Doctoral Thesis Machine learning in Industrial Turbomachinery: development of new framework for design, analysis and optimization PhD Candidate: Supervisor: Gino Angelini Prof. Alessandro Corsini A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Industrial and Management Engineering in October 2019 iii SAPIENZA UNIVERSITÀ DI ROMA Abstract Department of Mechanical and Aerospace Engineering Doctor of Philosophy Machine learning in Industrial Turbomachinery: development of new framework for design, analysis and optimization by Gino Angelini In the age of Industry4.0, turbomachinery community is facing a significant effort in re- thinking all manufacturing activities. A new industrial paradigm is developing, which encourages the introduction of Big-Data, Internet of Things (IoT) and Artificial Intel- ligence (AI) into the production and manufacturing environment to follow the market trend of innovation and strong customization of product. This thesis aims to improve the classical approach to turbomachinery design, analysis and optimization, which relays to empiric procedures or computational intensive CFD based optimizations, implementing new framework and strategies based on the use of Machine Learning and Deep Learning, the successful sub-fields of AI, recognized as crucial technologies to stay competitive in this dynamic landscape. The introduction of learning algorithms changes the global structure of classic approach determining modern design support tools. The introduction of regression models facilitates the exploration of design space providing easy-to-run analytical mod- els. Regression models can also be used as cost functions in multi-objective optimization problems avoiding the use of time consuming simulations. The application of unsupervised learning methods to turbomachine performance data extracts new insights to improve the designer understanding of blade-flow interaction and allows a better formulation of spe- cific problem. The design space exploration artificial neural network based allowed the creation of multi-dimensional Balje charts, enriched using correlations between the main interesting geometric parameters and the rotor performance, which guide designer in pre- liminary blade geometry selection. Surrogate model based optimization allowed reducing the computational cost required by canonical approach producing reliable set of better solu- tions in comparison with the standard optimization. Application of unsupervised learning techniques allows developing a new parametrization strategy based on statistical shape representation of complex three dimensional surfaces. This work can be ideally divided into two parts. The first reviews the state-of-the-art of artificial intelligence techniques highlighting the role of machine learning in supporting design, analysis and optimization for turbomachinery community. Second part reports the development of a set of strategies to exploit metamodeling algorithms improving the classical design approach. v Publications • Angelini G., Corsini A., Delibra G., Tieghi L., "A Multidimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence-Based Meta- Models." Journal of Engineering for Gas Turbines and Power 141.11, 2019. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., Venturini P., "A Methodology for Optimal Aerofoil Selection for Axial Fans Subjected to particle- laden flows“, abstract submitted to ETC 2019. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., "Application of IA-driven turbulence modelling to NACA aerofoil with leading edge bumps to control stall dynamics “, abstract submitted to ETC 2019. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., "On Surrogate- Based Optimization of Truly Reversible Blade Profiles for Axial Fans." Designs 2.2: 19, 2019. • Angelini G., Corsini A., Delibra G., Giovannelli M., Lucherini G., Minotti S., Rossin S., Tieghi L. "Meta-modelling of gas-leak in gas turbine enclosures”. ASME GT2019- 91198, 2019. • Angelini G., Corsini A., Delibra G., Giovannelli M., Lucherini G., Minotti S., Rossin S., Tieghi L. "Identification of poorly ventilated zones in gas-turbine enclosures with machine learning." ASME GT2019-91199, 2019. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., “A metamodel for deviation in 2D cascade with variable stagger, solidity and reversible profiles”, ASME IGTI TurboExpo 2018, June 11-15, Oslo, Norway, GT2018-76363. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., “Effects of fan inflow distortion on heat exchange in air-cooled condenser. Unsteady computa- tions with synthetic blade model”, ASME IGTI TurboExpo 2018, June 11-15, Oslo, Norway, GT2018-76518. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., “Noise Re- duction Of A Large Axial Flow Fan For Csp Air-Cooled Condensers”, submitted at FAN, International Conference on Fan Noise, Aerodynamics, Applications and Systems 2018, April 18-20, Darmstadt, Geramny. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., “Rapid Pro- totyping And Testing Of A Small Scale Fan For Csp Power Plant Applications”, submitted at FAN, International Conference on Fan Noise, Aerodynamics, Applica- tions and Systems 2018, April 18-20, Darmstadt, Geramny. • Angelini G., Bonanni T., Corsini A., Delibra G., Tieghi L., Volponi D., “Optimization of an axial fan for air cooled condensers”, ATI 2017. • Angelini G., “A tool for preliminary design and selection of industrial fans”, student poster for IGTI ASME Turbo Expo 2017 vii Contents Abstract iii Introduction 1 1 Learning in turbomachinery 5 1.1 Fundamentals and classification . .5 1.1.1 Artificial Intelligence . .5 1.1.2 Machine Learning . .6 1.1.3 Deep Learning . .7 1.2 Why Today? . .9 1.2.1 Dataset . 10 1.2.2 Approximation model accuracy . 11 1.2.3 A review of machine learning applications . 11 2 Turbomachinery machine-learnt design 13 2.1 Classical turbomachinery design approach . 13 2.1.1 Flow chart of classical design approach . 13 2.1.2 Blade geometry and design parameters . 16 2.1.3 Axisymmetric flow solver . 17 2.2 Role of machine learning . 17 2.2.1 Design Space . 19 2.2.2 Design and Optimization . 21 2.2.3 Statistical Shape Modelling . 24 3 Machine learning algorithms 25 3.1 Introduction . 25 3.2 Classification . 26 3.3 Regression . 26 3.3.1 Least Square Method . 28 3.3.2 Ensemble Methods . 29 3.3.2.1 Random Forest . 30 3.3.2.2 Gradient Boosting . 31 3.3.3 Deep Learning . 33 3.3.3.1 Anatomy of Multi-layer Perceptron . 34 3.3.3.2 Activation function . 35 3.3.3.3 Loss function . 36 3.3.3.4 Optimizer . 36 3.4 Dimensionality reduction . 37 3.4.1 Principal Components Analysis . 38 3.4.2 Projection to Latent Structures . 39 3.5 Clustering . 40 3.5.1 k-Means . 40 3.5.2 DBSCAN . 41 viii Contents 3.6 Model Evaluation . 41 3.6.1 Training and Validation . 42 3.6.2 Overfitting and Underfitting . 43 3.7 Problem-specific data flow . 44 4 Relevance of data treatment 47 4.1 Importance of Data . 47 4.2 Normalization . 48 4.3 Exploratory Data Analysis . 48 4.3.1 Removing Outliers: Box and Whiskers Plot . 49 4.3.2 Distribution Shape Improvement . 49 4.3.2.1 Data Skewness . 50 4.3.2.2 Transformation Functions . 50 4.4 Data dimension . 51 4.4.1 Correlation Matrix Analysis . 52 5 Multi-Dimensional Extension of Balje Chart for Axial Flow Turboma- chinery Machine Learning Based 53 5.1 Introduction . 53 5.2 Methodology . 53 5.2.1 Dataset: Axial flow fan population . 53 5.2.2 Big Data Analysis . 54 5.3 Results . 56 5.3.1 PCA Analysis . 56 5.3.2 PLS Analysis . 56 5.3.3 Multidimensional Balje Charts . 60 5.4 Conclusions . 64 5.5 Annex A . 65 5.5.1 Design space . 65 5.5.2 Data sampling . 65 5.5.3 Numerical model . 66 5.5.4 Artificial Neural Network . 67 5.5.5 Improved solver validation . 68 6 Surrogate Model Based Optimization of Truly Reversible Fans 71 6.1 Introduction . 71 6.2 Multi Objective Optimization with Evolutionary Algorithms . 72 6.2.1 Objective Functions . 72 6.2.2 Aerofoil parametrization . 73 6.2.3 Optimization algorithm . 74 6.2.4 Test matrix . 75 6.3 Surrogate Model-Based Optimizations . 75 6.3.1 Data Sampling . 75 6.3.2 Least Square Method . 75 6.3.3 Artificial Neural Network . 76 6.3.4 Pareto Front Properties . 76 6.4 Results . 76 6.4.1 MOEA . 77 6.4.2 No Evolution Control (NEC) or single level approach . 77 6.4.3 Indirect Fitness Replacement (IFR) or Bi-level approach . 80 6.5 Conclusions . 84 Contents ix 7 Machine-learnt topology of complex tip geometries in gas turbine rotors 87 7.1 Introduction . 87 7.2 Case study . 87 7.3 Dataset Pre-processing . 88 7.3.1 Dataset quality . 88 7.3.2 Dataset dimension . 89 7.4 Statistical model of the tip shape . 89 7.5 Pareto front exploration methodology . 91 7.5.1 Metamodeling performance prediction . 92 7.6.