A semantics, energy-based approach to automate biomodel composition

Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model's components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.

Effect of asymmetries of stiffness and mass on parametric instabilities of hollow rotor system

The present work aims to understand the influence of the asymmetries on the dynamics of the hollow rotor system. The study comprises the analytical study through the extended Lagrangian Hamiltonian approach and validation conducted with the simulation study through the bond graphs. This study involves an investigation of the dynamics of the hollow rotor system through two case studies. First case study considers a hollow rotor system with small asymmetry in stiffness while another study considers a hollow rotor system with asymmetry in mass. The lumped analysis involves the development of a mathematical model by considering the symmetry breaking of a hollow rotor caused by mass or stiffness. The study shows good agreement in simulation and analytical results.

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.

Backlash Fault Suppression Using LQ Optimal Control Based RST Controllers in Wind Turbine Systems Using Bond Graphs and Matlab/Simulink

The presence of backlash in wind turbines is a source of limitations as it introduces nonlinearities that reduce their efficiency in speed/torque control which affect the performance of the power quality. Because of production tolerances during rotation, the teeth contact is lost for a small angle; until it is re-established, it produces a backlash phenomenon. The desire to eliminate this phenomenon is often hard to realise due to the nonlinear dynamic behaviour, which arises with the presence of backlash fault in a system. Therefore, the goal of this study is to develop an LQ optimal control structure in a form of an R-S-T controller in order to reduce the disturbing torque transmitted inside the dead zone of a gearbox in the wind turbine system. The actual system is also developed to be used as a demonstration model at lectures or presentations. The efficacy of the proposed control is illustrated via simulations.

Teaching Mechatronic System Modeling: A Fifteen-Year Journey

Most innovations happen at the intersections of disciplines. New products get designed through synergistic integration of multi-disciplinary concepts. For example, in today’s automobiles purely mechanical systems have been replaced by “by-wire” devices that are software controlled, lighter, more efficient, and reliable. While engineering disciplines are merging seamlessly in real world products, academic silos are mostly still intact. At University of Detroit Mercy, we have broken down some silos by launching the Robotics and Mechatronics Systems Engineering major. Mechatronic Systems Modeling is a mandatory course in this major. This course uses a technique of power flow called bond graphs to model mechatronic systems. This technique is not discipline specific and students with different disciplinary background can easily understand and master it. Recently, the use of Simscape, a MATLAB/Simulink tool for physical system modeling has also been added to this course. The use of these two tools in complex system modeling tasks helps students develop an understanding of engineering system behavior by moving beyond the narrow boundaries of individual disciplines. This paper describes the course content and structure, the modeling methods, selected student projects, some of the lessons learned, and several offshoot activities that have resulted from this course.

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.