Academic literature on the topic 'Acausal Modelling'

Modelling and simulation are key tools for analysis and design of systems and processes from almost any scientific or engineering discipline. Models of complex systems are typically built on acausal Differential-Algebraic Equations (DAE) and discrete events using Object-Oriented Modelling (OOM) languages, and some of their key concepts can be explained as symmetries. To obtain a computer executable version from the original model, several algorithms, based on bipartite symmetric graphs, must be applied for automatic equation generation, removing alias equations, computational causality assignment, equation sorting, discrete-event processing or index reduction. In this paper, an open source tool according to OOM paradigm and developed in MATLAB is introduced. It implements such algorithms adding an educational perspective about how they work, since the step by step results obtained after processing the model equations can be shown. The tool also allows to create models using its own OOM language and to simulate the final executable equation set. It was used by students in a modelling and simulation course of the Automatic Control and Industrial Electronics Engineering degree, showing a significant improvement in their understanding and learning of the abovementioned topics after their assessment.

Spacecraft power system simulation involves the coupling of electrical, thermal and control domains. At present, the modeling and simulation of multi-domain physical system mainly uses the single-domain software to establish a single-domain model, and solves the unified multi-domain modeling and simulation through the interface between the software or using HLA. But it cannot fully support the modeling and simulation of multi-domain physical system, and the model has poor reusability and extensibility. As a multi-domain modeling language, Modelica language supports acausal modelling, unified multi-domain modeling, object-oriented physical modeling and hybrid modeling. So it is widely used in the aerospace area. In this paper, Modelica language is used to establish module library of spacecraft power system on simulation platform MWorks, and the multi-domain simulation model of spacecraft power system is obtained by assembling each sub-model, and the performance of the model is simulated and analyzed so as to achieve the purpose of improving and verifying the model.

SUMMARY Ambient noise cross-correlations can be used as self-consistent observables, opening novel possibilities for investigating ambient noise sources. To optimize the forward-modelling of global ambient noise cross-correlations for any given distribution of noise sources in the microseismic frequency range up to 0.2 Hz, we implement (i) pre-computed wavefields and (ii) spatially variable grids. This enables rapid inversions for microseismic noise sources based on finite-frequency source sensitivity kernels. We use this advancement to perform regional and global gradient-based iterative inversions of the logarithmic energy ratio in the causal and acausal branches of microseismic noise cross-correlations. Synthetic inversions show promising results, with good recovery of the main dominant noise sources of the target model. Data inversions for several consecutive days at the beginning of October 2019 demonstrate the capability of inverting for the spatio-temporal variations of the sources of secondary microseisms in the ocean. This paves the way for daily ambient noise source inversions which could help improve full-waveform ambient noise tomography and subsurface monitoring methods.

Simulation is an important tool for evaluating the design of a ship-course control system. The object-oriented modeling supports the physical modeling of a multi-domain dynamical system by using a hierarchical acausal structure, as compared to block diagrams or differential equation-based causal structures. In this paper we describe the use of the SIMSCAPE simulation environment for the physical modeling-based design of the course control system of a ship. The complete model has been implemented by using this physical modeling approach whereby dynamic system equations are transformed into a diagram of interconnected physical blocks so as to represent in this way the true structure of the modeled system. The performance of the course control of the ship model was analyzed by simulation in light of the existing hypothesis and indirect validation tests previously performed with operational data. The results obtained by using sequences of course-changing maneuvers with varying disturbances serve to demonstrate the usefulness of the physical modeling-based approach with high accuracy and small computational cost as compared to the classical differential equation-based or the adimensional block-oriented diagrams. This methodology can be easily extended to other engineering fields provided that a suitable set of SIMSCAPE physical libraries can be used (electrical, mechanical, hydraulics, thermal,…). In fact, the ship-course control system model here developed represents an interesting benchmark in the field of engineering systems to get insight on the physical modeling approach under SIMSCAPE or even under MODELICA, despite its specificity to marine systems.

The automotive industry has long been searching for efficient ways to improve vehicle performance such as drivability, fuel consumption, and emissions. Researchers in the automotive industry have tried to develop methods to improve fuel consumption and reduce the emission gases of a vehicle, while satisfying drivability and ride comfort issues. Today, by developing computer/software technologies, automotive manufacturers are moving more and more towards modelling a real component (prototype) in a software domain (virtual prototype). For instance, modelling the components of a vehicle's powertrain (driveline) in the software domain helps the designers to iterate the model for different operating conditions and scenarios to obtain better performance without any cost of making a real prototype. The objective of this research is to develop and validate physics-based powertrain models with sufficient fidelity to be useful to the automotive industry for rapid prototyping. Developing a physics-based powertrain model that can accurately simulate real phenomenon in the powertrain components is of great importance. For instance, a high-fidelity simulation of the combustion phenomenon in the internal combustion (IC) engine with detailed physical and chemical reactions can be used as a virtual prototype to estimate physical prototype characteristics in a shorter time than it would take to build a physical prototype. Therefore, the powertrain design can be explored and validated virtually in the software domain to reduce the cost and time of product development. The main focus of this thesis is on development of an internal combustion engine model, four-cylinder spark ignition engine, and a hydrodynamic torque converter model. Then, the models are integrated along with the rest of a powertrain's components (e.g. vehicle longitudinal dynamics model) through acausal connections, which represents a more feasible physics-based powertrain model for model-based control design. The powertrain model can be operated at almost all operating conditions (e.g. wide range of the engine speeds and loads), and is able to capture some transient behaviour of the powertrain as well as the steady state response. Moreover, the parametric formulation of each component in the proposed powertrain model makes the model more efficient to simulate different types of powertrain (e.g. for a passenger car or truck).