An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Arpan Biswas ◽  
Christopher Hoyle
Keyword(s):  
Fatigue Failure ◽  
Design Space ◽  
Black Box ◽  
Bayesian Optimization ◽  
Design Parameters ◽  
Classification Error ◽  
Support Vector ◽  
Thin Tube ◽  
Creep Fatigue ◽  
Training Cost

Abstract The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ∼150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting.

An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

2020 ◽  
Author(s):  
Arpan Biswas ◽  
Christopher Hoyle
Keyword(s):  
Objective Function ◽  
Prior Knowledge ◽  
Design Space ◽  
Black Box ◽  
Training Data ◽  
Bayesian Optimization ◽  
Design Parameters ◽  
Classification Error ◽  
True Solution ◽  
Classification Tool

Abstract The paper presents a novel approach to apply Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is an emerging field of study in the Sequential Design Methods where we learn and update our knowledge from prior evaluated designs, and proceed to the selection of new designs for future evaluation. It has been considered as a low-cost global optimization tool for design problems having expensive black-box objective functions. However, BO is mostly suited to problems with the assumption of a continuous objective function, and does not guarantee true convergence if the objective function has a discontinuity. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. Therefore, in this paper, we have proposed to predict the discontinuity of the objective function using a BO algorithm which can be considered as the pre-stage before optimizing the same unknown objective function. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transitions between those regions; therefore, the discontinuity has also been treated as an unknown constraint. The paper focuses on developing BO framework with maximizing the reformulated objective function on the same design space to predict the transition regions as a design methodology or classification tool between safe and unsafe designs, where we start with very limited data or no prior knowledge and then iteratively focus on sampling most designs near the transition region through better prior knowledge (training data) and thereby increase the accuracy of prediction to the true boundary while minimizing the number of expensive function evaluations. The converged model has been compared with the true solution for different design parameters and the results provided a classification error rate and function evaluations at an average of < 1% and ∼150, respectively. The results in this paper show some future research directions in extending the application of BO and considered as the proof of concept on large scale problem of complex diffusion bonded hybrid Compact Heat Exchangers.

scholarly journals On the Design of a Class of Rotary Compressors Using Bayesian Optimization

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 219
Author(s):  
Kui Lu ◽  
Truong H. Phung ◽  
Ibrahim A. Sultan
Keyword(s):  
Black Box ◽  
Bayesian Optimization ◽  
Optimization Process ◽  
Design Parameters ◽  
Mathematical Form ◽  
The Novel ◽  
Numerical Illustration ◽  
Positive Displacement ◽  
Machine Performance ◽  
The Relationship

The optimization process of compressors is usually regarded as a ‘black-box’ problem, in which the mathematical form underlying the relationship between design parameters and the design objective is impractical and costly to be obtained. To solve the ‘black-box’ problem, Bayesian optimization has been proven as an accurate and efficient method. However, the application of such a method in the design of compressors is rarely discussed, particularly no work has been reported in terms of the positive displacement type compressor. Therefore, this paper aims to introduce the Bayesian optimization to the design of positive displacement compressors through the optimization process of the novel limaçon compressor. In this paper, a two-stage optimization process is presented, in which the first stage optimizes the geometric parameters as per design requirements and the second stage focuses on revealing an optimum setting of port geometries that improves machine performance. A numerical illustration is offered to prove the validity of the presented approach.

scholarly journals Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

2021 ◽  
pp. 027836492110333
Author(s):  
Gilhyun Ryou ◽  
Ezra Tal ◽  
Sertac Karaman
Keyword(s):  
Real World ◽  
High Speed ◽  
Analytical Approximation ◽  
Black Box ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Quadrotor Aircraft ◽  
Time Optimal ◽  
Feasibility Constraints ◽  
Generation Problem

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.

scholarly journals Evaluation of Static Mapping for Dynamic Space-Shared Multi-task Processing on FPGAs

2021 ◽  
Author(s):  
Umar Ibrahim Minhas ◽  
Roger Woods ◽  
Georgios Karakonstantis
Keyword(s):  
High Performance ◽  
Design Space Exploration ◽  
Design Space ◽  
System Throughput ◽  
Design Parameters ◽  
Temporal Constraints ◽  
Shared Resources ◽  
Task Processing ◽  
High Level ◽  
Performance Computing

AbstractWhilst FPGAs have been used in cloud ecosystems, it is still extremely challenging to achieve high compute density when mapping heterogeneous multi-tasks on shared resources at runtime. This work addresses this by treating the FPGA resource as a service and employing multi-task processing at the high level, design space exploration and static off-line partitioning in order to allow more efficient mapping of heterogeneous tasks onto the FPGA. In addition, a new, comprehensive runtime functional simulator is used to evaluate the effect of various spatial and temporal constraints on both the existing and new approaches when varying system design parameters. A comprehensive suite of real high performance computing tasks was implemented on a Nallatech 385 FPGA card and show that our approach can provide on average 2.9 × and 2.3 × higher system throughput for compute and mixed intensity tasks, while 0.2 × lower for memory intensive tasks due to external memory access latency and bandwidth limitations. The work has been extended by introducing a novel scheduling scheme to enhance temporal utilization of resources when using the proposed approach. Additional results for large queues of mixed intensity tasks (compute and memory) show that the proposed partitioning and scheduling approach can provide higher than 3 × system speedup over previous schemes.

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

Creep-Fatigue Interaction Effects On Pressure-Reducing Valve Under Cyclic Thermo-Mechanical Loadings Using Direct Cyclic Method

2021 ◽  
Author(s):  
Nak-Kyun Cho ◽  
Youngjae Choi ◽  
Haofeng Chen
Keyword(s):  
High Temperature ◽  
Power Plant ◽  
Fatigue Failure ◽  
Material Properties ◽  
Structural Integrity ◽  
Direct Method ◽  
Element Model ◽  
Critical Loading ◽  
Creep Fatigue ◽  
Pressure Reducing Valve

Abstract Supercritical boiler system has been widely used to increase efficiency of electricity generation in power plant industries. However, the supercritical operating condition can seriously affect structural integrity of power plant components due to high temperature that causes degradation of material properties. Pressure reducing valve is an important component being employed within a main steam line of the supercritical boiler, which occasionally thermal-fatigue failure being reported. This research has investigated creep-cyclic plastic behaviour of the pressure reducing valve under combined thermo-mechanical loading using a numerical direct method known as extended Direct Steady Cyclic Analysis of the Linear Matching Method Framework (LMM eDSCA). Finite element model of the pressure-reducing valve is created based on a practical valve dimension and temperature-dependent material properties are applied for the numerical analysis. The simulation results demonstrate a critical loading component that attributes creep-fatigue failure of the valve. Parametric studies confirm the effects of magnitude of the critical loading component on creep deformation and total deformation per loading cycle. With these comprehensive numerical results, this research provides engineer with an insight into the failure mechanism of the pressure-reducing valve at high temperature.

Prediction of CO2 Minimum Miscibility Pressure Using an Augmented Machine-Learning-Based Model

SPE Journal ◽  
2021 ◽  
pp. 1-13
Author(s):  
Utkarsh Sinha ◽  
Birol Dindoruk ◽  
Mohamed Soliman
Keyword(s):  
Machine Learning ◽  
Phase Behavior ◽  
Hybrid Method ◽  
Gas Injection ◽  
Supervised Machine Learning ◽  
Design Parameters ◽  
Support Vector ◽  
Hydrocarbon Gases ◽  
Minimum Miscibility Pressure ◽  
Analytical Correlation

Summary Minimum miscibility pressure (MMP) is one of the key design parameters for gas injection projects. It is a physical parameter that is a measure of local displacement efficiency while subject to some constraints due to its definition. Also, the MMP value is used to tune compositional models along with proper fluid description constrained with other available basic phase behavior data, such as bubble point pressure and volumetric properties. In general, carbon dioxide (CO2) and hydrocarbon gases are the most common gases used for (or screened for) gas injection processes, and because of recent focus, they are used to screen for the coupling of CO2-sequestration and CO2-enhanced oil recovery (EOR) projects. Because the CO2/oil phase behavior is quite different than the hydrocarbon gas/oil phase behavior, researchers developed specialized correlations for CO2 or CO2-rich streams. Therefore, there is a need for a tool with expanded range capabilities for the estimation of MMP for CO2 gas streams. The only known and widely accepted measurement technique for MMP that is coherent with its formal definition is the use of a slimtube apparatus. However, the use of slimtube restricts the amount of data available, even though there are other alternative techniques presented over the last three decades, which all have various limitations (Dindoruk et al. 2021). Due to some of the complexities highlighted in Dindoruk et al. (2021) and time and resource requirements, there have been a number of correlations developed in the literature using mostly classical regression techniques with relatively sparse data using various combinations of limited input data (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Jaubert et al. 1998; Emera and Sarma 2005; Yuan et al. 2005; Ahmadi et al. 2010; Ahmadi and Johns 2011). In this paper, we present two separate approaches for the calculation of the MMP of an oil for CO2 injection: analytical correlation in which the correlation coefficients were tuned using linear support vector machines (SVMs) (Press et al. 2007; MathWorks 2020; RDocumentation 2020b; Cortes and Vapnik 1995) and using a hybrid method (i.e., superlearner model), which consists of the combination of random forest (RF) regression (Breiman 2001) and the proposed analytical correlation. Both models take the compositional analysis of oils up to heptane plus fraction, molecular weight of oil, and the reservoir temperature as input parameters. Based on statistical and data analysis techniques in combination with the help of corresponding crossplots, we showed that the performance of the final proposed method (hybrid method) is superior to all the leading correlations (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Emera and Sarma 2005; Yuan et al. 2005) and supervised machine-learning (Metcalfe 1982) methods considered in the literature (Altman 1992; Chambers and Hastie 1992; Chapelle and Vapnik 2000; Breiman 2001; Press et al. 2007; MathWorks 2020). The proposed model works for the widest spectrum of MMPs from 1,000 to 4,900 psia, which covers the entire range of oils within the scope of CO2 EOR based on the widely used screening criteria (Taber et al. 1997a, 1997b).

Adaptive Geometry Representation of Turbine Vane Frames for Use in Optimization

2021 ◽  
Author(s):  
Sebastian F. Riebl ◽  
Christian Wakelam ◽  
Reinhard Niehuis
Keyword(s):  
Design Space ◽  
Cfd Simulation ◽  
A Priori ◽  
Three Dimensional ◽  
Optimization Method ◽  
Three Dimensions ◽  
Design Parameters ◽  
Adjustment Process ◽  
Turbine Vane ◽  
Integrated Concept

Abstract Turbine Vane Frames (TVF) are a way to realize more compact jet engine designs. Located between the high pressure turbine (HPT) and the low pressure turbine (LPT), they fulfill structural and aerodynamic tasks. When used as an integrated concept with splitters located between the structural load-bearing vanes, the TVF configuration contains more than one type of airfoil with sometimes pronouncedly different properties. This system of multidisciplinary demands and mixed blading poses an interesting opportunity for optimization. Within the scope of the present work, a full geometric parameterization of a TVF with splitters is presented. The parameterization is chosen as to minimize the number of parameters required to automatically and flexibly represent all blade types involved in a TVF row in all three dimensions. Typical blade design parameters are linked to the fourth order Bézier-curve controlled camber line-thickness parameterization. Based on conventional design rules, a procedure is presented, which sets the parameters within their permissible ranges according to the imposed constraints, using a proprietary developed code. The presented workflow relies on subsequent three dimensional geometry generation by transfer of the proposed parameter set to a commercially available CAD package. The interdependencies of parameters are discussed and their respective significance for the adjustment process is detailed. Furthermore, the capability of the chosen parameterization and adjustment process to rebuild an exemplary reference TVF geometry is demonstrated. The results are verified by comparing not only geometrical profile data, but also validated CFD simulation results between the rebuilt and original geometries. Measures taken to ensure the robustness of the method are highlighted and evaluated by exploring extremes in the permissible design space. Finally, the embedding of the proposed method within the framework of an automated, gradient free numerical optimization is discussed. Herein, implications of the proposed method on response surface modeling in combination with the optimization method are highlighted. The method promises to be an option for improvement of optimization efficiency in gradient free optimization of interdependent blade geometries, by a-priori excluding unsuitable blade combinations, yet keeping restrictions to the design space as limited as possible.

Design Parameters Prediction of New Type Gas Turbine Based on a Hybrid GRA-SVM Prediction Model

2017 ◽  
Author(s):  
Tingting Wei ◽  
Dengji Zhou ◽  
Jinwei Chen ◽  
Yaoxin Cui ◽  
Huisheng Zhang
Keyword(s):  
Prediction Model ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Technology Development ◽  
Design Parameters ◽  
Support Vector ◽  
Performance Parameters ◽  
New Type ◽  
Equipment Manufacture ◽  
Grey Relational

Since the late 1930s, gas turbine has begun to develop rapidly. To improve the economic and safety of gas turbine, new types were generated frequently by Original Equipment Manufacture (OEM). In this paper, a hybrid GRA-SVM prediction model is established to predict the main design parameters of new type gas turbines, based on the combination of Grey Relational Analysis (GRA) and Support Vector Machine (SVM). The parameters are classified into two types, system performance parameters reflecting market demands and technology development, and component performance parameters reflecting technology development and coupling connections. The regularity based on GRA determines the prediction order, then new type gas turbine parameters can be predicted with known system parameters. The model is verified by the application to SGT600. In this way, the evolution rule can be obtained with the development of gas turbine technology, and the improvement potential of several components can be predicted which will provide supports for overall performance design.

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