Multiphysics Systems Research Articles

Genetic Algorithm-Based Optimal Design of a Rolling-Flying Vehicle

Abstract This work describes a design optimization framework for a rolling-flying vehicle consisting of a conventional quadrotor configuration with passive wheels. For a baseline comparison, the optimization approach is also applied for a conventional (flight-only) quadrotor. Pareto-optimal vehicles with maximum range and minimum size are created using a hybrid multi-objective genetic algorithm in conjunction with multi-physics system models. A low Reynolds number blade element momentum theory aerodynamic model is used with a brushless DC motor model, a terramechanics model, and a vehicle dynamics model to simulate the vehicle range under any operating angle-of-attack and forward velocity. To understand the tradeoff between vehicle size and operating range, variations in Pareto-optimal designs are presented as functions of vehicle size. A sensitivity analysis is used to better understand the impact of deviating from the optimal vehicle design variables. This work builds on current approaches in quadrotor optimization by leveraging a variety of models and formulations from the literature and demonstrating the implementation of various design constraints. It also improves upon current ad hoc rolling-flying vehicle designs created in previous studies. Results show the importance of accounting for oft-neglected component constraints in the design of high-range quadrotor vehicles. The optimal vehicle mechanical configuration is shown to be independent of operating point, stressing the importance of a well-matched, optimized propulsion system. By emphasizing key constraints that affect the maximum and nominal vehicle operating points, an optimization framework is constructed that can be used for rolling-flying vehicles and conventional multi-rotors.

Coupling a metal hydride tank with a PEMFC for vehicular applications: A simulations framework

This paper presents a thermal coupling topology of a proton exchange membrane fuel cell (PEMFC) with a metal hydride tank (MHT) based on FeTi metal hydride (MH) for vehicular applications. Usually, the heat produced by the PEMFC is evacuated to the ambient and MHT is heated by an external heat source, which negatively affects the efficiency of the PEMFC system as a whole. The idea is to reuse the heat generated by the PEMFC and re-inject it into the MHT to extract the usable hydrogen (H2) for PEMFC. Initially, a comprehensive model of a multiphysics system to manage the exchange of energy flux among the PEMFC and MHT is developed. Subsequently, two distinctive heat exchangers are integrated into the multiphysics system. The performance of the overall system depends on the effective regulation of both temperature and pressure associated with introduced exchanges. For that, two parallel controllers are devised to simultaneously regulate the H2 pressure and temperature of the PEMFC stack. The simulations of the system are established on MATLAB/Simulink software. It is shown by the simulation results that the proposed controller achieves the two requested objectives: maintaining the temperature and pressure of both the PEMFC stack and MHT for reliable operation.

Validation of Serpent-SUBCHANFLOW-TRANSURANUS pin-by-pin burnup calculations using experimental data from the Temelín II VVER-1000 reactor

This work deals with the validation of a high-fidelity multiphysics system coupling the Serpent 2 Monte Carlo neutron transport code with SUBCHANFLOW, a subchannel thermalhydraulics code, and TRANSURANUS, a fuel-performance analysis code. The results for a full-core pin-by-pin burnup calculation for the ninth operating cycle of the Temelín II VVER-1000 plant, which starts from a fresh core, are presented and assessed using experimental data. A good agreement is found comparing the critical boron concentration and a set of pin-level neutron flux profiles against measurements. In addition, the calculated axial and radial power distributions match closely the values reported by the core monitoring system. To demonstrate the modeling capabilities of the three-code coupling, pin-level neutronic, thermalhydraulic and thermomechanic results are shown as well. These studies are encompassed in the final phase of the EU Horizon 2020 McSAFE project, during which the Serpent-SUBCHANFLOW-TRANSURANUS system was developed.

Limits to Energy Conversion

The Second Law of thermodynamics implies that no thermodynamic system with a single heat source at constant temperature can convert heat into mechanical work in a recurrent manner. First, we note that this is equivalent to cyclo-passivity at the mechanical port of the thermodynamic system, while the temperature at the thermal port of the system is kept constant. This leads to the question, which general systems with two power ports have similar behavior: when is a system cyclo-passive at one of its ports, while the output variable at the other port (such as the temperature in the thermodynamic case) is kept constant? This property is called “one-port cyclo-passivity,” and entails, whenever it holds, a fundamental limitation to energy transfer from one port to the other. Sufficient conditions for one-port cyclo-passivity are derived for general multiphysics systems formulated in port-Hamiltonian form. This is illustrated by a variety of examples from different (multi-)physical domains; from coupled inductors and capacitor microphones to synchronous machines.

A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media

Comprehensive analyses of transport phenomena and thermodynamics of complex multiphysics systems are laborious and computationally intensive. Yet, such analyses are often required during the design of thermal and process equipment. As a remedy, this paper puts forward a novel approach to the prediction of transport behaviours of multiphysics systems, offering significant reductions in the computational time and cost. This is based on machine learning techniques that utilize the data generated by computational fluid dynamics for training purposes. The physical system under investigation includes a stagnation-point flow of a hybrid nanofluid (Cu−Al2O3/Water) over a blunt object embedded in porous media. The problem further involves mixed convection, entropy generation, local thermal non-equilibrium and non-linear thermal radiation within the porous medium. The SVR (Support Machine Vector) model is employed to approximate velocity, temperature, Nusselt number and shear-stress as well as entropy generation and Bejan number functions. Further, PSO meta-heuristic algorithm is applied to propose correlations for Nusselt number and shear stress. The effects of Nusselt number, temperature fields and shear stress on the surface of the blunt-body as well as thermal and frictional entropy generation are analysed over a wide range of parameters. Further, it is shown that the generated correlations allow a quantitative evaluation of the contribution of a large number of variables to Nusselt number and shear stress. This makes the combined computational and artificial intelligence (AI) approach most suitable for design purposes.

Validation of Serpent-SUBCHANFLOW-TRANSURANUS pin-by-pin burnup calculations using experimental data from a Pre-Konvoi PWR reactor

The focus of this work is the validation of Serpent-SUBCHANFLOW-TRANSURANUS, a high-fidelity multiphysics system coupling Monte Carlo neutron transport, subchannel thermalhydraulics and fuel-performance analysis. A full-core pin-by-pin burnup calculation for the first operating cycle of a Pre-Konvoi PWR plant is presented and the results are assessed using experimental data. The critical boron concentration and a set of pin-level neutron flux profiles are compared against measurements, with very good agreement. The impact of using fuel-performance analysis is discussed comparing the results obtained with the three-code coupling with the ones using the traditional neutronic-thermalhydraulic approach. The studies presented here are part of the final stage of the EU Horizon 2020 McSAFE project, closing the development cycle of the Serpent-SUBCHANFLOW-TRANSURANUS system, from implementation to validation.

Perspectives on spintronic diodes

Spintronic diodes are emerging as disruptive candidates for impacting several technological applications ranging from the Internet of things to artificial intelligence. Here, an overview of the recent achievements on spintronic diodes is briefly presented, underlying the major breakthroughs that have led these devices to have the largest sensitivity measured to date for a diode. For each class of spintronic diodes (passive, active, resonant, nonresonant), we indicate the remaining developments to improve the performances as well as the future directions. We also devoted the last part of this Perspective to ideas for developing spintronic diodes in multiphysics systems by combining two-dimensional materials and antiferromagnets.

An Analytical Study in Multi-physics and Multi-criteria Shape Optimization

A simple multi-physical system for the potential flow of a fluid through a shroud, in which a mechanical component, like a turbine vane, is placed, is modeled mathematically. We then consider a multi-criteria shape optimization problem, where the shape of the component is allowed to vary under a certain set of second-order Hölder continuous differentiable transformations of a baseline shape with boundary of the same continuity class. As objective functions, we consider a simple loss model for the fluid dynamical efficiency and the probability of failure of the component due to repeated application of loads that stem from the fluid’s static pressure. For this multi-physical system, it is shown that, under certain conditions, the Pareto front is maximal in the sense that the Pareto front of the feasible set coincides with the Pareto front of its closure. We also show that the set of all optimal forms with respect to scalarization techniques deforms continuously (in the Hausdorff metric) with respect to preference parameters.

Model‐Based Overpotential Deconvolution, Partial Impedance Spectroscopy, and Sensitivity Analysis of a Lithium‐Ion Cell with Blend Cathode

Lithium‐ion battery cells are multiscale and multiphysics systems. Design and material parameters influence the macroscopically observable cell performance in a complex and nonlinear way. Herein, the development and application of three methodologies for model‐based interpretation and visualization of these influences are presented: 1) deconvolution of overpotential contributions, including ohmic, concentration, and activation overpotentials of the various cell components; 2) partial electrochemical impedance spectroscopy, allowing a direct visualization of the origin of different impedance features; and 3) sensitivity analyses, allowing a systematic assessment of the influence of cell parameters on capacity, internal resistance, and impedance. The methods are applied to a previously developed and validated pseudo‐3D model of a high‐power lithium‐ion pouch cell. The cell features a blend cathode. The two blend components show strong coupling, which can be observed and interpreted using the results of overpotential deconvolution, partial impedance spectroscopy, and sensitivity analysis. The presented methods are useful tools for model‐supported lithium‐ion cell research and development.

Development and Validation of an Accurate 1D Model for Pressure Drop in Complex Coolant Piping Systems of Hybrid and Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">The development of efficient, reliable, and affordable Hybrid and Electric Vehicles (xEVs) relies on optimized Vehicle Thermal Management System (VTMS) architecture and control strategies. Compared to conventional vehicles, xEVs have more complex VTMS due to additional powertrain components and cooling circuits to meet distinct thermal requirements. The cooling circuits comprise a combination of hoses, straight, and bent pipes to route coolant flow around obstacles between powertrain components at distinct locations in the vehicle. The increased length and geometrical complexity of these piping systems, compared to conventional vehicles, results in increased pressure losses. Thus, accurate predictions of pressure drop within these piping systems is critical for component selection for an optimized VTMS. Numerical simulations are often used to study interactions between components from a system-level perspective allowing early stage rapid assessment of performance. In this work, an accurate 1D model for pressure drop in piping systems is developed based on literature review and validated using 3D Computational Fluid Dynamics (CFD) predictions. The 1D model is implemented in the software tool GT-SUITE which is used for integrated 0D/1D/3D multi-physics system simulation of the entire VTMS. The CFD calculations are performed using GT-CONVERGE. Overall, the pressure drop predictions of the 1D model are in good agreement with the 3D CFD for a range of Reynolds numbers including laminar, transition, and turbulent regimes and even when significant losses are present due to flow redevelopment after bends. Typically, commercial tools often ignore flow redevelopment and were originally developed for high Reynolds number flows. A typical battery electric vehicle is constructed in GT-SUITE and results indicate that deviations on pressure drop predictions lead to significant deviations on pump operating conditions and isentropic efficiency. The 1D model allows fast and accurate simulations over a broad range of complex piping configurations for an optimized VTMS.</div></div>

Numerical simulation of tethered–wing power systems based on variational integration

This paper describes dynamic formulations for a multi-physics system consisting of an electrical generator and a finite element model of a variable-length cable attached at one end to a reeling mechanism and the other to a rigid body that represents a flying wing. Lie group methods are utilized to obtain singularity-free dynamics of the wing. Two different methods are employed for the derivation and integration of the Euler–Lagrange equations. In the first method, the equations of motion are derived in continuous-time and integrated by a general-purpose implicit ODE solver. The second is the application of the discrete analogue of Lagrange–d’Alembert principle that yields a variational integrator. Extensive simulations are carried out to calculate the computational complexity of the discrete integrator and compare the results for CPU time and accuracy to those of the ODE solver. It is shown that the variational integrator has a significant advantage in terms of preservation of the orthogonality of the wing’s attitude matrix and reduction of the CPU time. The descriptions of the implemented aerodynamic models and controllers, along with the results for the simulation of a tethered-wing system operating in a turbulent wind environment are also presented.

Application of genetic programming for model-free identification of nonlinear multi-physics systems

This study provides a general methodology based on genetic programming (GP) to identify nonlinear multi-physics systems. The proposed GP-based method aims to discover governing equations for the systems, characterized by a set of ordinary differential equations (ODEs), solely from the measured data. The GP-based methodology employs expression trees, having mathematical components in their nodes, to randomly construct candidate models (i.e., ODEs) for the systems and fits them into the measured data set by applying evolutionary processes (e.g., crossover and mutation) over consecutive generations. To enhance the convergence of the identification process, the model fitness is evaluated by computing model outputs that are numerical solutions of the ODEs (i.e., candidate models) under the measured input, and comparing them to the measured output. The proposed generalization of the GP-based method leads to an additional computational burden which is resolved with parallel processing on high-performance computing systems. We demonstrate the applicability of the methodology to two distinct models originating from different physical systems: a Bouc–Wen model from mechanics and an environmental soil-moisture model from ecohydrology. For the Bouc–Wen model, governing equations that are close to the reference model were discovered. The GP-identified models yielded output errors less than 8%, regardless of errors embedded in training data, up to 5%. For the soil-moisture model, relatively simple models are identified with output errors similar to, or less than, 10%. It is shown that the method under discussion is useful in providing some physical insight into the equations that govern complex, nonlinear, multi-physics systems.

A Simplified Approach to Simulate Quench Development in a Superconducting Magnet

Cross-sectional 2D models often represent a computationally efficient alternative to full 3D models, when simulating complex multi-physical magnet systems. However, especially for the case of self-protected, superconducting magnets, where the stored energy has to be dissipated within the magnet coils, the thermal diffusion and the quench development in all three dimensions become key aspects. In order to further improve the simulation of transients in 2D models, a new modelling method for simplified quench development along the direction of the transport current is introduced. The original 2D model is hereby utilized for modelling the thermal domain, and the electrical resistance of each turn is scaled by the estimated time-dependent fraction of quenched conductor. Furthermore, the turn to turn quench propagation following the electrical connections is implemented. The proposed approach allows a very computationally efficient and easy-to-implement calculation since the model is effectively two-dimensional while providing a good approximation of the coil resistance development with sufficient accuracy. In order to illustrate the proposed quench-propagation modelling approach, simulations are compared to experimental results for the case of a self-protected, superconducting Nb-Ti dipole magnet. In general, a very good agreement between measurements and simulations was achieved.

A method to estimate battery SOH indicators based on vehicle operating data only

Batteries are multi-physical systems and during actual operating conditions they are submitted to variable ambient operating conditions which can affect the dynamic behavior and the degradation. Therefore, a good understanding of the dynamic behavior and the degradation laws under actual operating conditions is the key to a durability improvement and to the development of better energy management strategies. The purpose of the proposed study is to use an experimental database issued from a three years monitoring of a ten postal vehicle fleet to model the batteries with respect to operating conditions. Based on an electrical circuit model, an optimization algorithm and a Kalman filter, the scientific contribution is to propose a simple but efficient method, using vehicle operating data only, to estimate on-board the state of charge and state of health indicators linked to internal resistance and available capacity. The proposed model presents a very good accuracy and state of health indicators estimations show promising results. In the future, the proposed method could be applied on-board to estimate and analyze the state of health during the entire battery lifetime in order to provide an accurate state of charge estimation and to contribute to a better understanding of the degradation laws.

A Global-in-time Domain Decomposition Method for the Coupled Nonlinear Stokes and Darcy Flows

We study a decoupling iterative algorithm based on domain decomposition for the time-dependent nonlinear Stokes–Darcy model, in which different time steps can be used in the flow region and in the porous medium. The coupled system is formulated as a space-time interface problem based on the interface condition for mass conservation. The nonlinear interface problem is then solved by a nested iteration approach which involves, at each Newton iteration, the solution of a linearized interface problem and, at each Krylov iteration, parallel solution of time-dependent linearized Stokes and Darcy problems. Consequently, local discretizations in time (and in space) can be used to efficiently handle multiphysics systems of coupled equations evolving at different temporal scales. Numerical results with nonconforming time grids are presented to illustrate the performance of the proposed method.

Calculation of structure-borne sound in a\xa0direct drive wind turbine

In this publication, the methods will be presented that are deployed to formulate a multi-physical system model of a direct drive wind turbine in order to calculate structure borne sound. The model includes excitation effect as well as sound radiating behaviour. The mechanical structure as a medium partner between excitation and radiation will be formulated through a multi-body simulation model in the time domain. In the multi-body simulation model, all relevant drivetrain components are considered with their structural eigenmodes in the frequency range of interest. The electromagnetic forces of the multi-pole ring generator are calculated and introduced into the mechanical structure at each stator tooth, rotor pole and various axial positions individually. Similarly, the modelling of the bearings is investigated for a range of available methods. Sound emission is evaluated at the large outer surface structures like tower, blades and nacelle cover. To minimize computational effort, the surface accelerations are not calculated for each surface node, instead a modal approach is used. Through a combination of mode shapes with mode participation factors of the respective structures, the surface accelerations can be regained during a post-processing step. Those results are used as input for airborne sound calculations. Nevertheless, the high number of modal and spatial degrees of freedom results in high computing costs.

Linear electromagnetic energy harvester system embedded on a vehicle suspension: From modeling to performance analysis

Although linear electromagnetic energy harvester (LEH) is a promising technique for converting energy in a vehicle suspension, due to the large displacements, one of the main drawbacks of the solutions inside the vehicle is still their size and complexity. To address this issue, this paper focuses on the design and fabrication of a fully embedded LEH without any modification of the suspension initial structure. After a determination of the electrical, mechanical and electromechanical parameters using a Finite Element analysis, the dynamic efficiency is highlighted with a global Bond Graph model. This formalism is well adapted to simulate energy transfers inside multiphysic systems and to reduce the computational time, whereas the finite element model is not exploitable for a complete suspension simulation. In order to validate the Bond Graph simulation results, an embedded prototype has been built and tested in a laboratory environment. The embedded LEH system delivers around 10 W for a solicitation of linear velocity of 1 m/s which is sufficient to power a classical electronic circuit which is in good correlation with the measured ones and significant power has been obtained.

GINNs: Graph-Informed Neural Networks for multiscale physics

We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate for physics-based representations of multiscale and multiphysics systems. GINNs address the twin challenges of removing intrinsic computational bottlenecks in physics-based models and generating large data sets for estimating probability distributions of quantities of interest (QoIs) with a high degree of confidence. Both the selection of the complex physics learned by the NN and its supervised learning/prediction are informed by a PGM, which includes the formulation of structured priors for tunable control variables (CVs) to account for their mutual correlations and ensure physically sound CV and QoI distributions. GINNs accelerate the prediction of QoIs essential for simulation-based decision-making where generating sufficient sample data using physics-based models alone is often prohibitively expensive. Using applications grounded in energy storage, we describe the construction of GINNs for a domain-aware Bayesian network that embeds a homogenized model of supercapacitor dynamics and a data-driven Bayesian network for a Langmuir adsorption model. Both examples demonstrate the ability of GINNs to produce kernel density estimates of relevant non-Gaussian, skewed QoIs with tight confidence intervals.

Multifidelity computing for coupling full and reduced order models.

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.

Exergetic port-Hamiltonian systems: modelling basics

ABSTRACT Port-Hamiltonian systems theory provides a structured approach to modelling, optimization and control of multiphysical systems. Yet, its relationship to thermodynamics seems to be unclear. The Hamiltonian is traditionally thought of as energy, although its meaning is exergy. This insight yields benefits: 1. Links to the GENERIC structure are identified, making it relatively easy to borrow ideas from a popular nonequilibrium thermodynamics framework. 2. The port-Hamiltonian structure combined with a bond-graph syntax is expected to become a main ingredient in thermodynamic optimization methods akin to exergy analysis and beyond. The intuitive nature of exergy and diagrammatic language facilitates interdisciplinary communication that is necessary for implementing sustainable energy systems and processes. Port-Hamiltonian systems are cyclo-passive, meaning that a power-balance equation immediately follows from their definition. For exergetic port-Hamiltonian systems, cyclo-passivity is synonymous with degradation of energy and follows from the first and the second law of thermodynamics being encoded as structural properties.