Surrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning

International audienceThis paper describes a methodology, called local decomposition method, which aims at building a surrogate model based on steady turbulent aerodynamic fields at multiple operating conditions. The various shapes taken by the aerodynamic fields due to the multiple operation conditions pose real challenges as well as the computational cost of the high-fidelity simulations. The developed strategy mitigates these issues by combining traditional surrogate models and machine learning. The central idea is to separate the solutions with a subsonic behavior from the transonic and high-gradient solutions. First, a shock sensor extracts a feature corresponding to the presence of discontinuities, easing the clustering of the simulations by an unsupervised learning algorithm. Second, a supervised learning algorithm divides the parameter space into subdomains, associated to different flow regimes. Local reduced-order models are built on each subdomain using proper orthogonal decomposition coupled with a multivariate interpolation tool. Finally, an improved resampling technique taking advantage of the subdomain decomposition minimizes the redundancy of sampling. The methodology is assessed on the turbulent two-dimensional flow around the RAE2822 transonic airfoil. It exhibits a significant improvement in terms of prediction accuracy for the developed strategy compared with the classical method of surrogate modeling

유해화학물질 확산 분석 및 인공신경망을 사용한 대리 모델 최적화

학위논문 (박사)-- 서울대학교 대학원 공과대학 화학생물공학부, 2017. 8. 한종훈.CFD simulations can be used to estimate the accident consequences by hazardous chemical releases. Both strength and weakness of this method lies on the complexity of the CFD simulation. In other words, CFD can predict chemical dispersion in the complex geometry like urban area, but it takes much computational resources. Comparison between actual dispersion and CFD simulation is done to show usefulness of the CFD simulation. Anhydrous hydrogen fluoride dispersion field test is simulated and it is within the dispersion model criteria. Actual hydrogen fluoride accidental release in 2012 is also simulated and its human and environmental damage prediction is similar to actual accident consequences. Case study of the toxic gas dispersion in the urban area varying source and meteorological condition is conducted, and numerical method is tested for the worst case scenario. Time variant CFL stepping method and multi-threading reduce the calculation time. Also ANN is able to classify the safe and danger based on the input condition. CFD simulation can be applied in the detector allocation problem in the process unit. Simulation inputs are determined by computer experiment procedure and other sets of data detection time is estimated with ANN. Then based on these simulated and estimated data MILP is solved and showed better result than using simulation data alone.CHAPTER 1 : Introduction 1 1.1. Research motivation 1 1.2. Description of CFD model in this thesis 2 1.3. Outline of the thesis 3 Chapter 2: Comparison between CFD model and actual dispersion 4 2.1. Chapter outline 4 2.2. Validation of a CFD model against a field test 4 2.2.1 Validation objective 4 2.2.2. Validation results in literature 5 2.2.3. The Goldfish hydrogen fluoride spill field test 6 2.2.4. Field test validation result 8 2.3. Comparison between a CFD model and an actual accident 14 2.3.1. Accident description 14 2.3.2. The hydrogen fluoride gas leak simulation setting 15 2.3.3. FLACS simulation settings 17 2.3.4. Hydrogen fluoride gas leak simulation result 21 2.3.5. Comparison between the simulation and reported damage 24 2.4. Chapter conclusion 35 Chapter 3: Chlorine dispersion CFD model in an urban area 36 3.1. Chapter outline 36 3.2. Background 37 3.2.1. CFL number 37 3.2.2. Chlorine toxicity calculation 38 3.3. Method 39 3.3.1. Data acquisition 39 3.3.2. CFD simulation setting 43 3.3.3. Time step increase method 45 3.3.4. Classification method 46 3.4. Result and discussion 48 3.4.1. Case study result 48 3.4.2. Time step change result 51 3.4.3. Classification result 56 3.5. Chapter conclusion 60 Chapter 4: Detector allocation optimization using CFD 61 4.1. Background 61 4.1.1. Detector allocation problem 61 4.1.2. Surrogate model with CFD 62 4.2. Method 63 4.2.1. CFD Setting 66 4.2.3. ANN Regression 69 4.2.3. Sensor Allocation 71 4.3. Results 74 4.3.1. ANN results 74 4.3.2. Sensor Allocation Result 77 4.3.3. Discussion 79 4.4. Chapter conclusion 83 Chapter 5: Conclusion 84 5.1. Concluding remarks 84 5.2. Future works 84 Nomenclature 85 Literature cited 87 Abstract in Korean (요 약) 97Docto

A hybrid anchored-ANOVA - POD/Kriging method for uncertainty quantification in unsteady high-fidelity CFD simulations

International audienceTo significantly increase the contribution of numerical computational fluid dynamics (CFD) simulation for risk assessment and decision making, it is important to quantitatively measure the impact of uncertainties to assess the reliability and robustness of the results. As unsteady high-fidelity CFD simulations are becoming the standard for industrial applications, reducing the number of required samples to perform sensitivity (SA) and uncertainty quantification (UQ) analysis is an actual engineering challenge. The novel approach presented in this paper is based on an efficient hybridization between the anchored-ANOVA and the POD/Kriging methods, which have already been used in CFD-UQ realistic applications, and the definition of best practices to achieve global accuracy. The anchored-ANOVA method is used to efficiently reduce the UQ dimension space, while the POD/Kriging is used to smooth and interpolate each anchored-ANOVA term. The main advantages of the proposed method are illustrated through four applications with increasing complexity, most of them based on Large-Eddy Simulation as a high-fidelity CFD tool: the turbulent channel flow, the flow around an isolated bluff-body, a pedestrian wind comfort study in a full scale urban area and an application to toxic gas dispersion in a full scale city area. The proposed c-APK method (anchored-ANOVA-POD/Kriging) inherits the advantages of each key element: interpolation through POD/Kriging precludes the use of quadrature schemes therefore allowing for a more flexible sampling strategy while the ANOVA decomposition allows for a better domain exploration. A comparison of the three methods is given for each application. In addition, the importance of adding flexibility to the control parameters and the choice of the quantity of interest (QoI) are discussed. As a result, global accuracy can be achieved with a reasonable number of samples allowing computationally expensive CFD-UQ analysis. (C) 2016 Elsevier Inc. All rights reserved