A Theoretical Multiscale Analysis of Electrical Field for Fuel Cells Stack Structures

2018

In this work, the analytical derivation and the computer implementation of the adjoint method are described. The adjoint method can be effectively used for solving the optimal control problem associated with a large class of nonlinear mechanical systems. As discussed in this investigation, the adjoint method represents a broad computational framework, rather than a single numerical algorithm, in which the control problem for nonlinear dynamical systems can be effectively formulated and implemented employing a set of advanced analytical methods as well as an array of well-established numerical procedures. A detailed theoretical derivation and a comprehensive description of the numerical algorithm suitable for the computer implementation of the methodology used for performing the adjoint analysis are provided in the paper. For this purpose, two important cases are analyzed in this work, namely the design of a feedforward control scheme and the development of a feedback control architecture. In this investigation, the control problem relative to the mechanical vibrations of a nonlinear oscillator characterized by a generalized Van der Pol damping model is considered in order to illustrate the effectiveness of the computational algorithm based on the adjoint method by means of numerical experiments.

Section: Background and Significancementioning

confidence: 99%

Use of the Adjoint Method for Controlling the Mechanical Vibrations of Nonlinear Systems

2018

“…In this context, the estimation process is an iterative process in which the mathematical structure of a dynamical model is devised and, once a suitable mathematical model is constructed for capturing the physics of the system of interest, one can identify the unknown parameters in the model by using an experimental dataset [6][7][8][9]. This process is guided by the intuition of the analyst and is based on the useful information embedded in the dataset available from the experimental campaign [10][11][12][13][14][15][16]. In the validation process, on the other hand, the analyst verifies that the selected dynamical model and the identified model parameters are able to reproduce the input-output datasets that are different from those used for constructing the system model by means of the system identification methods.…”

Section: Introductionmentioning

confidence: 99%

System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems

2018

Abstract:The goal of this investigation is to construct a computational procedure for identifying the modal parameters of linear mechanical systems. The methodology employed in the paper is based on the Eigensystem Realization Algorithm implemented in conjunction with the Observer/Kalman Filter Identification method (ERA/OKID). This method represents an effective and efficient system identification numerical procedure based on the time domain. The algorithm developed in this work is tested by means of numerical experiments on a full-car vehicle model. To this end, the modal parameters necessary for the design of active and semi-active suspension systems are obtained for the vehicle system considered as an illustrative example. In order to analyze the performance of the methodology developed in this investigation, the system identification numerical procedure was tested considering two case studies, namely a full state measurement and an incomplete state measurement. As expected, the numerical results found for the identified dynamical model showed a good agreement with the modal parameters of the mechanical system model. Furthermore, numerical results demonstrated that the proposed method has good performance considering a scenario in which the signal-to-noise ratio of the input and output measurements is relatively high. The method developed in this paper can be effectively used for solving important engineering problems such as the design of control systems for road vehicles.

“…As shown in several investigations, the multibody approach used for modelling the dynamics of mechanical systems constrained by mechanical joints represents an effective and efficient method for describing the kinematic structure of a given mechanical system and for analyzing the dynamic behavior resulting from a prescribed loading condition [43][44][45][46][47][48][49][50][51]. In this paper, therefore, a comparative study is carried out considering two general formulation strategies and two important solution procedures for solving the differential-algebraic dynamic equations of mechanical systems composed of multiple rigid bodies connected by mechanical joints.…”

Section: Introductionmentioning

confidence: 99%

On the Computational Methods for Solving the Differential-Algebraic Equations of Motion of Multibody Systems

2018

In this investigation, different computational methods for the analytical development and the computer implementation of the differential-algebraic dynamic equations of rigid multibody systems are examined. The analytical formulations considered in this paper are the Reference Point Coordinate Formulation based on Euler Parameters (RPCF-EP) and the Natural Absolute Coordinate Formulation (NACF). Moreover, the solution approaches of interest for this study are the Augmented Formulation (AF) and the Udwadia-Kalaba Equations (UKE). As shown in this paper, the combination of all the methodologies analyzed in this work leads to general, effective, and efficient multibody algorithms that can be readily implemented in a general-purpose computer code for analyzing the time evolution of mechanical systems constrained by kinematic joints. This study demonstrates that multibody algorithm based on the combination of the NACF with the UKE turned out to be the most effective and efficient computational method. The conclusions drawn in this paper are based on the numerical results obtained for a benchmark multibody system analyzed by means of dynamical simulations.