Article
Computer Science, Interdisciplinary Applications
Nicolas Delaisse, Toon Demeester, Dieter Fauconnier, Joris Degroote
Summary: This article presents a new framework for incorporating surrogate models in quasi-Newton methods and compares several existing methods with it. This approach can expedite the convergence by providing an initial solution and a Jacobian matrix.
COMPUTERS & STRUCTURES
(2022)
Article
Engineering, Marine
E. Sanmiguel-Rojas, R. Fernandez-Feria
Summary: The numerical analysis of a flexible plunging hydrofoil in a current at Reynolds number 10000 demonstrates propulsion enhancement due to flexibility. Validated with experimental data and linear theory predictions, the study concludes that the linear theory serves as a reliable and useful guide for the design of underwater flexible flapping-foil thrusters.
Article
Materials Science, Multidisciplinary
Debiao Meng, Yan Li, Chao He, Jinbao Guo, Zhiyuan Lv, Peng Wu
Summary: An enhanced CO method based on adaptive surrogate models is proposed to improve the accuracy of the non-linear region of the response surface. The method modifies traditional surrogate models and replaces original objectives and constraints with adaptive surrogate models to enhance the optimization process. This approach demonstrates the effectiveness of CO in engineering structure design problems.
MATERIALS & DESIGN
(2021)
Article
Engineering, Marine
Tobias Martin, Arun Kamath, Gang Wang, Hans Bihs
Summary: The numerical framework of the open source CFD solver REEF3D is used to study the interaction between fluid and structure in an open ocean aquaculture system subjected to waves. A semi-empirical screen force model is used to define the hydrodynamic loads on the net panels, and a simulation-based screen force model is developed to calculate the necessary force coefficients. The proposed net model is validated against measured data and applied to simulate the dynamics of an open ocean aquaculture cage.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Thermodynamics
Mario Javier Rincon, Martino Reclari, Xiang I. A. Yang, Mahdi Abkar
Summary: Computational fluid dynamics is used to predict turbulent flow and perform robust design optimization of domestic ultrasonic flow meters. Surrogate modeling based on Kriging, Latin hypercube sampling, and Bayesian strategies is utilized to ensure high-quality response surface. A novel function is defined to quantify flow meter measurement uncertainty and optimize the pressure drop. The applied methodology improves ultrasonic flow meters and similar internal-flow problems.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW
(2023)
Article
Mechanics
Korosh Khorshidi, Babak Soltannia, Mahdi Karimi, Mahdi Zakaryaei
Summary: This paper investigates the free vibration analysis of a microplate, interacting with a stationary fluid, and adopts the modified strain gradient theory to capture the size effects.
COMPOSITE STRUCTURES
(2023)
Article
Environmental Sciences
Yihuan Yan, Xueren Li, Weijie Sun, Xiang Fang, Fajiang He, Jiyuan Tu
Summary: A semi-surrogate model for CFD with XGBoost was proposed to efficiently solve and predict complex fluid dynamics-related problems. Droplet evaporation was studied using traditional CFD framework and the data generated was used to train XGBoost. The well-trained XGBoost model demonstrated its potential to provide rapid and efficient predictions of complex fluid dynamics-related phenomena.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Engineering, Mechanical
Umberto Alibrandi, Lars V. Andersen, Enrico Zio
Summary: This paper applies information theory to probabilistic sensitivity analysis and surrogate modelling with active learning. It introduces a new measure of dependence between random variables using the informational coefficient of correlation. The paper also presents effective informational sensitivity indices based on mutual information and proposes two novel learning functions for adaptive sampling. Numerical examples demonstrate the features and potential applications of the proposed approach.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Engineering, Ocean
S. A. Brown, N. Xie, M. R. Hann, D. M. Greaves
Summary: Wave-driven hydroelasticity is crucial in offshore and coastal engineering, but its impacts may vary depending on specific cases, highlighting the need for accurate numerical tools. This study provides novel experimental data to aid in the development of a coupled numerical methodology for simulating highly-flexible floating structures' fully nonlinear hydroelastic interactions.
APPLIED OCEAN RESEARCH
(2022)
Article
Engineering, Aerospace
Bei Liu, Hua Liang, Zhong-Hua Han, Guang Yang
Summary: This article proposes a new morphing mechanism for aerodynamic shape design considering both subsonic and hypersonic performance. The optimization design utilizes a surrogate-based optimization algorithm and considers multiple configurations and flow conditions. The results show that the optimized morphing wing exhibits significant aerodynamic performance improvement across different flow regimes.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Marine
Jingru Xing, Songgui Chen, Dimitris Stagonas, Liang Yang
Summary: This study introduces a new three-phase Fluid-Structure Interaction (FSI) model for simulating oil spill containment. The model incorporates Level Sets and spring forces to capture the evolution of interfaces and handle multi-phase deformation. By comparing numerical results with experimental data, the study reveals that the movement of oil spills is dominated by the current when the current exceeds 0.2 m/s, providing important insights for the design of effective containment systems.
Article
Engineering, Mechanical
Da Teng, Yun-Wen Feng, Jun-Yu Chen
Summary: In this study, an intelligent weighted Kriging-based moving extremum framework is developed by incorporating moving least squares thought, Gaussian weight, particle swarm optimization method and Kriging model into extremum response surface method. The effectiveness of the method is demonstrated through the verification of radial deformation of turbine blisk, showing high performance compared to direct simulation, ERSM and traditional Kriging model.
ENGINEERING FAILURE ANALYSIS
(2022)
Article
Engineering, Civil
Pinghe Ni, Jun Li, Hong Hao, Hongyuan Zhou
Summary: This paper presents a reliability-based design optimization method for bridge structures, which considers uncertainties of material parameters and bridge-vehicle interaction. The proposed approach is validated through numerical studies on simply supported beam and box-section bridge, demonstrating its efficiency and accuracy in determining the minimum required cross-section area under probability constraints.
ENGINEERING STRUCTURES
(2021)
Article
Computer Science, Interdisciplinary Applications
Muzaffer Akbay, Craig Schroeder, Tamar Shinar
Summary: The paper presents a boundary pressure projection method to alleviate the incompatibility while addressing the incompressibility dilemma. This method projects incompatible velocities from the structure solver to be compatible and computes constant pressure modes for the Dirichlet regions.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Engineering, Mechanical
Haiqi Feng, Wei Huang, Sihua Deng, Caiyu Yin, Peng Wang, Jiayi Liu
Summary: The cavitation evolution caused by one-dimensional fluid-structure interaction in graded foam core sandwich panels subjected to underwater impulsive loadings is investigated in this paper. It is found that the initiatiAon, extension, and collapse of cavitation are significantly influenced by the propagation of breaking front and closing front, with different correlations to considering effects. The non-uniform distribution of cavitated bubbles indicates that the cavitation region closer to the fluid-solid interface experiences larger pressure drops. The dynamic response of the weakest layer of the core plays a predominant role in the propagation trajectories of wave fronts and the reduction of cavitation ratio.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Nasrulloh Loka, Ivo Couckuyt, Federico Garbuglia, Domenico Spina, Inneke Van Nieuwenhuyse, Tom Dhaene
Summary: Multi-objective optimization of complex engineering systems is a challenging problem. Bayesian optimization is a popular technique to tackle this problem. We develop an approach that can handle a mix of expensive and cheap objective functions, offering lower complexity and superior performance in cases where the cheap objective function is difficult to approximate.
ENGINEERING WITH COMPUTERS
(2023)
Article
Construction & Building Technology
Janez Perko, Eric Laloy, Rafael Zarzuela, Ivo Couckuyt, Ramiro Garcia Navarro, Maria J. Mosquera
Summary: The effectiveness of sol-gel based treatments for concrete protection depends on their ability to penetrate the pores of the material. Optimizing the sol formulation to achieve maximum penetration depth is complex due to the varying influence of sol's physical properties with different concrete pore size distributions. This manuscript presents a combined computational and experimental approach to design impregnation products with optimized penetration depth on concrete with different pore structures. The effectiveness of the approach is demonstrated through three cases, showing significant improvement in penetration compared to traditional methods.
CEMENT & CONCRETE COMPOSITES
(2023)
Article
Geochemistry & Geophysics
Geethika Bhavanasi, Lorin Werthen-Brabants, Tom Dhaene, Ivo Couckuyt
Summary: This paper builds and compares open-set recognition (OSR) systems for patient activity recognition (PAR) using compact radar sensors in a hospital setting. A deep discriminative representation network (DDRN) is trained using large margin cosine loss (LMCL) and triplet loss (TL), and a probability of an inclusion model based on the Weibull distribution is used to separate knowns from unknowns. The proposed approach significantly outperforms state-of-the-art open-set approaches for human activity recognition (HAR) with radar.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Operations Research & Management Science
Jixiang Qing, Ivo Couckuyt, Tom Dhaene
Summary: Bayesian optimization is a popular tool for optimizing objective functions with limited function evaluations. This paper introduces a novel Bayesian optimization framework for multi-objective optimization considering input uncertainty. The framework utilizes a robust Gaussian Process model and a two-stage optimization process to find robust solutions.
JOURNAL OF GLOBAL OPTIMIZATION
(2023)
Article
Materials Science, Textiles
Axel Bral, Lode Daelemans, Joris Degroote
Summary: Textiles and their production machines are increasingly using simulations of the production process, which require reliable structural yarn models. However, these models are often based on simplifying assumptions, and more realistic fiber models have not been used. This paper proposes a new method to obtain a structural yarn model through numerical simulations, using a high-fidelity geometrical yarn model. The method is validated by comparing the results with experimental data.
TEXTILE RESEARCH JOURNAL
(2023)
Review
Computer Science, Interdisciplinary Applications
Nicolas Delaisse, Toon Demeester, Rob Haelterman, Joris Degroote
Summary: Fluid-structure interaction simulations can be performed in a partitioned way to couple a flow solver with a structural solver. However, without additional stabilization efforts, the Gauss-Seidel iterations between the solvers can converge slowly or not at all under common conditions. Quasi-Newton methods can stabilize and accelerate the coupling iterations, while still treating the solvers as black boxes and accessing data only at the fluid-structure interface. This review focuses on reformulating various coupling methods in the generalized Broyden framework to highlight their similarities and differences, and also compares their performance.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Federico Garbuglia, Domenico Spina, Dirk Deschrijver, Ivo Couckuyt, Tom Dhaene
Summary: In microwave design, Bayesian optimization techniques are used to optimize the frequency response of components and devices. This article proposes using a deep Gaussian process to directly model all relevant S coefficients over the frequency and design parameter ranges of interest, leading to increased accuracy in identifying the optimal frequency response.
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES
(2023)
Article
Engineering, Mechanical
Peyman Havaej, Joris Degroote, Dieter Fauconnier
Summary: This paper investigates the effects of double-sided surface waviness on Thermo-Elastohydrodynamic Lubrication (TEHL) in line contacts through numerical simulations. The study examines the surface amplitude, wavelength, relative position, and slide-to-roll ratio. The results show that the classic equivalent deformable body method produces 17%, 21%, and 26% deviations in maximum pressure, temperature, and friction compared to the full two-body simulations. The tangential elastic deformation of surface asperities is identified as the fundamental cause of these deviations, which cannot be described by the equivalent geometry.
TRIBOLOGY INTERNATIONAL
(2023)
Article
Engineering, Mechanical
Peyman Havaej, Joris Degroote, Dieter Fauconnier
Summary: This study compares the film thickness, lubricant temperature, and traction curves of two groups of commonly used constitutive models for lubricants in thermo-elastohydrodynamic lubrication (TEHL) modelling. The results show significant deviations in central film thickness, coefficient of friction (CoF), and maximum lubricant temperature between the different constitutive models. The study highlights the sensitivity of TEHL simulation results to the choice of lubricant constitutive models and the importance of carefully selecting appropriate models for specific applications.
Article
Nuclear Science & Technology
Henri Dolfen, Stefan Vandewalle, Joris Degroote
Summary: The design evaluation of nuclear components using numerical methods typically focuses on ideal conditions, but in reality, the geometry and operating conditions may differ. Understanding and ensuring the safety of nuclear energy systems requires investigating more realistic conditions, such as the deformation of fuel assemblies due to thermal and irradiation effects. A paradigm shift is needed to move from deterministic simulations to simulations involving stochastic processes.
NUCLEAR ENGINEERING AND DESIGN
(2023)
Article
Engineering, Electrical & Electronic
Federico Garbuglia, Torsten Reuschel, Christian Schuster, Dirk Deschrijver, Tom Dhaene, Domenico Spina
Summary: This work presents a machine learning technique using a new Gaussian processes model to accurately model wide-band scattering parameters of interconnects. By employing delay estimation and a physics-informed kernel, the new model accurately predicts S-parameter values and outperforms standard models in terms of accuracy.
IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jixiang Qing, Tom Dhaene, Ivo Couckuyt
Summary: In this paper, we study the inference of mean-variance robustness measures under the Gaussian Process framework. We propose a Spectral Representation of Robustness Measures based on the GP's spectral representation and apply them to robust Bayesian Optimization. The results demonstrate their competitive performance on numerical benchmarks and real-life applications.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162
(2022)
