Flow Science and Engineering

Faculty of Mechanical and Aerospace Engineering ITB

Research

Flow Science and Engineering Laboratory focuses on four different subjects of research including computational aeroscience, data-driven fluid mechanics with machine learning, experimental fluid mechanics, and intelligent computer vision. Computational aeroscience utilizes existing CFD software in analyzing flow properties and develops in-house solver for fluid problems. Data-driven fluid mechanics has become a topic of interest since the first time, where a variety of sophisticated methods such as Physics Informed Neural Network and Gaussian Process have been implemented on thermo-fluid and solid mechanics problems. Experimental fluid mechanics practice experiments on various flow visualization and analysis, as well as biomimetic propulsion system. Research topics on computer vision focused on the explainability of machine learning-based classifications in engineering and medical applications.

  • Computational Aeroscience
  • Experimental Fluid Mechanics
  • Data Driven Fluid Mechanics with Machine Learning
  • Intelligent Computer Vision

    • ——————————————————————————————————————————————————————————————————————————————————————————-

    Latest Publications:

    2024

    1. Dung, D.V.,Luc, N.V., Tien, N.V.., Palar, S.P., Zuhal, L.R., Thuc, N.T., Dinh, C.T., Wang, W.C., A Numerical Study on Dynamic Flows past Three Tandem Inclined Elliptic Cylinders Near Moving Wall , Physics of Fluids, 2024, 36(2), 023615
    2. Wiragunarsa, I.M., Zuhal, L.R., Dirgantara, T., Putra, I.S., Contact framework for total Lagrangian smoothed particle hydrodynamics using an adaptive hybrid kernel scheme, International Journal for Numerical Methods in Engineering, 2024, 125(7), e7431
    3. Dung, D.V.,Luc, N.V., Tien, N.V., Palar, S.P., Zuhal, L.R., Tuan, L.A., Lin, J.K., Wang, W.C., Near-Moving-Wall Flows Past Three Tandem Elliptical Cylinders at Low Reynolds Number of 150, Physics of Fluids, 2024, 36(1), 013612
    4. Palar, P.S., Dwianto, Y.B., Zuhal, L.R, Morlier, J., Shimoyama, K., Obayashi, S., Multi-objective design space exploration using explainable surrogate models, Structural and Multidisciplinary Optimization, 2024, 67(3), 38
    5. Dwianto, Y.B.,Palar,P.S., Zuhal, L.R., Oyama, A., On the Advantages of Searching Infeasible Regions in Constrained Evolutionary-based Multi-Objective Engineering Optimization, Journal of Mechanical Design, 2024, 146(4), 041701
    6. Wiragunarsa, I.M., Zuhal, L.R., Dirgantara, T., Putra, I.S., SPH Method for Crack Growth Modelling using Particle Deletion and Interaction Pair-based Framework, Procedia Structural Integrity, 2024, 52, pp. 583–593

    ———————————————————————————————————————————————————————————————————————————————————————————–

    2023

    1. Palar, P.S.,Zuhal, L.R., Shimoyama, K., Global Sensitivity Analysis in Aerodynamic Design using Shapley Effects and Polynomial Chaos Regression, IEEE Access, 2023
    2. Zuhal, L.R.,Faza, G.A.,Palar, P.S.,Liem, R.P., Performance assessment of Kriging with partial least squares for high-dimensional uncertainty and sensitivity analysis, Structural and Multidisciplinary Optimization, 2023, 66(5), 115
    3. Firdaus, A, Luc, N.V., Zuhal, L.R., Investigation of the flow around two tandem rotated square cylinders using the least square moving particle semi-implicit based on vortex particle method, Physics of Fluids, 2023, 35(2), 027117
    4. Palar, P.S., Parussini, L., Bregant, L., Shimoyama, K., Zuhal, L.R., On kernel functions for bi-fidelity Gaussian process regressions, Structural and Multidiciplinary Optimization, 2023, 66(2), 37
    5. Palar, P.S., Zuhal, L.R., Shimoyama, K., Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations, Reliability Engineering & System Safety, Vol.232, 2023, 109045
    6. Palar, P.S., Zuhal, L.R., Shimoyama, K.,Dwianto, Y.B., Morlier, J., Shapley Additive Explanations for Knowledge Discovery via Surrogate Models, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2023, AIAA 2023-0332
    7. Palar, P.S., Stevenson, R., Amalinadhi,C., Zakaria, K. and Zuhal,L.R., Data-driven Surrogate Modeling using Deep Learning for Uncertainty Quantification of Random Fields, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2023, AIAA 2023-2044
    8. Zakaria, K., Palar, P.S., Zuhal,L.R.,Morlier, J., Physics-Informed Proper Orthogonal Decomposition for Data Reconstruction, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2023, AIAA 2023-0538
    9. Dung, D.V., Song, N.D., Palar, P.S., Zuhal,L.R., On The Choice of Activation Functions in Physics-Informed Neural Network for Solving Incompressible Fluid Flows, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2023, AIAA 2023-1803
    10. Palar, P.S., Aziz, M.A., Zuhal,L.R.,Sambegoro, P.L., Dung, D.V., Using Physics-Informed Neural Networks to Solve Inverse Heat Conduction Problems, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2023, AIAA 2023-0537

    ——————————————————————————————————————————————————————————————————————————————————————————–

    2022

    1. Fathurrohim,L.,Zuhal, L.R.,Palar, P.S.,Dwianto, Y.B., Maximizing the thrust performance of flexible caudal fin panels via experimental optimization. Ocean Engineering, 2022, 266, 112969
    2. Peeters, H.H.,Judith, E.T.,Silitonga, F.Y.,Zuhal, L.R., Visualizing the velocity fields and fluid behavior of a solution using artificial intelligence during EndoActivator activation. Dental Journal, 2022, 55(3), pp. 125–129
    3. Adnel, C., Zuhal, L.R., Discretization Corrected Particle Strength Exchange for Steady State Linear Elasticity. Journal of Engineering and Technological Sciences, 2022, 54(4)
    4. Duong, V.D., Zuhal, L.R., Vortex particle method with iterative Brinkman penalization for simulation of flow past sharp-shape bodies. International Journal of Micro Air Vehicles, 2022, 14
    5. Peeters, H.H., Silitonga, F., Zuhal, L.R., Application of artificial intelligence in a visual-based fluid motion estimator surrounding a vibrating EDDY® tip. Giornale Italiano di Endodonzia, 2022, 36(1), pp. 151–159
    6. Putra, C.A., Palar, P.S., Stevenson, R., Zuhal, L.R., On Physics-Informed Deep Learning for Solving Navier-Stokes Equations. American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2022, AIAA 2022-1436
    7. Izzaturrahman, M.F., Palar, P.S., Zuhal, L.R., Shimoyama, K., Modeling Non-Stationarity with Deep Gaussian Processes: Applications in Aerospace Engineering. American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2022, AIAA 2022-1096
    8. Palar, P.S., Parussini, L., Bregant, L., Baehaqi, F.A., Zuhal, L.R., Composite Kernel Functions for Surrogate Modeling using Recursive Multi-Fidelity Kriging. American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2022, AIAA 2022-0506
    9. Nasution, M.R.E., Palar, P.S., Hadi, B.K., Zuhal, L.R., Yudhanto, A., Uncertainty Quantification and Sensitivity Analysis for In-plane Thermo-mechanical Properties of 3-D Textile Composites. American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, 2022, AIAA 2022-1435

    ———————————————————————————————————————————————————————————————————————————————————————————-

    2021

    1. Wiragunarsa, I.M., Zuhal, L.R., Dirgantara, T. and Putra, I.S., A particle interaction-based crack model using an improved smoothed particle hydrodynamics for fatigue crack growth simulations. International Journal of Fracture 2021, 229(2), pp.229-244
    2. Zuhal, L. R., Faza, G. A., Palar, P. S., & Liem, R. P., On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning. Reliability Engineering & System Safety, 2021, 215, 1078484
    3. Zuhal, L. R., Zakaria, K., Palar, P. S., Shimoyama, K., Liem, R. P., Polynomial-Chaos–Kriging with Gradient Information for Surrogate Modeling in Aerodynamic Design. AIAA Journal, 2021, 59(8), pp.2950-2967
    4. Jim, T., Faza, G. A., Palar, P. S., Shimoyama, K., Bayesian Optimization of a Low-Boom Supersonic Wing Planform. AIAA Journal, 2021, 59(11), pp.4514-4529
    5. Duong, V.D., Zuhal, L.R., Muhammad, H., Fluid–structure Coupling in Time Domain for Dynamic Stall using Purely Lagrangian Vortex Method, CEAS Aeronautical Journal, 2021, 12(2), pp.381-399
    6. Zuhal, L.R., Faza, G.A., Palar, P.S., Liem, R., Fast and Adaptive Reliability Analysis via Kriging and Partial Least Squares, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Jan. 2021
    7. Palar, P.S., Zakaria, K., Zuhal, L.R., Shimoyama, K., Gaussian Processes and Support Vector Regression for Uncertainty Quantification in Aerodynamics, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Jan. 2021
    8. Nathan, Palar, P.S., Zuhal,L.R., A Multi-objective Approach for Robust Structural Topology Optimization, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Jan. 2021
    9. Robani, M.D., Palar, P.S., Zuhal, L.R., Heteroscedastic Gaussian Process Regression using Nearest Neighbor Point Estimates, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Jan. 2021

    ———————————————————————————————————————————————————————————————————————————————————————————

    2020

    1. Palar, P.S., Shimoyama, K., Zuhal, L.R., Uncertainty quantification methods for evolutionary optimization under uncertainty, Genetic and Evolutionary Computation Conference (GECCO) 2020, Cancún Mexico, July 2020
    2. Palar,P.S., Zuhal,L.R., Shimoyama, K. Gaussian Process Surrogate Model with Composite Kernel Learning for Engineering Design, AIAA Journal, Vol. 58(4), pp 1864–1880, 2020
    3. Palar, P.S., Zuhal, L.R., Chugh, T., Rahat, A., On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Orlando, USA, Jan. 2020
    4. Zuhal, L.R., Zakaria, K., Palar, P.S., Shimoyama, K., Liem, R., Gradient-Enhanced Universal Kriging with Polynomial Chaos as Trend Function, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Orlando, USA, Jan. 2020
    5. Palar, P.S., Zuhal, L.R., Chugh, T., Rahat, A., On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Orlando, USA, Jan. 2020

    ——————————————————————————————————————————————————————————————————————————————————————————-

    2019

    1. Chugh, T., Rahat, A., Palar, P.S., Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels, The Fifth International Conference on Machine Learning, Optimization, and Data Science, Siena, Italy, Sep. 2019
    2. Zuhal, L.R., Palar, P.S., Shimoyama, K., A Comparative Study of Multi-objective Expected Improvement for Aerodynamic Design, Aerospace Science and Technology, Vol. 91, pp 548-560, 2019.
    3. Palar, P.S., Zuhal, L.R., Shimoyama, K., On the use of Metaheuristics in Hyperparameters Optimization of Gaussian Processes, Genetic and Evolutionary Computation Conference (GECCO) 2019, Prague, Czech Republic, July 2019
    4. Yang, K., Palar, P.S., Emmerich, M., Shimoyama, K., Bäck, T., On the use of Metaheuristics in Hyperparameters Optimization of Gaussian Processes, Genetic and Evolutionary Computation Conference (GECCO) 2019, Prague, Czech Republic, July 2019
    5. Palar, P.S., Zuhal, L.R., Liem, R.P., Shimoyama, K. On the use of Surrogate Models in Engineering Design Optimization and Exploration: The Key Issues, Genetic and Evolutionary Computation Conference (GECCO) 2019, Prague, Czech Republic, July 2019
    6. Palar, P.S., Dwianto, Y.B., Regis, R.G., Oyama, A., Zuhal, L.R., Benchmarking Constrained Surrogate-based Optimization on Low Speed Airfoil Design problems, Genetic and Evolutionary Computation Conference (GECCO) 2019, Prague, Czech Republic, July 2019
    7. Zuhal, L.R., Faza, G.A., Palar, P.S., Shimoyama, K., Multi-objective kriging-based optimization for high-fidelity wind turbine design, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, San Diego, USA, Jan. 2019

    ——————————————————————————————————————————————————————————————————————————————————————————-

    2018

    1. Zuhal, L.R., Amalinadhi, C., Dwianto, Y.B., Palar, P.S., Shimoyama, K., Benchmarking Multi-Objective Bayesian Global Optimization Strategies for Aerodynamic Design, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, Kissimmee, USA, Jan. 2018
    2. Palar, P.S., Zuhal, L.R., Shimoyama, K., Tsuchiya, T., Global sensitivity analysis via multi-fidelity polynomial chaos expansion, Reliability Engineering & System Safety, Vol.170, pp 175-190, 2018

    ——————————————————————————————————————————————————————————————————————————————————————————

    2017

    1. Febrianto, E.V., Zuhal, L.R., Vortex In Cell Method to Predict Flutter Phenomenon of 2D Bridge Deck Model, ARPN Journal of Engineering and Applied Sciences, Vol. 12, No. 10, pp. 3040-3045, 2017
    2. Dwianto, Y. B, Palar, P.S., Zuhal, L.R., Robust Optimization of Wind Turbine‘s Airfoil Under Geometric Uncertainty Using Surrogate Assisted Memetic Algorithm, 3rd International Conference on Electrical, Mechanical, and Industrial Engineering (ICEMIE), Mar. 2017

    ———————————————————————————————————————————————————————————————————————————————————————————

    2016

    1. Luc, N.V., Zuhal, L.R., Uchiyama, T., Numerical Simulation of Flow around Two Tandem Cylinders by Vortex In Cell Method Combined with Immersed Boundary Method, Advances and Applications in Fluid Mechanics, Vol. 19, Issue 4, pp. 787-810, 2016
    2. Luc, N.V., Zuhal, L.R., Uchiyama, T., Simulation of Flow around Two Cylinders in Tandem Arrangement by Vortex In Cell Method Combined with Immersed Boundary Method, 7th International Conference on Vortex Flows and Vortex Models, Sep. 2016
    3. Palar, P.S., Dwianto, Y.B., Zuhal, L.R., Tsuchiya, T., Framework for Robust Optimization Combining Surrogate Model, Mimetic Algorithm, and Uncertainty Quantification, Lecture Notes in Computer Science, Vol. 9712, pp 48-55, 2016
    4. Canh, C.X., Zuhal, L.R., Muhammad, H., Numerical Simulation of 2D Flow around Deforming Fish-like Body, Applied Mechanics and Materials, Vol. 842, pp. 228-232, 2016
    5. Tien, N.V., Canh, C.X., Zuhal, L.R., Smooth Particle Hydrodynamic (SPH) for Simulating 2D Elastodynamics Problems, Applied Mechanics and Materials, Vol. 842, pp. 127-131, 2016

    ————————————————————————————————————————————————————————————————————————————————————————————

    2015

    1. Dung, D. V., Zuhal, L.R., Muhammad, H., Two-dimensional Fast Lagrangian Vortex Method for Simulating Flows around a Moving Boundary, Journal of Mechanical Engineering, Vol. 12, No.1, 2015

    ————————————————————————————————————————————————————————————————————————————————————————————

    2014

    1. Zuhal, L.R., Dung, D. V., Muhammad, H., Core Spreading Vortex Method for Simulating 3D Flows around Bluff Bodies, Journal of Engineering and Technological Sciences, Vol. 46, No. 4, 2014
    2. Zuhal, L.R., Febrianto, E.V., Dung, D.V., Flutter Speed Determination of Two Degree of Freedom Model using Discrete Vortex Method, Applied Mechanics and Materials, Vol. 660, 2014
    3. Zuhal, L.R., Dwianto, Y.B., Palar, P.S., Evolutionary Algorithm Based Multi-objective Aerodynamic Optimization Method for Low Reynolds Number Airfoil, Applied Mechanics and Materials, Vol. 660, 2014
    4. Widodo, A. F., Zuhal, L.R., Muhammad, H., Simulation of Flow around a Flapping Wing Using Two-dimensional Vortex Methods, Journal of Mechanical Engineering, Vol. 10, No.2, 2014
    5. Palar, P.S., Zuhal, L.R., Flow Field around Asymmetric Flapping Flat Plate Optimized Using Micro Genetic Algorithm, American Institute of Aeronautics and Astronautics (AIAA) Science and Technology Forum and Exposition, National Harbor, USA, Jan. 2014