Integration of an object formalism within a hybrid dynamic simulation environment

PrODHyS is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of systems engineering. Its major characteristic is its ability to simulate processes described by a hybrid model. In this framework, this paper focuses on the "Object Differential Petri Net" (ODPN) formalism integrated within PrODHyS. The use of this formalism is illustrated through a didactic example relating to the field of Chemical Process System Engineering (PSE)

Advances in Rule-based Modeling: Compartments, Energy, and Hybrid Simulation, with Application to Sepsis and Cell Signaling

Biological systems are commonly modeled as reaction networks, which describe the system at the resolution of biochemical species. Cellular systems, however, are governed by events at a finer scale: local interactions among macromolecular domains. The multi-domain structure of macromolecules, combined with the local nature of interactions, can lead to a combinatorial explosion that pushes reaction network methods to their limits. As an alternative, rule-based models (RBMs) describe the domain-based structure and local interactions found in biological systems. Molecular complexes are represented by graphs: functional domains as vertices, macromolecules as groupings of vertices, and molecular bonding as edges. Reaction rules, which describe classes of reactions, govern local modifications to molecular graphs, such as binding, post-translational modification, and degradation. RBMs can be transformed to equivalent reaction networks and simulated by differential or stochastic methods, or simulated directly with a network-free approach that avoids the problem of combinatorial complexity. Although RBMs and network-free methods resolve many problems in systems modeling, challenges remain. I address three challenges here: (i) managing model complexity due to cooperative interactions, (ii) representing biochemical systems in the compartmental setting of cells and organisms, and (iii) reducing the memory burden of large-scale network-free simulations. First, I present a general theory of energy-based modeling within the BioNetGen framework. Free energy is computed under a pattern-based formalism, and contextual variations within reaction classes are enumerated automatically. Next, I extend the BioNetGen language to permit description of compartmentalized biochemical systems, with treatment of volumes, surfaces and transport. Finally, a hybrid particle/population method is developed to reduce memory requirements of network-free simulations. All methods are implemented and available as part of BioNetGen. The remainder of this work presents an application to sepsis and inflammation. A multi-organ model of peritoneal infection and systemic inflammation is constructed and calibrated to experiment. Extra-corporeal blood purification, a potential treatment for sepsis, is explored in silico. Model simulations demonstrate that removal of blood cytokines and chemokines is a sufficient mechanism for improved survival in sepsis. However, differences between model predictions and the latest experimental data suggest directions for further exploration

Dynamic state reconciliation and model-based fault detection for chemical processes

In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processe

Modelling Of An Automotive Multi-Evaporator Air-Conditioning System

With the arrival of plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV) with significant autonomy, battery cooling becomes a necessity in driving mode to ensure their durability and ability to charge rapidly.  For these vehicles, the refrigerating system may be composed of two evaporators (for front and rear passengers) in order to afford cooled air in the cabin and a chiller or a built-in battery evaporator to cool down the traction battery. This kind of multi-evaporator air-conditioning system has a number of technological barriers that must be undone. They are related to the components sizing in a context of cost reduction and control of such complex systems. The study therefore focuses on understanding the dynamic coupling of the several loop components such as the three evaporators having different cooling capacities. Understanding the behaviour of their respective expansion devices and the choice of these latter is also essential to control properly the transient phase and ensure an optimal operation of the air-conditioning system. In the literature, the effect of battery cooling by means of a chiller on the automotive air-conditioning loop has been already proved by simulation in the Dymola® [1] environment. The simulation results for several driving cycles, refrigerants and ambient conditions emphasize the thermal discomfort caused by the use of the chiller loop. However, no global control strategy has been established. More recently, a first study of an air conditioning system model with three evaporators was carried out [2]. After the validation of their component models, a cool down test was performed to test the performance of their air conditioning system. From a control point of view, a simple PI control on the temperature of air blown at the front evaporator was used to regulate the speed of the compressor. In the building sector [3], the benefits of a supervisory controller to regulate the multi-evaporator air conditioning system was developed. Although this type of decentralized model seems to be robust and applicable to the car, it requires the use of sensors and components currently too costly and subject to a less restrictive environment than in automotive. The challenge of such a cooling loop lies in the dynamic coupling of components as well as their design. The model of a multi-evaporator automotive air conditioning system (two evaporators and a chiller) is thus produced using the 0D AMESim® software. In order to obtain more representative results in the transient state, the majority of components, including the chiller and regulators, are physical models giving a good representation of their internal geometries. These models were validated using experimental test maps. The first results highlight the importance of the regulators choice on the loop stability. A comparison of several types of expansion valves (orifice, thermostatic and electronic) will be conducted in order to select the most suitable to meet the price-performance compromise. Finally, control strategies are studied in transient state to further improve the stability and speed of convergence to the target instructions

Dissemination of Health Information within Social Networks

In this paper, we investigate, how information about a common food born health hazard, known as Campylobacter, spreads once it was delivered to a random sample of individuals in France. The central question addressed here is how individual characteristics and the various aspects of social network influence the spread of information. A key claim of our paper is that information diffusion processes occur in a patterned network of social ties of heterogeneous actors. Our percolation models show that the characteristics of the recipients of the information matter as much if not more than the characteristics of the sender of the information in deciding whether the information will be transmitted through a particular tie. We also found that at least for this particular advisory, it is not the perceived need of the recipients for the information that matters but their general interest in the topic

Automated Topology Synthesis and Optimization of Hybrid Electric Vehicle Powertrains

This thesis presents a framework to automate the process of designing Hybrid Electric Vehicle (HEV) powertrain architectures. An algorithm was developed to assemble and compare all possible configurations of powertrain components. Combinatorics was used to discover all possible combinations of: an internal combustion engine, high-torque low-speed electric motor, low-torque high-speed electric motor, planetary gearset, and five-speed discrete gearbox. The Graph Theoretic Method was used to generate the powertrain models. The powertrain models were comprised of steady-state equations in symbolic form. An optimal control strategy is required to fairly compare the different topologies because a powertrain control strategy is dependant on the configuration. Dynamic Programming was used to determine the control law that minimizes the energy consumption for a given drivecycle. Evaluating every possible topology would take an extremely long time, so topologies were evaluated using a multi-stage screening process. The first stage examined the structure of the powertrain and used heuristics to eliminate infeasible topologies; 512 topologies were feasible. The second stage eliminated topologies that could not meet basic driving performance; 193 topologies were feasible. Basic driving performance was tested using a section of the US06 drivecycle. The sizes of three components were optimized to ensure the topology is feasible independent of the size of the components. The third stage eliminated topologies which could not achieve driving performance design goals; 159 could achieve the performance requirements, but only 119 were reasonably fuel efficient. The driving performance goals were implemented with a drivecycle based on the Partnership for a New Generation of Vehicles (PNGV) goals. The sizes for five components were optimized at this stage. The 20 most fuel efficient powertrains were selected for further evaluation. Additionally, 4 common powertrains were evaluated for reference. The size of the components were optimized for a combination of the PNGV drivecycle and the HWFET drivecycle. The most fuel efficient topology was found to be a Powersplit hybrid which has a discrete gearbox between the final drive and the powersplit device. The electric motor, planetary carrier gear, and gearbox were connected in parallel. It was found that Parallel-like, Powersplit-like, and Complex-like topologies were were the most efficient powertrain configurations. Powertrains containing two gearboxes were more efficient because the geartrain models ignored mechanical inefficiencies