Modeling Of Engineering Systems Research Articles

An integrated data collection and analysis framework for remote monitoring and planning of construction operations

Recent advances in data collection and operations analysis techniques have facilitated the process of designing, analyzing, planning, and controlling of engineering processes. Mathematical tools such as graphical models, scheduling techniques, operations research, and simulation have enabled engineers to create models that represent activities, resources, and the environment under which a project is taking place. Traditionally, most simulation paradigms use static or historical data to create computer interpretable representations of real engineering systems. The suitability of this approach for modeling construction operations, however, has always been a challenge since most construction projects are unique in nature as every project is different in design, specifications, methods, and standards. Due to the dynamic nature and complexity of most construction operations, there is a significant need for a methodology that combines the capabilities of traditional modeling of engineering systems and real time field data collection. This paper presents the requirements and applicability of a data-driven modeling framework capable of collecting and manipulating real time field data from construction equipment, creating dynamic 3D visualizations of ongoing engineering activities, and updating the contents of a discrete event simulation model representing the real engineering system. The developed framework can be adopted for use by project decision-makers for short-term project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities.

Approximability and Variational Description of the Zeno Behavior in Affine Switched Systems

This paper deals with a new theoretic description of a specific interaction of continuous and discrete dynamics in switched control systems known as a Zeno dynamics. We consider solutions to the closed loop realizations of control systems with affine structure that admit infinitely many discrete transitions on a finite time interval. Although the real-world processes do not present the corresponding behavior, mathematical models of some interconnected engineering systems may be Zeno due to the formal abstraction. In this paper we give a variational interpretation of the Zeno-like dynamics. A generic trajectory associated with this dynamics is finally specified as a result of a particular optimization procedure applied to the original model. The variational description of the Zeno effect provides an analytic basis for the constructive approximations techniques. We also study an intersection of a class of switched systems and the conventional sliding mode control processes.

A work-centered perspective on research needs for systems engineering with models

Compared to document-based systems engineering (SE) models, IT-enabled ones can introduce radical changes in how SE is practiced. Such models are increasingly important today, but how well do we understand their use, impact, and issues? Real-world cases are helpful but tell us little about how and why models work (or don’t), their suitability for a particular context, or how to guide selection and use. Lacking sound knowledge and theory, we cannot assess the value of Model-Based Systems Engineering (MBSE) [1] or predict its outcomes [2]. Instead, we rely on heuristics and judgment, e.g., to make process tailoring decisions [3]. Our motivations include identifying core research needs, provoking discussion on key issues and questions [4] and generating interest by applied and theoretical researchers, including from outside engineering. Other authors have noted that SE would benefit from broader research methodologies and methods (e.g., [5]; [6]) and greater participation by non-engineering disciplines (e.g., [2]; [7]). This paper identifies MBSE research topics culled from literature and briefly discusses some key ones based on a work-centered organizational approach with the goal of sparking interest in foundational research.

Optimisation of the Design Parameters of a Compressed Air Engine Based on Numerical Simulation

Using compressed air as a vehicle propulsion is an appealing prospect. It would provide a longterm potential to reverse environmental impact associated with burning of fossil fuels, provided that clean energy (wind energy or solar energy) is used to produce the compressed air. This paper presents a systematic study on the requirement of the design parameters of a prototype compressed air engine for optimising its efficiency and performance. An air engine system model has been developed and a detailed numerical simulation is carried out to investigate the effect of a number of key design parameters including the dimensions of the intake system, compression ratio, bore and stroke, crank/conrod (connecting rod) ratio. It has been found that the air engine efficiency and performance are sensitive to these parameters, and optimisation of them is a necessary requirement during the compressed air engine design process.

An approach to modeling and thermodynamic analysis of modern power engineering systems

An approach to modeling and thermodynamic analysis of modern power engineering systemsThe new efficient method of modeling and thermodynamic analysis of power engineering systems has been presented. With its help a comparison of different structures and investigation of the influence of a particular constituent process onto the whole system efficiency is possible. The shaft work or the exergy is the main thermodynamic quantity taken into account in analyses, and the appropriate dimensionless modeling parameter has been introduced.

Surrogate-based modeling and dimension reduction techniques for multi-scale mechanics problems

Successful modeling and/or design of engineering systems often requires one to address the impact of multiple “design variables” on the prescribed outcome. There are often multiple, competing objectives based on which we assess the outcome of optimization. Since accurate, high fidelity models are typically time consuming and computationally expensive, comprehensive evaluations can be conducted only if an efficient framework is available. Furthermore, informed decisions of the model/hardware’s overall performance rely on an adequate understanding of the global, not local, sensitivity of the individual design variables on the objectives. The surrogate-based approach, which involves approximating the objectives as continuous functions of design variables from limited data, offers a rational framework to reduce the number of important input variables, i.e., the dimension of a design or modeling space. In this paper, we review the fundamental issues that arise in surrogate-based analysis and optimization, highlighting concepts, methods, techniques, as well as modeling implications for mechanics problems. To aid the discussions of the issues involved, we summarize recent efforts in investigating cryogenic cavitating flows, active flow control based on dielectric barrier discharge concepts, and lithium (Li)-ion batteries. It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for “scale bridging.”

Numerical Simulation of Valve Timing and Size on a Compressed Air Engine Performance

The compressed air engine is receiving increasingly worldwide attention because it takes advantage of renewable energy and has zero exhaust emissions. This paper presents a systematic study on valve timing and size of a prototype compressed air engine for optimizing its efficiency and performance. An in-house air engine system model has been developed using the FLOWMASTER platform. The simulated results show that the optimizing valve timings is probably the most critical requirement in the compressed air engine design process.

Exact Reliability of a Linear Connected-$(r,s)$-out-of-$(m,n)$: F System

A linear connected- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r,s)$</tex> </formula> -out-of- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(m,n):F$</tex> </formula> system consists of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$m\times n$</tex></formula> components arranged in <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$m$</tex></formula> rows by <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$n$</tex></formula> columns, and it fails iff there exists a <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r\times s$</tex></formula> subsystem in which all components are failed. The linear connected- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$(r,s)$</tex></formula> -out-of- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$(m,n):F$</tex></formula> system can be used for modeling engineering systems such as temperature feeler systems, supervision systems, etc. In this paper, a general method is proposed based on the finite Markov chain imbedding approach to study the exact reliability of a linear connected- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r,s)$</tex></formula> -out-of- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(m,n):F$</tex></formula> system. Then a new more efficient method, which reduces the size of the state space by combining some states into one state, is presented to reduce the computing time. Furthermore, three numerical examples are given. The first two numerical examples show that the proposed algorithm is efficient not only when the component states are i.i.d., but also when the component states are statistically independent and non-identically distributed. And the last numerical example shows that our method can be used to compute not only the reliability, but also the component importance.

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGG...

Bond graph modelling of a wave energy conversion system with hydraulic power take-off

A system viewpoint is essential in the study of wave energy converters, since several different energy domains are involved in such devices. In this regard, bond graph, a graphical, port-based approach to modelling engineering systems, serves as a useful tool. This article presents a study of a wave energy conversion system with hydraulic power take-off. With the aid of bond graph, two alternative hydraulic system designs are modelled by assembling hydraulic subsystems/components in different manners. A shallow-water pitching wave energy conversion system is considered as a case study, and selected simulation results using the two alternative hydraulic system designs are presented. In addition, this article suggests how to model by bond graph the dynamics of a multi-body wave energy conversion system.

A comprehensive engine to drive-cycle modelling framework for the fuel economy assessment of advanced engine and combustion technologies

A comprehensive engine to drive-cycle modelling framework has been developed for evaluating fuel economy improvements of new engine technologies. The framework comprises three key components: (a) full engine system models and routines for the generation of engine performance and fuel consumption maps; (b) an improved experimental heat release analysis and model calibration tool, which was created by coupling an in-house heat release analysis program to an engine cycle simulation, and which can be used for model calibration and validation when experimental data are available; and (c) an integrated vehicle modelling and drive-cycle simulation platform for fuel economy assessment. The framework implementation has been demonstrated through a fuel economy study of three engine and combustion technologies: a conventional spark-ignition (SI) engine, a high compression ratio SI engine with early intake valve closing (EIVC), and a homogeneous-charge compression ignition engine (HCCI) employing a recompression valve strategy, used in dual-mode SI-HCCI operation. Simulation results for three drive cycles indicate potential fuel economy gains of the order of 7–11% for the high-compression EIVC SI engine and 7–21% for the dual-mode HCCI engine configurations. Further analysis of the latter, however, reveals frequent mode switching and short excursions in the HCCI region, which could be a challenge during practical implementation, and potentially result in reduced fuel economy gains.

Endoreversible Modeling and Optimization of a Multistage Heat Engine System with a Generalized Heat Transfer Law via Hamilton-Jacobi-Bellman Equations and Dynamic Programming

heat transfer law [q / (¢(T n )) m ] is investigated in this paper. Optimal control theory is applied to derive the continuous Hamilton‐Jacobi‐Bellman equations, which determine the optimal fluid temperature configurations for maximum power output under the conditions of fixed initial time and fixed initial temperature of the driving fluid. Based on the general optimization results, the analytical solution for the case with Newtonian heat transfer law [q / ¢(T)] is further obtained. Since there are no analytical solutions for the other heat transfer laws, the continuous Hamilton‐Jacobi‐Bellman equations are discretized and the dynamic programming algorithm is adopted to obtain the complete numerical solutions of the optimization problem, and the relationships among the maximum power output of the system, the process period and the fluid temperature are discussed in detail. The results show that the optimal high-temperature fluid reservoir temperature for the maximum power output of the multistage heat engine system with Newtonian and linear phenomenological [q / ¢(T i1 )] heat transfer laws decrease exponentially and linearly with time, respectively, while those with the Dulong‐Petit [q / (¢T) 1:25 ], radiative [q / ¢(T 4 )] and [q / (¢(T 4 )) 1:25 ] heat transfer laws are dierent from the former two cases significantly.

Reliability Evaluation for a Multi-State System Under Stress-Strength Setup

The two most commonly used reliability models in engineering applications are binary k-out-of-n:G and consecutive k-out-of-n:G systems. Multi-state k-out-of-n:G and multi-state consecutive k-out-of-n:G systems have been proposed as an extension of these systems and they have been found to be more flexible tool for modeling engineering systems. In this article, multi-state systems, in particular, multi-state k-out-of-n:G and multi-state consecutive k-out-of-n:G, are considered in a stress-strength setup. The states of the system are classified considering the number of components whose strengths above (below) the multiple stresses available in an environment. The exact state probabilities are provided and the results are illustrated for various stress-strength distributions. Maximum likelihood estimators of state probabilities are also presented.

Modelling and application of an online reverse system

A three-dimensional coordinate measuring machine or a three-dimensional scanner is generally applied in reverse engineering to realise the digital parts, but it limits the application of reverse engineering because of its high cost. To solve this problem, using CNC machine tools' characteristics of high precision walking mechanism, proposed an on-machine reverse engineering system model, established the IDEF0 system framework, including CAD-based measurement path planning, automatically generated on-machine measurement process, automatically measurement data processing and reversing, and planned the on-machine reverse flow from the physical to its CAD model. And to realise the part's shape reverse seeking, an on-machine reverse experimental system, which is based on the model, has been established. The model provided a new low-cost method for reverse engineering.

Semi-Markovian models of the functioning of complex chemical engineering systems

This article deals with the general approaches to the construction of semi-Markovian models of chemical engineering systems. The basic lines of development of the semi-Markovian modeling apparatus, used for the determination of the rational set of measures to ensure the models’ economically efficient functioning, are shown. Expressions for the characterization of the maintenance service system and the repair of complex chemical engineering facilities are suggested.

Use of General Nonlinear Material Models in Beam Problems: Application to Belts and Rubber Chains

Accurate modeling of many engineering systems requires the integration of multibody system and large deformation finite element algorithms that are based on general constitutive models, account for the coupling between the large rotation and deformation, and allow capturing coupled deformation modes that cannot be captured using beam formulations implemented in existing computational algorithms and computer codes. In this investigation, new three-dimensional nonlinear dynamic rubber chains and belt drives models are developed using the finite element absolute nodal coordinate formulation (ANCF) that allows for a straight forward implementation of general linear and nonlinear material models for structural elements such as beams, plates, and shells. Furthermore, this formulation, which is based on a more general kinematic description, can be used to predict the cross section deformation and its coupling with the extension and bending of the belt drives and rubber chains. The ANCF cross section deformation results are validated by comparison with the results obtained using solid finite elements in the case of a simple tension test problem. The effect of the use of different linear and nonlinear constitutive laws in modeling belt drive mechanisms is also examined in this investigation. The finite element formulation presented in this paper is implemented in a general purpose three-dimensional flexible multibody algorithm that allows for developing detailed models of mechanical systems subject to general loading conditions, nonlinear algebraic constraint equations, and arbitrary large displacements that characterize belt drives and tracked vehicle dynamics. The successful integration of large deformation finite element and multibody system algorithms is shown to be necessary in order to be able to study the dynamics of complex tracked vehicles with rubber chains. A computer simulation of a three-dimensional multibody tracked vehicle model that consists of twenty rigid bodies and two flexible rubber chains is used in order to demonstrate the use of the formulations presented in this investigation.

СистемніЙ аналіЗ комплексУ подачІ І розподілУ водИ В житлово-комУнальномУ господарствІ

The problem of system analysis and mathematical modeling of complex engineering systems by the example of the complex water supply and distribution of housing and communal services was considered.

Sensitivity of the Human Comfort Equation and of Free Convection in a Vertical Enclosure as Examples of the Use of Global Sensitivity to Evaluate Parameter Interactions

Many models of engineering and scientific systems involve interactions between and among the parameters, stimuli, and physical properties. The determination of the adequacy of models for predictions and for designing experiments generally involves sensitivity studies. Good designs mandate that the experiments be sensitive to the parameters sought with little interaction between them because such interaction generally confuses the estimation and reduces the precision of the estimates. For design purposes, analysts frequently want to evaluate the sensitivities of the predicted responses to specific variables, but if the variables interact it is often difficult to separate the effects. Global sensitivity is a technique by which one can evaluate the magnitude of the interactions between multiple variables. In this paper, the global sensitivity approach is applied to the human comfort equation and to free convection in a rectangular enclosure. It is found that when occupants are uncomfortable, there is little interaction and that one can predict the effects of changing several environmental conditions at once by adding the separate effects. But when occupants are comfortable, there is a large interaction and the effects cannot be treated separately. Free convective heat transfer in an enclosure is a function of the Rayleigh number Ra and the aspect ratio H/W, and the flow field changes from unicellular to multicellular as Ra increases. There is a strong interaction for H/W≤2 but little for H/W≥2.

Dissection of trained neural network hydrologic models for knowledge extraction

Artificial neural networks (ANNs) are powerful tools for the modeling and forecasting of complex engineering systems and have been exploited by researchers to solve a variety of problems over the last couple of decades. In spite of their proven ability to provide superior model performance compared to traditional modeling approaches, they have not become popular among decision makers for operational use. It is probably because of their perceived black box nature that does not explain or consider the underlying physical processes involved. This paper presents the results of a study aimed at a systematic dissection of the massively parallel architectures of trained ANN hydrologic models to determine if they learn the underlying physical subprocesses during training. This has been achieved using simple qualitative and quantitative techniques. The data derived from three contrasting catchments at two different time scales were employed to develop ANN models and test the methodologies employed for knowledge extraction. The results obtained in this study indicate that the number of hidden neurons determined during training for a particular data set correspond to certain subprocesses of the overall physical process being modeled. It has been found that the time scale of the data employed has an effect on optimum ANN architecture and knowledge extracted.

Optimal uncertainty reduction for multi-disciplinary multi-output systems using sensitivity analysis

We present a sensitivity analysis based uncertainty reduction approach, called Multi-dIsciplinary Multi-Output Sensitivity Analysis (MIMOSA), for the analysis model of a multi-disciplinary engineering system decomposed into multiple subsystems with each subsystem analysis having multiple inputs with reducible uncertainty and multiple outputs. MIMOSA can determine: (1) the sensitivity of system and subsystem outputs to input uncertainties at both system and subsystem levels, (2) the sensitivity of the system outputs to the variation from subsystem outputs, and (3) the optimal “investment” required to reduce uncertainty in inputs in order to obtain a maximum reduction in output variations at both the system and subsystem levels. A numerical and an engineering example with two and three subsystems, respectively, have been used to demonstrate the applicability of the MIMOSA approach.