cryomodule Design: From Prototype to Production

In early 2023 the assembly of the prototype HB650 cryomodule (pHB650 CM) was completed and cold tests started to evaluate its performance. The lessons learned from the design, assembly and preliminary cold tests of this cryomodule, and from the design of the SSR2 pre-production cryomodule played a fundamental role during the design optimization process of the production HB650 cryomodule (HB650 CM). Several workshops have been organized to share experiences and solve problems. This paper presents the main design changes from pHB650 to the HB650 production cryomodules and their impact on the heat loads.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Airborne Imaging Spectrometer - An advanced concept instrument

The Airborne Imaging Spectrometer (AIS) is a multispectral infrared imaging instrument utilizing a 32- x 32-element mercury-cadmium-telluride detector array to achieve high spatial resolution. A novel optical design provides high spectral resolution, and, in conjunction with the 32-element spatial resolution, makes optimal use of the capabilities of the area array. The experiment requirements and the details of instrument design, implementation, and calibration are presented. The superior resolution, accuracy, and data acquisition rate of this sensor/instrument concept for reconnaissance and geophysical survey is detailed and plans for upgrading/improving the instrument and future applications of this design concept are discussed.

Labaw, C.↗

Bremsstrahlung x-ray generation for high optical depth radiography applications on the National Ignition Facility [Bremsstrahlung x-ray generation for opacity radiography applications on the National Ignition Facility]

Here, we have tested a set of x-ray sources for use as probes of highly attenuating, laser-driven experiments on the National Ignition Facility (NIF). Unlike traditional x-ray sources that optimize for a characteristic atomic transition (often the n = 2 → n = 1 transition in ionized, He-like atoms), the design presented here maximizes the total photon flux by optimizing for intense, broadband Bremsstrahlung radiation. Three experiments were performed with identical targets, including a uranium x-ray source foil and a tungsten substrate with a narrow (25 μm wide) collimating slit to produce a quasi-1D x-ray source. Two experiments were performed using 12 beams from the NIF laser, each delivering approximately 46 kJ of laser energy but with different laser spatial profiles. This pair yielded similar temporal x-ray emission profiles, spatial resolution, and inferred hot electron temperature. A third experiment with only 6 beams delivering approximately 25 kJ produced a lower hot electron temperature and significantly lower x-ray flux, as well as poorer spatial resolution. The data suggest that laser pointing jitter may have affected the location and intensity of the emitting plasma, producing an emission volume that was not well centered behind the collimating slit and lower intensity than designed. However, the 12-beam design permits x-ray radiography through highly attenuating samples, where lower energy line-emission x-ray sources would be nearly completely attenuated.

47 OTHER INSTRUMENTATION↗

Analysis-Driven Design Optimization of a SMA-Based Slat-Cove Filler for Aeroacoustic Noise Reduction

Airframe noise is a significant component of environmental noise in the vicinity of airports. The noise associated with the leading-edge slat of typical transport aircraft is a prominent source of airframe noise. Previous work suggests that a slat-cove filler (SCF) may be an effective noise treatment. Hence, development and optimization of a practical slat-cove-filler structure is a priority. The objectives of this work are to optimize the design of a functioning SCF which incorporates superelastic shape memory alloy (SMA) materials as flexures that permit the deformations involved in the configuration change. The goal of the optimization is to minimize the actuation force needed to retract the slat-SCF assembly while satisfying constraints on the maximum SMA stress and on the SCF deflection under static aerodynamic pressure loads, while also satisfying the condition that the SCF self-deploy during slat extension. A finite element analysis model based on a physical bench-top model is created in Abaqus such that automated iterative analysis of the design could be performed. In order to achieve an optimized design, several design variables associated with the current SCF configuration are considered, such as the thicknesses of SMA flexures and the dimensions of various components, SMA and conventional. Designs of experiment (DOE) are performed to investigate structural response to an aerodynamic pressure load and to slat retraction and deployment. DOE results are then used to inform the optimization process, which determines a design minimizing actuator forces while satisfying the required constraints.

Scholten, William↗

Intelligent Sampling for Surrogate Modeling, Hyperparameter Optimization, and Data Analysis

Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from each other. When an application imposes new constraints, for example, by requiring samples in a non-rectangular domain or the addition of new samples to an existing set, a common solution is to modify the algorithm currently in use, often with less than satisfactory results. As an alternative, we propose the concept of intelligent sampling, where we devise algorithms specifically tailored to meet our sampling needs, either by creating new algorithms or by modifying suitable algorithms from other fields. Surprisingly, both qualitative and quantitative comparisons indicate that some relatively simple algorithms can be easily modified to meet the many sampling requirements of surrogate modeling, hyperparameter optimization, and data analysis; these algorithms outperform their more sophisticated counterparts currently in use, resulting in better use of time and computer resources.

97 MATHEMATICS AND COMPUTING↗

CFD-Based Design Optimization Tool Developed for Subsonic Inlet

The traditional approach to the design of engine inlets for commercial transport aircraft is a tedious process that ends with a less-than-optimum design. With the advent of high-speed computers and the availability of more accurate and reliable computational fluid dynamics (CFD) solvers, numerical optimization processes can effectively be used to design an aerodynamic inlet lip that enhances engine performance. The designers' experience at Boeing Corporation showed that for a peak Mach number on the inlet surface beyond some upper limit, the performance of the engine degrades excessively. Thus, our objective was to optimize efficiency (minimize the peak Mach number) at maximum cruise without compromising performance at other operating conditions. Using a CFD code NPARC, the NASA Lewis Research Center, in collaboration with Boeing, developed an integrated procedure at Lewis to find the optimum shape of a subsonic inlet lip and a numerical optimization code, ADS. We used a GRAPE-based three-dimensional grid generator to help automate the optimization procedure. The inlet lip shape at the crown and the keel was described as a superellipse, and the superellipse exponents and radii ratios were considered as design variables. Three operating conditions: cruise, takeoff, and rolling takeoff, were considered in this study. Three-dimensional Euler computations were carried out to obtain the flow field. At the initial design, the peak Mach numbers for maximum cruise, takeoff, and rolling takeoff conditions were 0.88, 1.772, and 1.61, respectively. The acceptable upper limits on the takeoff and rolling takeoff Mach numbers were 1.55 and 1.45. Since the initial design provided by Boeing was found to be optimum with respect to the maximum cruise condition, the sum of the peak Mach numbers at takeoff and rolling takeoff were minimized in the current study while the maximum cruise Mach number was constrained to be close to that at the existing design. With this objective, the optimum design satisfied the upper limits at takeoff and rolling takeoff while retaining the desirable cruise performance. Further studies are being conducted to include static and cross-wind operating conditions in the design optimization procedure. This work was carried out in collaboration with Dr. E.S. Reddy of NYMA, Inc.

Source record↗

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user’s manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers.

97 MATHEMATICS AND COMPUTING↗

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Theory Manual (V.6.15)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗