Simulation-based Bayesian Optimal Design for Ice Sheet Borehole Experiments.
Abstract not provided.
Huan, Xun↗
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Simulation-based Bayesian Optimal Design for Ice Sheet Borehole Experiments.
Abstract not provided.
Huan, Xun↗
Optimal choice of multiple line-of-sight measurements determining plasma hotspot velocity at the National Ignition Facility
The measurement of plasma hotspot velocity provides an important diagnostic of implosion performance for inertial confinement fusion experiments at the National Ignition Facility. The shift of the fusion product neutron mean kinetic energy as measured along multiple line-of-sight time-of-flight spectrometers provides velocity vector components from which the hotspot velocity is inferred. Multiple measurements improve the hotspot velocity inference; however, practical considerations of available space, operational overhead, and instrumentation costs limit the number of possible line-of-sight measurements. Here, we propose a solution to this classical “experiment design” problem that optimizes the precision of the velocity inference for a limited number of measurements.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Integrating novel stellarator single-stage optimization algorithms to design the Columbia stellarator experiment
Abstract The Columbia Stellarator eXperiment (CSX), currently being designed at Columbia University, aims to test theoretical predictions related to QA plasma behavior, and to pioneer the construction of an optimized stellarator using three-dimensional, non-insulated high-temperature superconducting (NI-HTS) coils. The magnetic configuration is generated by a combination of two circular planar poloidal field (PF) coils and two 3D-shaped interlinked (IL) coils, with the possibility to add windowpane coils to enhance shaping and experimental flexibility. The PF coils and vacuum vessel are repurposed from the former Columbia Non-Neutral Torus experiment, while the IL coils will be custom-wound in-house using NI-HTS tapes. To obtain a plasma shape that meets the physics objectives with a limited number of coils, novel single-stage optimization techniques are employed, optimizing both the plasma and coils concurrently, in particular targeting a tight aspect ratio QA plasma and minimized strain on the HTS tape. Despite the increased complexity due to the expanded degrees of freedom, these methods successfully identify optimized plasma geometries that can be realized by coils meeting engineering specifications. This paper discusses the derivation of the constraints and objectives specific to CSX, and describe how two recently developed single-stage optimization methodologies are applied to the design of CSX. A set of selected configurations for CSX is then described in detail.
Baillod, A. (ORCID:0000000303529180)↗
Supercritical wing design using numerical optimization and comparisons with experiment
A numerical minimization scheme is used in conjunction with two-dimensional and three-dimensional inviscid transonic flow analysis codes to provide procedures for wing leading edge aerodynamic design. The procedures are demonstrated in the design of a new leading edge to improve C-141 cruise performance. For the high aspect ratio moderately swept C-141 wing, the 2-D procedure is shown to yield results which are in close agreement with those obtained using the 3-D technique. Although the 2-D approach uses much less computation time than the 3-D technique, the latter requires fewer manhours than the former. Comparisons of predicted and wind tunnel measured performance improvements are presented which verify the design procedures.
Lores, M. E.↗
Constrained Bayesian Optimization of Criticality Experiments at LLNL [Slides]
The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for critical ity safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.
42 ENGINEERING↗
Model calibration and optimization of a post-combustion CO 2 WAG pilot in a mature oil field
A carbon dioxide (CO 2 ) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO 2 flood in the West Ranch Field as part of the Petra Nova project, the world's largest postcombustion CO 2 capture and utilization initiative. With a fluvial formation geology, this is a challenging and novel application of CO 2 enhanced oil recovery. The aim of this study is two-fold. First, to build a predictive dynamic model that incorporates the data acquired during the pilot operation. Second, to utilize this model to provide guidance for optimal design for a field-wide CO 2 EOR operation. Our study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. Next, local permeability field is fine-tuned using a streamline-based method to match CO 2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO 2 recovery as a fraction of the injected CO 2 . Sensitivity studies were conducted using the calibrated pilot model to evaluate the effects of various operational parameters. Finally, we performed a Design of Experiment based multi-objective optimization of the CO 2 WAG processes using the influential operational parameters identified from the sensitivity analysis.
02 PETROLEUM↗
[Retracted] Research on Application Experience Design of Ice and Snow Sports Equipment Based on Bee Colony Model
Sports equipment is the key to the smooth development of ice and snow sports. With the rapid development of social economy and the improvement of people’s living standards, the demand for ice and snow sports equipment is increasing day by day. This article presents an improved method based on the chaos theory and the bee colony algorithm to quantify the application experience design of ice and snow sports equipment and reduce the influence of uncertain factors on the design results. First, the chaos theory can establish the dataset of application experience design and analyze the discreteness of the set. According to the bee colony algorithm, the dataset is divided into several groups, and each group obtains the best application experience design by using the design optimization strategy. Finally, the results are mixed to obtain the final experience design results. Through MATLAB simulation analysis and verification, the improved bee colony model can improve the accuracy of application experience design of ice and snow sports equipment in an uncertain environment, shorten the overall design time, and meet the requirements of application experience design of different ice and snow sports equipment. Therefore, the model proposed in this paper is suitable for the application experience design of ice and snow sports equipment.
Li, Yuanjing (ORCID:000000018276647X)↗
NAMER: A FORTRAN 4 program for use in optimizing designs of two-level factorial experiments given partial prior information
Under certain specified conditions, the Bayes procedure for designing two-level fractional factorial experiments is that which maximizes the expected utility over all possible choices of parameter-estimator matchings, physical-design variable matchings, defining parameter groups, and sequences of telescoping groups. NAMER computes the utility of all possible matchings of physical variables to design variables and parameters to estimators for a specified choice of defining parameter group or groups. The matching yielding the maximum expected utility is indicated, and detailed information is provided about the optimal matchings and utilities. Complete documentation is given; and an example illustrates input, output, and usage.
Sidik, S. M.↗
Fuel Injector Design Optimization for an Annular Scramjet Geometry
A four-parameter, three-level, central composite experiment design has been used to optimize the configuration of an annular scramjet injector geometry using computational fluid dynamics. The computational fluid dynamic solutions played the role of computer experiments, and response surface methodology was used to capture the simulation results for mixing efficiency and total pressure recovery within the scramjet flowpath. An optimization procedure, based upon the response surface results of mixing efficiency, was used to compare the optimal design configuration against the target efficiency value of 92.5%. The results of three different optimization procedures are presented and all point to the need to look outside the current design space for different injector geometries that can meet or exceed the stated mixing efficiency target.
Steffen, Christopher J., Jr.↗
Bayesian Optimal Experimental Design for Photoionization Mass Spectrometry Experiments.
Abstract not provided.
Oreluk, James↗
Optimizing designs of two-level factorial experiments given partial prior information (NAMER)
NAMER is used to find Bayes procedure for designing two level, fractional factorial experiments when given partial prior information. Program is written in FORTRAN 4 for IBM 7094 computer.
Sidik, S. M.↗
Practical input optimization for aircraft parameter estimation experiments
The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.
Morelli, Eugene A.↗
Identifiability and characterization of transmon qutrits through Bayesian experimental design
Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.
97 MATHEMATICS AND COMPUTING↗
Automated and highly parallelized Bayesian optimization scheme for direct drive fusion experiments on OMEGA
Finding the optimal implosion design on existing experimental facilities for inertial confinement fusion requires an exhaustive search of the vast design parameter space. This is infeasible both with experiments and with simulations. Consequently, a large fraction of the experimentally realizable design space remains unexplored, and new design schemes are challenging to optimize in a reasonable time frame. On the OMEGA laser facility, predictive machine learning models have been developed to accurately forecast the result of an experiment using only inexpensive simulations and the large dataset of prior experimental data. However, the full design space remains vast enough to be unassailable with simple optimization techniques. Here we develop an automated and optimally parallel Bayesian optimization algorithm that can entirely optimize the target and pulse shape of a direct-drive ICF implosion under a given design paradigm. We use this algorithm to find a markedly improved design for the performance implosions on OMEGA that is predicted to hydroequivalently scale to ignition at 2.15 MJ.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Observations Regarding Use of Advanced CFD Analysis, Sensitivity Analysis, and Design Codes in MDO
Observations regarding the use of advanced computational fluid dynamics (CFD) analysis, sensitivity analysis (SA), and design codes in gradient-based multidisciplinary design optimization (MDO) reflect our perception of the interactions required of CFD and our experience in recent aerodynamic design optimization studies using CFD. Sample results from these latter studies are summarized for conventional optimization (analysis - SA codes) and simultaneous analysis and design optimization (design code) using both Euler and Navier-Stokes flow approximations. The amount of computational resources required for aerodynamic design using CFD via analysis - SA codes is greater than that required for design codes. Thus, an MDO formulation that utilizes the more efficient design codes where possible is desired. However, in the aerovehicle MDO problem, the various disciplines that are involved have different design points in the flight envelope; therefore, CFD analysis - SA codes are required at the aerodynamic 'off design' points. The suggested MDO formulation is a hybrid multilevel optimization procedure that consists of both multipoint CFD analysis - SA codes and multipoint CFD design codes that perform suboptimizations.
Newman, Perry A.↗
Techniques for designing rotorcraft control systems
This report summarizes the work that was done on the project from 1 Apr. 1992 to 31 Mar. 1993. The main goal of this research is to develop a practical tool for rotorcraft control system design based on interactive optimization tools (CONSOL-OPTCAD) and classical rotorcraft design considerations (ADOCS). This approach enables the designer to combine engineering intuition and experience with parametric optimization. The combination should make it possible to produce a better design faster than would be possible using either pure optimization or pure intuition and experience. We emphasize that the goal of this project is not to develop an algorithm. It is to develop a tool. We want to keep the human designer in the design process to take advantage of his or her experience and creativity. The role of the computer is to perform the calculation necessary to improve and to display the performance of the nominal design. Briefly, during the first year we have connected CONSOL-OPTCAD, an existing software package for optimizing parameters with respect to multiple performance criteria, to a simplified nonlinear simulation of the UH-60 rotorcraft. We have also created mathematical approximations to the Mil-specs for rotorcraft handling qualities and input them into CONSOL-OPTCAD. Finally, we have developed the additional software necessary to use CONSOL-OPTCAD for the design of rotorcraft controllers.
Levine, William S.↗
ISS-Crystal Growth of Photorefractive Materials (BSO): Critical Design Issues for Optimized Data Extraction from Space Experiments
Realization of the full potential of photorefractive materials in device technology is seriously impeded by our inability to achieve controlled formation of critical defects during single crystal growth and by difficulties in meeting the required degree of compositional uniformity on a micro-scale over macroscopic dimensions. The exact nature and origin of the critical defects which control photorefractivity could not as yet be identified because of gravitational interference. There exists, however, strong evidence that the density of defect formation and their spatial distribution are adversely affected by gravitational interference which precludes the establishment of quantifiable and controllable heat and mass transfer conditions during crystal growth. The current, NASA sponsored research at MIT is directed at establishing a basis for the development of a comprehensive approach to the optimization of property control during melt growth of photorefractive materials, making use of the m-g environment, provided in the International Space Station. The objectives to be pursued in m-g research on photorefractive BSO (Bi12SiO20) are: (a) identification of the x-level(s) responsible for photorefractivity in undoped BSO; (b) development of approaches leading to the control of x-level formation at uniform spatial distribution; (c) development of doping and processing procedures for optimization of the critical, application specific parameters, spectral response, sensitivity, response time and matrix stability. The presentation will focus on: the rationale for the justification of the space experiment, ground-based development efforts, design considerations for the space experiments, strategic plan of the space experiments, and approaches to the quantitative analysis of the space experiments.
Hyers, Robert W.↗