arXiv subjects

Joris Degroote

Publications and source records attributed to Joris Degroote.

10 recordsLinked to original sources

arXiv2025

Characterisation and extension of a rigid body dynamics solver coupled with OpenFOAM for flight performance analysis of flapping-wing drones

The extraordinary aerial agility of hummingbirds and insects continues to inspire the design of flapping-wing drones. To replicate and analyze such flight, computational fluid dynamics (CFD) simulations that couple flow solvers with rigid body dynamics are essential. While OpenFOAM offers tools for these multiphysics simulations, two key limitations remain: (1) a lack of thorough verification and performance characterization, and (2) the reliance on torque-based control for wing motion, which is impractical for parametric studies and real-time control. The developments are tested with a four and a five degrees of freedom flapping-wing drone equipped with a rigid, semi-elliptical wing. Ascending flight motions are simulated using the overset method, a moving background grid, and an LES model. Parametric studies demonstrate the independence of the grid and integration schemes, while profiling analyses identify the overset method as the computational bottleneck. The drone trajectories are compared with those from a literature quasi-steady model, and the body-wing interaction is analyzed in detail.

physics.flu-dyn↗

arXiv2025

Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones

Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data. Recent advances in artificial intelligence (AI), particularly reinforcement learning (RL), have opened new perspectives for addressing such complex control problems through data-driven policy optimization from interaction with the environment. Yet purely data-driven methods are sample-inefficient, demanding extensive, sometimes unsafe exploration, especially without guiding physical models. This motivates hybrid AI-physics frameworks. This article proposes a hybrid model-free/model-based flight-control approach using the reinforcement twinning algorithm. The model-based (MB) component uses an adjoint formulation and an adaptive digital twin continuously identified from live trajectories; the model-free (MF) component uses RL. The two agents share knowledge via transfer learning, imitation learning, and shared experience between the real environment and the digital twin, coordinated by a policy referee that selects which agent acts in reality based on digital-twin performance and a real-to-virtual consistency ratio. The framework is evaluated for the longitudinal control of a flapping-wing drone, modelled as a nonlinear time-varying system driven by quasi-steady aerodynamic forces. The hybrid strategy is tested under three adaptive-model initializations: (1) offline identification from existing data, (2) random initialization with fully online identification, and (3) offline pre-training with biased parameters followed by online adaptation. In all cases, the hybrid framework improves performance, robustness, and sample efficiency over purely model-free and purely model-based approaches.

cs.RO↗

arXiv2024

A Two-Phase Flow Solver with Variable Liquid Compressibility and Temperature Equation for Partitioned Simulation of Elastohydrodynamic Lubrication

This paper presents a new solver developed in OpenFOAM for the modeling of lubricant in the narrow gap between two surfaces inducing hydrodynamic pressures up to few gigapascal. Cavitation is modeled using the homogeneous equilibrium model. The mechanical and thermodynamic constitutive behavior of the lubricant is accurately captured by inclusion of compressibility, lubricant rheology and thermal effects. Different constitutive models can be selected at run time, through the adoption of the modular approach of OpenFOAM. By combining the lubricant solver with a structural solver using a coupling tool, elastohydrodynamically lubricated contacts can be accurately simulated in a partitioned way. The solution approach is validated and examples with different slip conditions are included. The benefit for the OpenFOAM community of this work is the creation of a new solver for lubricant flow in challenging conditions and at the same the illustration of combining OpenFOAM solvers with other open-source software packages.

cs.CE↗

arXiv2024

Introducing DAIMYO: a first-time-right dynamic design architecture and its application to tail-sitter UAS development

In recent years, there has been a notable evolution in various multidisciplinary design methodologies for dynamic systems. Among these approaches, a noteworthy concept is that of concurrent conceptual and control design or co-design. This approach involves the tuning of feedforward and/or feedback control strategies in conjunction with the conceptual design of the dynamic system. The primary aim is to discover integrated solutions that surpass those attainable through a disjointed or decoupled approach. This concurrent design paradigm exhibits particular promise in the context of hybrid unmanned aerial systems (UASs), such as tail-sitters, where the objectives of versatility (driven by control considerations) and efficiency (influenced by conceptual design) often present conflicting demands. Nevertheless, a persistent challenge lies in the potential disparity between the theoretical models that underpin the design process and the real-world operational environment, the so-called reality gap. Such disparities can lead to suboptimal performance when the designed system is deployed in reality. To address this issue, this paper introduces DAIMYO, a novel design architecture that incorporates a high-fidelity environment, which emulates real-world conditions, into the procedure in pursuit of a `first-time-right' design. The outcome of this innovative approach is a design procedure that yields versatile and efficient UAS designs capable of withstanding the challenges posed by the reality gap.

math.OC↗

arXiv2024

On the unsteady aerodynamics of flapping wings under dynamic hovering kinematics

Hummingbirds and insects achieve outstanding flight performance by adapting their flapping motion to the flight requirements. Their wing kinematics can change from smooth flapping to highly dynamic waveforms, generating unsteady aerodynamic phenomena such as leading-edge vortices (LEV), rotational circulation, wing wake capture, and added mass. This article uncovers the interactions of these mechanisms in the case of a rigid semi-elliptical wing undergoing aggressive kinematics in the hovering regime at $Re\sim \mathcal{O}(10^3)$. The flapping kinematics were parametrized using smoothed steps and triangular functions and the flow dynamics were simulated by combining the overset method with Large Eddy Simulations (LES). The analysis of the results identifies an initial acceleration phase and a cruising phase. During the former, the flow is mostly irrotational and governed by the added mass effect. The added mass was shown to be responsible for a lift first peak due to the strong flapping acceleration. The dynamic pitching and the wing wake interaction generate a second lift peak due to a downwash flow and a vortex system on the proximal and distal parts of the wing's pressure side. Conversely, aerodynamic forces in the cruising phase are mainly governed by the growth and the establishment of the LEV. Finally, the leading flow structures in each phase and their impact on the aerodynamic forces were isolated using the extended Proper Orthogonal Decomposition (POD).

physics.flu-dyn↗

arXiv2023

On the number of subproblem iterations per coupling step in partitioned fluid-structure interaction simulations

In literature, the cost of a partitioned fluid-structure interaction scheme is typically assessed by the number of coupling iterations required per time step, while ignoring the internal iterations within the nonlinear subproblems. In this work, we demonstrate that these internal iterations have a significant influence on the computational cost of the coupled simulation. Particular attention is paid to how limiting the number of iterations within each solver call can shorten the overall run time, as it avoids polishing the subproblem solution using unconverged coupling data. Based on systematic parameter studies, we investigate the optimal number of subproblem iterations per coupling step. Lastly, this work proposes a new convergence criterion for coupled systems that is based on the residuals of the subproblems and therefore does not require any additional convergence tolerance for the coupling loop.

math.NA↗

arXiv2021

A novel iterative penalty method to enforce boundary conditions in Finite Volume POD-Galerkin reduced order models for fluid dynamics problems

A Finite-Volume based POD-Galerkin reduced order model is developed for fluid dynamics problems where the (time-dependent) boundary conditions are controlled using two different boundary control strategies: the lifting function method, whose aim is to obtain homogeneous basis functions for the reduced basis space and the penalty method where the boundary conditions are enforced in the reduced order model using a penalty factor. The penalty method is improved by using an iterative solver for the determination of the penalty factor rather than tuning the factor with a sensitivity analysis or numerical experimentation. The boundary control methods are compared and tested for two cases: the classical lid driven cavity benchmark problem and a Y-junction flow case with two inlet channels and one outlet channel. The results show that the boundaries of the reduced order model can be controlled with the boundary control methods and the same order of accuracy is achieved for the velocity and pressure fields. Finally, the reduced order models are 270-308 times faster than the full order models for the lid driven cavity test case and 13-24 times for the Y-junction test case.

math.NA↗

arXiv2021

Reduced order models for the incompressible Navier-Stokes equations on collocated grids using a 'discretize-then-project' approach

A novel reduced order model (ROM) for incompressible flows is developed by performing a Galerkin projection based on a fully (space and time) discrete full order model (FOM) formulation. This 'discretize-then-project' approach requires no pressure stabilization technique (even though the pressure term is present in the ROM) nor a boundary control technique (to impose the boundary conditions at the ROM level). These are two main advantages compared to existing approaches. The fully discrete FOM is obtained by a finite volume discretization of the incompressible Navier-Stokes equations on a collocated grid, with a forward Euler time discretization. Two variants of the time discretization method, the inconsistent and consistent flux method, have been investigated. The latter leads to divergence-free velocity fields, also on the ROM level, whereas the velocity fields are only approximately divergence-free in the former method. For both methods, accurate results have been obtained for test cases with different types of boundary conditions: a lid-driven cavity and an open-cavity (with an inlet and outlet). The ROM obtained with the consistent flux method, having divergence-free velocity fields, is slightly more accurate but also slightly more expensive to solve compared to the inconsistent flux method. The speedup ratio of the ROM and FOM computation times is the highest for the open cavity test case with the inconsistent flux method.

physics.flu-dyn↗

arXiv2020

Development of a coupling between a system thermal-hydraulic code and a reduced order CFD model

The nuclear community has coupled several three-dimensional Computational Fluid Dynamics (CFD) solvers with one-dimensional system thermal-hydraulic (STH) codes. This work proposes to replace the CFD solver by a reduced order model (ROM) to reduce the computational cost. The system code RELAP5-MOD3.3 and a ROM of the finite volume CFD solver OpenFOAM are coupled by a partitioned domain decomposition coupling algorithm using an implicit coupling scheme. The velocity transported over a coupling boundary interface is imposed in the ROM using a penalty method. The coupled models are evaluated on open and closed pipe flow configurations. The results of the coupled simulations with the ROM are close to those with the CFD solver. Also for new parameter sets, the coupled RELAP5/ROM models are capable of predicting the coupled RELAP5/CFD results with good accuracy. Finally, coupling with the ROM is 3-5 times faster than coupling with the CFD solver.

physics.flu-dyn↗

arXiv2020

A POD-Galerkin reduced order model of a turbulent convective buoyant flow of sodium over a backward-facing step

A Finite-Volume based POD-Galerkin reduced order modeling strategy for steady-state Reynolds averaged Navier--Stokes (RANS) simulation is extended for low-Prandtl number flow. The reduced order model is based on a full order model for which the effects of buoyancy on the flow and heat transfer are characterized by varying the Richardson number. The Reynolds stresses are computed with a linear eddy viscosity model. A single gradient diffusion hypothesis, together with a local correlation for the evaluation of the turbulent Prandtl number, is used to model the turbulent heat fluxes. The contribution of the eddy viscosity and turbulent thermal diffusivity fields are considered in the reduced order model with an interpolation based data-driven method. The reduced order model is tested for buoyancy-aided turbulent liquid sodium flow over a vertical backward-facing step with a uniform heat flux applied on the wall downstream of the step. The wall heat flux is incorporated with a Neumann boundary condition in both the full order model and the reduced order model. The velocity and temperature profiles predicted with the reduced order model for the same and new Richardson numbers inside the range of parameter values are in good agreement with the RANS simulations. Also, the local Stanton number and skin friction distribution at the heated wall are qualitatively well captured. Finally, the reduced order simulations, performed on a single core, are about $10^5$ times faster than the RANS simulations that are performed on eight cores.

physics.flu-dyn↗