Modular Modeling Approach Research Articles

A modular modeling approach for describing the in-plane forming behavior of unidirectional non-crimp-fabrics

Various commercially available unidirectional (UD) non-crimp-fabrics (NCFs) are currently used for manufacturing carbon fiber reinforced plastic (CFRP) parts. These UD NCFs can differ significantly in their forming behavior. For optimizing and ensuring the manufacturability of the forming process of CFRP parts manufactured from UD NCFs these differences have to be taken into account. This motivates developing an efficient and universally applicable modular modeling approach for describing the in-plane forming behavior of various UD NCFs. The first component of this modular approach is a hyperelastic material model that accurately predicts the fiber orientation of UD NCFs during forming. This material model is implemented via a user-defined material subroutine in the commercial finite element package LS-DYNA. The second component is a simple truss structure that allows modeling the various stitch patterns of the different UD NCFs. This modular model can be calibrated via simple tensile tests. To demonstrate the versatility of this approach, the in-plane forming behavior of three different UD NCFs is validated by comparing experimental data and simulation results of the common picture frame test.

Freeway Traffic Modeling and Control in a First-Order Hybrid Petri Net Framework

The paper presents a model for freeway traffic performance evaluation and control in a First-Order Hybrid Petri Net (FOHPN) framework. Such a hybrid Petri net formalism includes continuous places holding fluid, discrete places containing a non-negative integer number of tokens and transitions, which are either discrete or continuous. In order to suitably describe the dynamics of the freeway traffic flow, we allow updating the transition firing speed as a function of the markings modeling the freeway traffic, as described by the stationary flow-density relationship. Moreover, we propose an online optimal control coordination of speed limits with the objective of maximizing the flow density. The use of FOHPNs offers several significant advantages with respect to the model existing in the related literature: the graphical feature enables an easy modular modeling approach and the mathematical aspects efficiently allow simulating and optimizing the system. The effectiveness of the FOHPN formalism is shown by applying the proposed modeling and control technique to a stretch of a freeway in the North-East of Italy, where a solution of an accident situation is considered.

Simulation of energy use in buildings with multiple micro generators

This paper focuses on the detailed modelling of micro combined heat and power (mCHP) modules and their interaction with other renewable micro generators in domestic applications based on an integrated modular modelling approach. The simulation model has been developed using Matlab/Simulink and incorporates a Stirling engine mCHP module embedded in a lumped-parameter domestic energy model, together with contributions from micro wind and photovoltaic modules. The Stirling cycle component model is based on experimental identification of a domestic-scale system which includes start up and shut down characteristics. The integrated model is used to explore the interactions between the various energy supply technologies and results are presented showing the most favourable operating conditions that can be used to inform the design of advanced energy control strategies in building. The integrated model offers an improvement on previous models of this kind in that a fully-dynamic approach is adopted for the equipment and plant enabling fast changing load events such as switching on/off domestic loads and hot water, to be accurately captured at a minimum interval of 1 min. The model is applied to two typical 3- and 4-bedroom UK house types equipped with a mCHP module and two other renewable energy technologies for a whole year. Results of the two cases show that the electrical contribution of a Stirling engine type mCHP heavily depends on the thermal demand of the building and that up to 19% of the locally-generated electricity is exported whilst meeting a similar percentage of the overall annual electricity demand. Results also show that the increased number of switching of mCHP module has an impact on seasonal module efficiency and overall fuel utilisation. The results demonstrate the need for the analysis of equipment design and optimal sizing of thermal and electrical energy storage.

Phase Transformation Kinetics: Advanced Modeling Strategies

Phase transformations in the solid state are often heterogeneous and can be described by concurring modes of nucleation, growth, and impingement. The classical Johnson–Mehl–Avrami–Kolmogorov-(JMAK-) model, although offering an easy-to-use description of the transformation kinetics, is limited to very specific cases of the transformation modes. Instead, a generalized modular model of phase transformation kinetics can be proposed that provides a flexible formalism adaptable to various modes of nucleation, growth, and impingement. Due to its large versatility, the modular model approach can be easily applied for characterization of phase transformation kinetics beyond the scope of classical JMAK(-type) modeling. Three different strategies recently employed for such advanced modeling are presented: (I) deliberate variation of the nucleation mode upon crystallization of an Fe-Ni-B metallic glass in order to determine separate activation energies for nucleation and growth, (II) incorporation of specific, dedicated modes for nucleation and growth kinetics for the allotropic hcp–fcc transformation in cobalt introducing driving-force-dependent rates of transformation, and (III) implementation of quantitative microstructural data for the description of the precipitation kinetics in a supersaturated CuCo alloy.

Optimum selection of mechanism type for heavy manipulators based on particle swarm optimization method

The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effective and efficient methods for the optimum selection among different types of mechanism candidates. This paper presents a new strategy for the purpose of optimum mechanism type selection based on the modified particle swarm optimization method. The concept of sub-swarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the sub-swarms to reassign their population size according to the relative performances of the mechanism candidates to implement the optimization. Combining with a modular modeling approach for fast calculation of the performance index of the potential candidates, the proposed method is applied to determine the optimum mechanism type among the potential candidates for the desired manipulator. The effectiveness and efficiency of the proposed method is demonstrated through a case study on the optimum selection of mechanism type of a heavy manipulator where six feasible candidates are considered with force capability as the specific performance index. The optimization result shows that the fitness of the optimum mechanism type for the considered heavy manipulator can be up to 0.578 5. This research provides the instruction in optimum selection of mechanism types for robotic manipulators.

Modeling Supermarket Refrigeration Systems for Demand-Side Management

Modeling of supermarket refrigeration systems for supervisory control in the smart grid is presented in this paper. A modular modeling approach is proposed in which each module is modeled and identified separately. The focus of the work is on estimating the power consumption of the system while estimating the cold reservoir temperatures as well. The models developed for each module as well as for the overall integrated system are validated by real data collected from a supermarket in Denmark. The results show that the model is able to estimate the actual electrical power consumption with a high fidelity. Moreover a simulation benchmark is introduced based on the produced model for demand-side management in smart grid. Finally, a potential application of the proposed benchmark in direct control of the power/energy consumption is presented by a simple simulation example.

Modular Reversibility Analysis in Self-loops Connections of Petri Net Systems

It is well known that the use of a modular approach for modeling has many advantages: it allows the modeler to consider different parts of the model independently of one another. A modular approach to analysis is also attractive: it often dramatically decreases the complexity of the analysis task. To create Petri net models of large systems, four bottom-up techniques, consisting of sharing operation, synchronous operation, self-loops connection as well as inhibitor-arc connection, have been developed. This paper focus on the concurrent behavior relation in self-loops connections of Petri net systems. First, for the property of dynamic invariance we show that it is possible to decide dynamic invariance of the global system from invariance of the individual modules. Second, for reversibility property we show that it is possible to construct reversibility of total modular system from reversibility of the individual modules without unfolding to the entire state space. Finally, we present some examples to illustrate the effectiveness of our approaches. The advantages of our approaches are in the context of concurrent language and can synthesize Petri net systems beyond asymmetric choice nets.

Modular Modeling Approach for Analysis of Cellular Beams

This paper presents the modular modeling approach adapted for the analysis of a cellular beam. These beams can be very useful for reducing the overall floor thickness in order to improve the Architectural design of a building. The cellular beam is made of a steel I-section with a total of 32 holes throughout its length. The finite element model of the beam is divided into 32 partitions and each partition is analyzed on a different processor. This partitioning method is based on a domain decomposition technique using dual partition super-elements. The analysis results are compiled and it is shown that with the help of the partitioning method, the nonlinear analysis of cellular beams has become more efficient and more accurate.

A modular and efficient approach to computational modeling and sensitivity analysis of robot and human motion dynamics

AbstractIn this paper a new class library for the computation of the forward multi‐body‐system (MBS) dynamics of robots and biomechanical models of human motion is presented. By the developed modular modeling approach the library can be flexibly extended by specific modeling elements like joints with specific geometry or different muscle models and thus can be applied efficiently for a number of dynamic simulation and optimization problems. The library not only provides several methods for solving the forward dynamics problem (like articulated body or composite rigid body algorithms) which can transparently be exchanged. Moreover, the numerical solution of optimal control problems, like in the forward dynamics optimization of human motion, is significantly facilitated by the computation of the sensitivity matrix with respect to the control variables. Examples are given to demonstrate the efficiency of the approach. (© 2012 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Evaluation of the logistic model of the reconfigurable manufacturing system based on generalised stochastic Petri nets

To reveal the influence on system performance by the logistic model of reconfigurable manufacturing system (RMS), the generalised stochastic Petri nets (GSPN) modular modelling approach is presented in this paper. It is based upon the characteristics of a bottleneck service. According to this approach, the bottleneck service in the production process is found first. By corresponding different resources in the service to different modules of the GSPN, the module is reconfigured. The analysis of the model using the Markov chain is hereby presented, as is the average utilisation factor of RMS. Following this, the production capacity of different products and the average productivity of reconfigurable manufacturing cells (RMCs) are discussed.

Electric utility resource planning using Continuous-Discrete Modular Simulation and Optimization (CoDiMoSO)

Electric utility resource planning traditionally focuses on conventional energy supplies such as coal, natural gas, and oil. Nowadays, planning of renewable energy generation as well as its side necessity of storage capacities have become equally important due to the increasing growth in energy demand, insufficiency of natural resources, and newly established policies for low carbon footprint. In this study, we propose to develop a comprehensive simulation based decision making framework to determine the best possible combination of resource investments for electric power generation and storage capacities. The proposed tool involves a combined continuous-discrete modular modeling approach for processes of different nature that exist within this complex system, and will help the utility companies conduct resource planning via employed multiobjective optimization techniques in a realistic simulation environment. The distributed power system considered here has four major components including (1) energy generation via a solar farm, a wind farm, and a fossil fuel power station, (2) storage via compressed air energy storage system, and batteries, (3) transmission via a bus and two main substations, and (4) demand of industrial, commercial, residential and transportation sectors. The proposed approach has been successfully demonstrated for the electric utility resource planning at a scale of the state of Florida.

Predicting intertidal organism temperatures with modified land surface models

Animals and plants in the marine intertidal zone live at the interface between terrestrial and marine environments. This zone is likely to be a sensitive indicator of the effects of climate change in coastal ecosystems, because of several key characteristics including steep environmental gradients, rapid temperature changes during tide transitions, fierce competition for limited space, and a community of mostly sessile organisms. Here we describe a modular modeling approach using modifications to a meteorological land surface model to determine body temperatures of the ecologically dominant rocky intertidal mussel Mytilus californianus, as a tool that can be used as a proxy for ecological performance. We validate model results against in situ measurements made with biomimetic body temperature sensors. Model predictions lie within the range of variability of biomimetic measurements, based on observations over a 4-year period at sites along 1700 km of the US west coast from southern California (34.5°N) to northern Washington (48.4°N). Our modular approach can be easily applied to many situations in the intertidal zone, including bare rock, mussel, barnacle, and algal beds, salt-marsh grasses, and sand- and mud-flats, by modifying the “vegetation layer” in a standard meteorological land surface model. Biophysical models such as these, which link ecological processes to changing climates through predictions of body temperature, are essential for understanding biogeographic patterns of physiological stress and mortality risk.

The kinetics of grain-boundary nucleated phase transformations: Simulations and modelling

The kinetics of nucleation-and-growth phase transformations for which nucleation occurs on the parent-phase grain boundaries was studied using mesoscopic microstructure simulations. The influences of the parent microstructure (notably grain size and grain-size distribution) and the parent → product transformation mechanism were determined and discussed. Subsequently, several mesoscopic kinetic models were investigated with respect to their ability to describe the simulated transformation kinetics, namely the classical Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, (i.e., assuming random nucleation), previously proposed empirical extensions of the JMAK model in the framework of the modular kinetic model approach, the Cahn model (assuming nucleation on randomly distributed planes) and a new empirical extension of the Cahn model proposed here. None of the models exactly predicts the real phase transformation kinetics. The classical JMAK model is unable to describe the simulated grain-boundary nucleated transformation kinetics. The Cahn model leads to erroneous results on fitting to simulated transformation curves. It is concluded that the modified Cahn model proposed here is best suited to infer correct values of kinetic material parameters from experimentally obtained data on the kinetics of grain-boundary nucleated phase transformations.

A Practical Framework for the Construction of a Biotracing Model: Application to Salmonella in the Pork Slaughter Chain

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables "backward reasoning" when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.

Robust control filter for manufacturing systems : application to PLC training

AbstractIn this paper, we show how research dealing with the design of a robust filter for manufacturing controllers can be used in the field of PLC training. Maintenance tasks and improvements in the manufacturing process involve changes in the PLC (programmable logic controller) program that can have bad safety consequences. Hence, a PLC program which is at the startup of the production lifecycle safety validated can become unsafe. Today, this problem has increased because remote maintenance monitoring is a reality in manufacturing systems. We have proposed to design a robust filter placed inside the PLC which authorizes or forbids outputs from the PLC. The filter is composed of several logical constraints which have to be respected at each PLC cycle. In order to guarantee the filter quality, it is necessary to check that all constraints have been well defined. For that, in order to validate the filter, an original formal method has been proposed. This one is based on a modular modelling approach of the manufacturing system and by considering the most permissive PLC program. UPPAAL checker is used to verify that the filter is sufficient to avoid the manufacturing system to reach dangerous forbidden states. If it is the case, the filter guarantees the safety whatever the PLC program. Hence, the filter still remains active even if there are changes in the PLC program during the lifecycle of the production system. This approach is applied to safe PLC training in order to test on line the students’ PLC program. The explanatory capacities of the filter are used to supply explanations. The paper is illustrated with an application based on a virtual “sorting system”.

Dynamic system simulation of small satellite projects

A prerequisite to accomplish a system simulation is to have a system model holding all necessary project information in a centralized repository that can be accessed and edited by all parties involved. At the Institute of Astronautics of the Technische Universitaet Muenchen a modular approach for modeling and dynamic simulation of satellite systems has been developed called dynamic system simulation (DySyS). DySyS is based on the platform independent description language SysML to model a small satellite project with respect to the system composition and dynamic behavior. A library of specific building blocks and possible relations between these blocks have been developed. From this library a system model of the satellite of interest can be created. A mapping into a C++ simulation allows the creation of an executable system model with which simulations are performed to observe the dynamic behavior of the satellite. In this paper DySyS is used to model and simulate the dynamic behavior of small satellites, because small satellite projects can act as a precursor to demonstrate the feasibility of a system model since they are less complex compared to a large scale satellite project.

Prevention of human control errors by robust filter for manufacturing system

In this paper, we are interested at the errors that can commit by a human operator in the control program. When a command error is made, the presence of a product in the system can cause damage. In this paper, we design a robust filter placed inside the PLC which authorizes or forbids outputs from the PLC. The filter is composed of several logical constraints which have to be respected at each PLC cycle. In order to guarantee the filter quality, it is necessary to check that all constraints have been well defined. For that, in order to validate the filter, an original formal method has been proposed. This one is based on a modular modelling approach of the manufacturing system by using different timed automata models (actuators, sensors, items, and PLC), and by considering the most permissive PLC program. UPPAAL checker is used to verify that the filter is sufficient to avoid the manufacturing system to reach dangerous forbidden states. If it is the case, the filter guarantees the safety whatever the PLC program. Hence, the filter still remains active even if there are changes in the PLC program during the lifecycle of the production system.

Quantifying exposure to Vero-cytotoxigenic Escherichia coli O157 in milk sold as pasteurized: A model-based approach

Milk sold as pasteurized has historically been implicated in the UK and worldwide as a vehicle for outbreaks of food-borne gastrointestinal disease, with a number of causative pathogenic organisms. One such organism is Verocytotoxigenic Escherichia coli, or E. coli, O157 (VTEC O157). We present a quantitative assessment of likely exposure to VTEC O157 via milk sold as pasteurized in the UK. Particular interest in our assessment concerns whether there is any differential risk between milk which is processed in on- and off-farm dairies. We model the milk production chain from the farm through to the point of retail and make a comparison between these two production environments. Our model is an example of the Modular Process Risk Modelling (MPRM) approach and represents uncertainty and variability in input parameters using probability distributions. We conclude that milk processed on farm poses the comparatively greater risk, although that risk is still small.

Industrial risk prevention by robust filter for manufacturing control system

Environmental damages can come from control errors. Indeed, for instance, maintenance tasks and improvements in the manufacturing process involve changes in the PLC (programmable logic controller) program that can have bad security consequences. Hence, a PLC program which is at the beginning of the production lifecycle safety validated can become unsafe. Today, this problem is increased because remote maintenance monitoring is a reality in manufacturing systems. In this paper, we design a robust filter placed inside the PLC which authorizes or forbids outputs from the PLC. The filter is composed of several logical constraints which have to be respected at each PLC cycle. In order to guarantee the filter quality, it is necessary to check that all constraints have been well defined. For that, in order to validate the filter, an original formal method has been proposed. This one is based on a modular modelling approach of the manufacturing system by using different timed automata models (actuators, sensors, items, and PLC), and by considering the most permissive PLC program. UPPAAL checker is used to verify that the filter is sufficient to avoid that the manufacturing system reaches dangerous forbidden states. If it is the case, the filter guarantees the safety whatever the PLC program. Hence, the filter still remains active even if there are changes in the PLC program during the lifecycle of the production system.

Risk Assessment for Escherichia coli O157:H7 in Marketed Unpasteurized Milk in Selected East African Countries

We carried out a study to assess the risk associated with the presence of Shiga toxigenic Escherichia coli (STEC) in informally marketed unpasteurized milk in urban East Africa. Data for the risk models were obtained from on-going and recently completed studies in Kenya and Uganda. Inputs for the model were complemented with data from published literature in similar populations. A fault-tree scenario pathway and modular process risk model approach were used for exposure assessment. Hazard characterization was based on a socioeconomic study with dose-responses derived from the literature. We used a probabilistic approach with Monte Carlo simulation and inputs from farm and household surveys. The qualitative analysis suggested a low to moderate risk of infection from consuming milk and that the widespread consumer practice of boiling milk before consumption was an important risk mitigator. Quantitative analysis revealed that two to three symptomatic STEC infections could be expected for every 10,000 unpasteurized milk portions consumed, with a possible range of 0 to 22 symptomatic cases. Sensitivity analyses to assess the uncertainty and variability associated with the model revealed that the factor with the greatest influence on disease incidence was the prevalence of STEC in dairy cattle. Risk assessment is a potentially useful method for managing food safety in informal markets.