System Modeling Techniques Research Articles

Petroleum systems modelling as an exploration tool: from surface seismic acquisition to basin modelling: a case study from a periplatform basin in Northern Adriatic

The Adriatic Sea hosts important historically explored hydrocarbon provinces. In the northern areas, the main sources of hydrocarbon productivity are from biogenic gas reservoirs located in Quaternary thick deltaic depositional systems related to the Apennine and Dinaric foreland domain. In addition, Mesozoic thermogenic oil plays were identified and exploited in the Southern areas. Herein, we use basin and petroleum systems modelling (BPSM) techniques to investigate the still unproved thermogenic hydrocarbon potential of deep Mesozoic basins in the North Adriatic Sea and to correlate the results with the sedimentary evolution of proven petroleum systems in the Central and South Adriatic, both in the Italian and Croatian offshore areas. We used the regional CROP-M16 marine seismic profile in the eastern segment of the study area, where it cuts the western margin of the Dinaric carbonate platform and reaches the pelagic domain of the Umbria-Marche basin intersecting the Barbara isolated platform (Figure 1). We reprocessed the 2D seismic data set to obtain the correct dips and depths of the geological structures from a depth-migrated profile. During the interpretation phase, we integrated the information from profile CROP-M16 with the orthogonal CROP-M17B and C profiles that are intersecting the Alessandra-001 exploration well. The interpretation allowed a detailed analysis of the main lithological discontinuities and of the fault system. We used the interpreted features together with the petrophysical parameters inferred by the available wells in the area and by literature, for 2D petroleum systems modelling. Our simulation integrated the available information to produce a set of possible scenarios of basin development, with the objective to understand the maturation and migration of hydrocarbons.

COMPUTATIONALLY SIMPLE NEURAL NETWORK APPROACH TO DETERMINE PIECEWISE-LINEAR DYNAMICAL MODEL

The article introduces a new technique for nonlinear system modeling. This approach, in comparison to its alternatives, is straight and computationally undemanding. The article employs the fact that once a nonlinear problem is modeled by a piecewise-linear model, it can be solved by many efficient techniques. Thus, the result of introduced technique provides a set of linear equations. Each of the equations is valid in some region of state space and together, they approximate the whole nonlinear problem. The technique is comprehensively described and its advantages are demonstrated on an example.

Some Contributions of Integrated Assessment Models of Global Climate Change

This article reviews the use of integrated assessment models (IAMs) in climate policy and research at the global scale. Two different types of IAMs are discussed. First, there are models that focus on climate change mitigation options and climate change impacts in some detail without necessarily valuing or aggregating all possible impacts into a single measure of projected climate damages. Here they are called detailed process (DP) IAMs. A second class of IAMs are much more highly aggregated and focus on calculating carbon emissions trajectories and carbon prices that maximize global welfare. Here these models are referred to as aggregate benefit–cost analysis (BCA) IAMs.Early IAMs of both types were introduced about 30 years ago and by now have been applied to many important policy and research design issues. Continual advancements in physical and economic system understanding, modeling techniques, and computational power should continue to open up many additional opportunities for using these models to provide relevant information to decision makers. While the models can be improved in many areas, much of the uncertainty that exists reflects a lack of complete scientific understanding of the systems involved rather than limitations of one or another approach to model construction and use.

GIS Based Groundwater Modeling Study to Assess the Effect of Artificial Recharge: A Case Study from Kodaganar River Basin, Dindigul District, Tamil Nadu

ABSTRACT Groundwater is a dynamic and replenishable natural resource. The numerical modeling techniques serve as a tool to assess the effect of artificial recharge from the water conservation structures and its response with the aquifers under different recharge conditions. The objective of the present study is to identify the suitable sites for artificial recharge structures to augment groundwater resources and assess its performance through the integrated approach of Geographic Information System (GIS) and numerical groundwater modeling techniques using MODFLOW software for the watershed located in the Kodaganar river basin, Dindigul district, Tamil Nadu. Thematic layers such as geology, geomorphology, soil, runoff, land use and slope were integrated to prepare the groundwater prospect and recharge site map. These potential zones were categorized as good (23%), moderate (54%), and poor (23%) zones with respect to the assigned weightage of different thematic layers. The major artificial recharge structures like percolation ponds and check dams were recommended based on the drainage morphology in the watershed. Finally, a three-layer groundwater flow model was developed. The model was calibrated in two stages, which involved steady and transient state condition. The transient calibration was carried out for the time period from January 1989 to December 2008. The groundwater model was validated after model calibration. The prediction scenario was carried out after the transient calibration for the time period of year up to 2013. The results show that there is 15 to 38% increase in groundwater quantity due to artificial recharge. The present study is useful to assess the effect of artificial recharge from the proposed artificial structures by integrating GIS and groundwater model together to arrive at reasonable results.

The S-Cycle Performance Matrix: Supporting Comprehensive Sustainability Performance Evaluation of Technical Systems

In this paper, we present the first generic framework for selecting comprehensive material/energetic sustainability performance indicators SPIs for technical systems: the S-Cycle Performance Matrix S-CPMatrix. This novel matrix is comprised of 6 generic sustainability goals, 11 SPI archetypes, and 23 corresponding metrics identified from our previously developed model of technical system sustainability the S-Cycle, and is intended to support decision makers in meeting three identified criteria for comprehensive SPI sets: C1 inclusion of indicators measuring performance at all relevant scales; C2 inclusion of efficiency and effectiveness indicators; and C3 coverage of all system sustainability goals. We evaluated the matrix by interpreting and classifying 324 indicators currently applied to assess technical system sustainability performance in the literature, with 94.1% found to be fully classifiable with respect to the proposed goals and SPI archetypes following several refinements. The matrix is applicable to different systems, and may be considered to facilitate the selection of a holistic set of SPIs from different sources and evaluation approaches. Thus, it addresses a need for consistent yet flexible guidance on how to comprehensively assess technical system sustainability performance, mirroring generic guidelines on organizational SPI selection widely available through several international initiatives. In addition to industrial evaluation of the S-CPMatrix, four avenues for future research are proposed: i use of the matrix for systems comparison/benchmarking; ii further investigation of unsupported metrics; iii the nature and measurement of contaminants; and iv assessing the comprehensiveness of current SPI sets for technical systems.

Modeling and Simulation of Microgrid

Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control and production. To deal with this change, one needs to interpret the electrical grid as a system of systems (SoS) and build new models that capture the dynamic behavior of the microgrid. In this paper, different models of electric components in a microgrid are presented. These models use complex system modeling techniques such as agent-based methods and system dynamics, or a combination of different methods to represent various electric elements. Examples show the simulation of the solar microgrid is presented to show the emergent properties of the interconnected system. Results and waveforms are discussed.

Rendering Systems Visible for Design: Synthesis Maps as Constructivist Design Narratives

Synthesis maps integrate research evidence, system expertise, and design proposals into visual narratives. These narratives support communication and decision-making among stakeholders. Synthesis maps evolved from earlier visualization tools in systemics and design. They help stakeholders to understand design options for complex sociotechnical systems. Other visual approaches map complexity for effective collaboration across perspectives and knowledge domains. These help stakeholder groups to work in higher-order design contexts for sociotechnical or human-ecological systems. This article describes a constructivist pedagogy for collaborative learning in small teams of mixed-discipline designers. Synthesis mapping enables these teams to learn systems methods for design research in complex problem domains. Synthesis maps integrate knowledge from research cycles and iterative sensemaking to define a coherent design narrative. While synthesis maps may include formal system modeling techniques, they do not require them. Synthesis maps tangibly render research observations and design choices. As a hybrid system design method, synthesis maps are a contribution to the design genre of visual systems thinking.

Predicting the characteristics of wideband absorbance in ears with middle-ear pathologies

Wideband absorbance (WBA), a measure of the proportion of energy absorbed by the middle ear, is an emerging technology to detect middle-ear dysfunction. WBA results in display absorbance as a function of frequency. WBA characteristics in adults with normal middle-ear function show a broad peak between 2 and 4 kHz. While changes in WBA results in patients with middle-ear dysfunction have been described previously, the acoustical properties accounting for those changes have not been investigated. This study investigated the wideband acoustical properties of the middle ear using a spring-mass-friction mechanical system modeling technique. When the system undergoes simple harmonic motion with damping, its frequency of vibration is given by f =1/2π√(k/m), where k and m represent stiffness and mass of the system, respectively. In the presence of middle-ear dysfunction, the k to m ratio changes, resulting in reduced WBA amplitude and a change of the frequency of vibration as shown by a shift of the broad peak of WBA. This study demonstrated the applicability of this modelling technique to account for the changes in acoustical properties of the middle ear in the presence of middle ear dysfunction arising from Eustachian tube dysfunction, otosclerosis, ossicular-chain discontinuity, and cholesteatoma.

A control strategy for electro-magneto-mechanical system based on virtual system model

A new approach to the control of electro-magneto-mechanical system is proposed in this paper. Conventionally, these systems are controlled based on the Maxwell system model via an on-off or PID control technique, which displays acceptable performance in the low frequency region, but not in the high frequency region where position control performance is greatly degraded. In order to improve the performance, a newly developed virtual 2nd order system modeling technique, SSID, is adopted for a complex electro-magnetomechanical system in the study. This technique states that any unknown system exposed to a random disturbance with unknown intensity can be identified in terms of a virtual 2nd order system model via the inverse process of a certain stochastic analysis. As a typical hybrid system, a solenoid valve is used as the target electro-magneto-mechanical system to study the modeling of the virtual 2nd order system. In order to confirm the performance of the proposed control strategy, autotuning PID controller in PWM mode is utilized. Simulations based on the conventional Maxwell system model with control via the bang-bang, autotuning PID, and the proposed virtual 2nd order system model approaches are conducted using MATLAB Simulink. Performance of these three systems in the low and high frequency bands is also compared. The simulation results reveal that the control performance of the virtual 2nd order system model is much improved compared with that of the Maxwell system model under autotuning PID and bang-bang controls in both low and high frequency regions, where the error is drastically reduced to approximately 1/5 of the original value.

Modeling‐Simulation‐Analysis‐Looping: 21st Century Game Changer

AbstractWhat if you could present to your boss and customers the expected performance characteristics of a new system in its target mission environment during concept development and before the first requirement or line of software is written? This paper follows up our IS 2014 paper, Uncertainty Quantification (UQ) in Complex System of Systems (SoS) Modeling and Simulation (M&S) Environments, and presents a new technique for system modeling and simulation that enhances insight and decision making based on quantified performance data. We describe how graph computing technology, readily available open source toolboxes and different systems thinking can “change the game” for systems engineers in the 21st Century.

Fundamental Limits of Data Analytics in Sociotechnical Systems

In the Big Data era, informational systems involving humans and machines are being deployed in multifarious societal settings. Many use data analytics as subcomponents for descriptive, predictive, and prescriptive tasks, often trained using machine learning. Yet when analytics components are placed in large-scale sociotechnical systems, it is often difficult to characterize how well the systems will act, measured with criteria relevant in the world. Here, we propose a system modeling technique that treats data analytics components as `noisy black boxes' or stochastic kernels, which together with elementary stochastic analysis provides insight into fundamental performance limits. An example application is helping prioritize people's limited attention, where learning algorithms rank tasks using noisy features and people sequentially select from the ranked list. This paper demonstrates the general technique by developing a stochastic model of analytics-enabled sequential selection, derives fundamental limits using concomitants of order statistics, and assesses limits in terms of system-wide performance metrics like screening cost and value of objects selected. Connections to sample complexity for bipartite ranking are also made.

Design of the Artificial Bone Scaffolds Based on the Multi-field Coupling Model

The integration design approach for the Artificial Bone Scaffolds based on the Computer Aided Design (CAD), Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD) is developed, and the multi-field coupling Scaffolds Modelling process is proposed. In modelling process, the complicated bone scaffolds can be designed with CAD tools, and FEA and CFD are the tools to analyse the mechanical and internal micro circulation properties. The CAD tools present the geometries and internal microstructure of bone scaffolds, and then export the models to CFD and FEA tools for properties analysis, such as the stress and strain of the blood against the channels, the flow velocity of blood. Three partial models composed the whole design approach, namely activity model, technical system model, and the information model. A special design case on the artificial bone scaffolds model demonstrates that this integration approach can facilitate to build the multi-physics fields coupled bone scaffolds.

An Emerging Industrial Business Model considering Sustainability Evaluation and using Cyber Physical System Technology and Modelling Techniques

There is a kind of movement for people to consider sustainability in all their daily activities, not only in the acquisition of goods and services but including the care for health. This induces the emergence of a new business model. Any type of production system (PS) should thus consider sustainability in all its activities and a relationship, which includes not only customers but suppliers, and partners involved from conception, design, manufacturing and provide even service after the use and the disposal of the products. Therefore, an evaluation procedure for PS is proposed to monitor four aspects of sustainability, namely: environmental, social, economical and technological. A framework is considered to deal with the concepts of sustainability and to evaluate performance in industrial PSs considering the indicators to qualify and to quantify their sustainability. It adopts the Petri net technique and extensions of the ANSI/ISA95; Cyber Physical System (CPS) and Smart Grid resources are also considered to treat information processing, storage and access flows by each system component and a Cloud Computing System to connect dispersed PSs. In addition, an evaluation process is systematized to support sustainable PSs by the rational use of production resources and the environment, to guarantee the safety of employees and to maintain the economic profitability of the company, thus, making production processes more efficient.

A state reduction approach to interpretation of system behavior based on system simulation

System modeling and simulation techniques have been introduced to the early stage of model-based design in product development. Given a description of a system in terms of system components and connections, these techniques compute its numerical behavior by building and solving an equation system derived from the topology of system components. Currently, these techniques have a limitation in supporting designers to interpret the overall behavior of a system in terms of semantics rather than numerical values. To overcome the limitation, the paper briefly introduces a semi-automated procedure to support designers to validate a product model with system simulation results. The procedure reduces the dynamic state of a system by clustering the behavior of system components in terms of their parameters at discrete time intervals. It also generates the overall system behavior by computing clustering patterns and its transitions and identity relations across intervals in the entire simulation duration.

A49 Visualizing active travel sentiment in an urban context

Abstract Background Commuting stress is known to be a root of many problems, including cardiovascular problems such as increased heart rate, increased blood pressure, back problems, as well as certain types of cancer. Examining how travel stress (or enjoyment) varies through time and space may reveal previously unknown relationships among active commute modes, intangible/tangible urban context, and daily weather. The present study set out to investigate these linkages using Geographic Information Systems (GIS) and space-time modeling techniques. Methods This research utilized exploratory spatiotemporal analysis and modeling of alternative commuter sentiment. Social media data was retrieved from twitter users in 6 metropolitan cities in the U.S., emotional content in the form of pleasure/arousal/dominance was extracted from it, spatial and aspatial data was obtained from various federal and local sources. A spatiotemporal weight was developed based on the space and time differentials for each daily tweeter. Exploratory analysis consisted of analyzing spatiotemporal patterning of tweeters using GIS techniques. Specifically, activity space analysis, surface estimation, spatial autocorrelation analysis (Getis-Ord hotspot analysis), global regression, and a spatiotemporal regression model was instituted. Results Preliminary exploratory analysis found that the majority of commuters tweeted once per day. When tweets were examined yearly, it was found that seasonality greatly affected commute sentiment patterns. Space-time weighted activity spaces varied significantly from un-weighted spaces. Furthermore, a weighted inverse-distance-weighted model demonstrated that travel mode affects human sentiment in both time and space. When hotspot analysis was visually examined in 3D, unique sentiments based on travel mode were observed in space and time. A global spatiotemporal regression model (OLS) indicated that weather, car usage, and mass-transit affected human sentiment (p-value Conclusions This analytical framework should be of interest to transportation planners seeking to understand how to promote alternative travel modes as well as health policy researchers studying the effect of commuting infrastructure on commuter’s well-being using a live, streaming dataset.

Modeling and Synchronous Control of Dual Mechanically Coupled Linear Servo System

This paper presents a system modeling technique for a high-speed gantry-type machine tool driven by linear motors. One feed axis of the investigated machine tool is driven by the joint thrust from two parallel linear motors. These two parallel motors are coupled mechanically to form the Y-axis while another standalone motor fixed to a support forms the X-axis. The components in the X-axis, which is treated as the mechanical coupling, are carried by the slides of the Y-axis motors. This configuration is applied to improve the dynamic stiffness of the system and operation speed/acceleration. However, the precise synchronous control of the two parallel and coupled motors would be the major challenge. To overcome this challenge, a multivariable system identification method is developed in this paper. This method is used to construct an accurate system mathematical model for the target coupled system. A synchronous control scheme is then applied to the model obtained using the proposed technique. The performance of the system is experimentally verified with a high-speed S-curve motion profile. The results substantiate the constructed system model and demonstrate the effectiveness of the control scheme.

Agglomerate modeling of cathode catalyst layer of a PEM fuel cell by the lattice Boltzmann method

Lattice Boltzmann method is a relatively new micro-scale modeling technique for complex fluid systems that offers several advantages over more conventional numerical methods and hence has attracted interest of researchers. However, lattice Boltzmann modeling of PEM fuel cell electrodes has faced with some challenges in tackling electrochemical reaction in the catalyst layer. In the present study, a two-dimensional single-phase lattice Boltzmann agglomerate model of the cathode catalyst layer of a PEM fuel cell is developed for the first time. After validation of different aspects of the model, the reactive air flow in a cathode electrode is simulated through a multi-scale lattice Boltzmann approach. Species distributions in the pore region, electrical potential distribution in the electrolyte film, and current density distribution at the interface of membrane and catalyst layer are presented and analyzed; both uniform and non-uniform agglomerate arrangements are simulated. The novel model can be adopted to simulate reactive flow in a cathode catalyst layer with a more complicated arrangement of agglomerates representing a realistic microstructure.

English

Safe operation of elements of power systems plays a crucial role in maintaining the reliability and safety of the system. Transformers being a key element in power systems need to be maintained and monitored on a regular basis. Dissolved gas analysis has been used as a reliable tool in maintaining the safe operation of transformers for a long time. Analysis of dissolved gases is analytical and often interpreted differently by different users and methods. The scope of Artificial Intelligence tools in dissolved gas analysis has become critical with increasing number of transformers being used in power systems coupled with rapid expansion of transmission and distribution components. Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique has emerged as one of the soft computing modeling technique for power transformer. An ANFIS model for dissolved gas analysis of power transformers is implemented.  Similarly the GA-based weight optimization during training of an ANN is employed to improve diagnostic accuracy. A Graphical User Interface (GUI) is designed using Matlab to help in the seamless integration of analysis and decision making. The user interface is simple and easy to use providing the user flexibility and wide options for analysis. Traditional methods like Rogers Ratio, Key Gas Method, IEC Ratio Method, Dorenburg Ratio Method, Total Dissolved Combustible Gases Method and Triangle Method. The tools also incorporate fuzzy based analysis based on Rogers’s ratios and Key Gas methods and analysis using Artificial Neural Networks. The primary motivation for the work is to provide a platform for analysis of dissolved gases to help in the early detection and diagnosis of transformer faults. This work is carried out with assistance from Andhra Pradesh State Transmission Corporation (APTRANSCO) in the form of required transformer analysis data and expert opinion for validation of the tool.   Key words: Transformer faults, expert system, Matlab, graphical user interface (GUI), fuzzy, artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm- artificial neural networks (GA-ANN).     &nbsp

Holistic Ontology-Based Assistance System for Efficient Process Model Parameter Identification

Accurate models of technical systems are the basis for many tasks like system analysis, predictions, or controller design. Usually, the values of several important parameters cannot be determined by theoretical analysis only; instead, process identification is necessary. For several applications, the efficiency of the identification procedure is very important, for example, for the creation of thermal models of machine tools, because of the large time constants and the expensive machine time. The goal of the authors is the support of this task as far as possible by software. This paper contributes to that goal twofold: on the one hand, it provides a collection of influences which have to be considered for supporting the identification procedure. On the other hand, concepts for computer-based support are presented—ontologies and automatic design methods based on evolutionary algorithms.

DesktopLabs] Desktop Laboratories: Web Share and Additive Manufacture of Engineering Educational Models

Physical models are desirable in engineering education as they enhance visual literacy, provide insight into critical design failure modes and design attributes, and can be implemented in a classroom scenario in concert with multimedia displays and lecture slides. Despite the advantage, such physical models are often: not commercially available; are excessively costly; are not tailored to the intended learning outcomes; or are difficult to share across multiple geographic locations. In this work, the authors have developed an approach to address these shortcomings by exploiting the emerging capabilities of additive manufacturing equipment, increasingly present within educational institutions. The [DesktopLabs] project engages students to develop and additively manufacture physical models, to allow in-class demonstration of complex systems that would not be otherwise possible. By engaging with physical models of technical systems, students enhance their capacity to engage with emerging engineering technologies, and to solve new and complex problems. Combined with access to online sharing facilities, [DesktopLabs] allow web share of the developed model files, thereby allowing ready dissemination amongst other educators for additive manufacture in other geographic locations. Although applied in an engineering context, the benefit of [DesktopLabs] are directly transferrable to teaching areas to aid in the education of successive generations of undergraduate and postgraduate students.