Meta-modeling Tool Research Articles

Sensitivity and Optimization Analysis of Torsional Behavior in Multicellular Thin-Walled Tubes

Multicellular thin-walled tubes are widely used due to their lightweight, economical design, and superior shear and torsional performance. Their design is sometimes governed by the available materials and the required dimensions. The current study uses advanced sensitivity analysis with meta-modeling tools to understand how different geometric and mechanical factors affect the torsional performance of multicellular thin-walled tubes. The geometric factors include the length, thickness, and width of the beams, while the mechanical properties involve the shear modulus. Variance-based sensitivity analysis is used to assess how variations in these factors impact the rate of twist, torsional stiffness, and shear stress. The interconnected relations between input parameters are exploited for optimal design and superior performance. The results revealed that for a three-celled tube, thick horizontal interior elements with thin deep vertical elements and thin exterior elements provide an optimal design when the cross-sectional area is constrained. This finding, combined with varying the geometrical and material properties, results in an optimal design using CFRP composites when constrained by minimizing the total weight and superior torsional performance. The analysis can be extended to include other constraint(s), but changing the design constraints might change the optimal design. Doi: 10.28991/CEJ-2024-010-09-09 Full Text: PDF

A Graph Transformation Approach for Modeling UML Diagrams

This article proposes an approach for transforming UML Statecharts, Sequence Diagrams and activity Diagrams to Time Petri Nets with Action Duration (DTPN) using graph transformation. By this transformation the author aims to bridge the gap between semi-formal models generated by UML and formal models DTPN. UML is considered to be the standardized language for modelling and describing systems behaviors for analysis. In other hand, DTPN models are tools for the specification and performance analysis of distributed and concurrent systems. However, the proposed approach allows to generate automatically a visual modeling tool for DTPN. The cost of building a visual modeling tool from scratch is prohibitive. Meta-modeling approach is useful to deal with this problem since it allows the modeling of the formalisms themselves, by means of graph grammars. The meta-modeling tool AToM3 is used.

Simulation optimization using stochastic kriging with robust statistics

Metamodels are widely used as fast surrogates to facilitate the optimization of simulation models. Stochastic kriging (SK) is an effective metamodeling tool for a mean response surface implied by stochastic simulation. In SK, it is usually assumed that the experimental data are normally distributed and uncontaminated. However, these assumptions can be easily violated in many practical applications. This paper proposes a new type of SK for simulation models that may have non-Gaussian responses; this new SK uses robust estimators of location (or central tendency) and scale (or variability) that are well-known in the literature on robust statistics. Statistical properties of the robust estimators used in this paper are briefly analyzed and the performances of the proposed methods are compared through numerical examples of different features. The comparison results show that the proposed robust SK with the robust estimators is quite efficient, no matter whether the standard assumptions hold or not.

What do practitioners expect from the meta-modeling tools? A survey

Modeling languages are defined with a meta-model, which are specified using the meta-modeling tools that produce the editors for specifying models in accordance with the meta-models. In this paper, we aim to understand the top-used meta-modeling tools and practitioners’ expectations and challenges with respect to different requirements. So, we designed and conducted a survey, which was responded by 103 practitioners. The questionnaire considers the notation, semantics, editor services, model-transformation, validation, testing, and composability requirements. Some of the results are as follows: the top-used meta-modeling tools are Sirius and GEMS, Metaedit+, Xtext, and Microsoft DSL tools respectively. The top-preferred language visualizations are diagrammatic or textual. Practitioners prefer the editors with free-editing mode and are not familiar with projectional editing that promotes hybrid modeling. Translational semantics definition (i.e., model-to-model and model-to-text) is more popular than the interpretative semantics definition. Importing/exporting models and meta-models is highly important for facilitating the communication and collaboration. Modeling and meta-modeling with re-use and their versioning are among the top-desired features. Practitioners are willing to integrate any validation tools (e.g., model checkers) to define the languages’ semantical and structural validation rules and prove the model correctness. Lastly, defining the language semantics by composition and testing the semantics definitions are crucial for many practitioners. The survey also reveals many important challenges in each type of requirements. The survey results will be useful for meta-modelers in analyzing the meta-modeling tools and tool vendors in learning practitioners’ expectations.

Artificial intelligence-based predictive model of nanoscale friction using experimental data

A recent systematic experimental characterisation of technological thin films, based on elaborated design of experiments as well as probe calibration and correction procedures, allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters, comprising normal forces, sliding velocities, and temperature, thus providing an indication of the intricate correlations induced by their interactions and mutual effects. This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts. Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data, meta-modelling tools fail, however, at predicting the factual behaviour. Based on the acquired experimental data, data mining, incorporating various state-of-the-art machine learning (ML) numerical regression algorithms, is therefore used. The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation. It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels, but the intrinsic nature of such algorithms prevents their usage in most practical applications. Genetic programming-based artificial intelligence (AI) methods are consequently finally used. Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements, the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy, depending on the sample type, between 72% and 91%, allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters. An effective tool for nanoscale friction prediction, adaptive control purposes, and further scientific and technological nanotribological analyses is thus obtained.

Model Driven Software Engineering Meta-Workbenches: An XTools Approach

Model Driven Software Engineering aims to provide a quality assured process for designing and generating software. Modelling frameworks that offer technologies for domain specific language and associated tool construction are called language workbenches. Since modelling is itself a domain, there are benefits to applying a workbenchbased approach to the construction of modelling languages and tools. Such a framework is a meta-modelling tool and those that can generate themselves are reflective metatools. This article reviews the current state of the art for modelling tools and proposes a set of reflective meta-modelling tool requirements. The XTools framework has been designed as a reflective meta-tool and is used as a benchmark.

Koopman Mode Analysis of agent-based models of logistics processes.

Modern logistics processes and systems can feature extremely complicated dynamics. Agent Based Modeling is emerging as a powerful modeling tool for design, analysis and control of such logistics systems. However, the complexity of the model itself can be overwhelming and mathematical meta-modeling tools are needed that aggregate information and enable fast and accurate decision making and control system design. Here we present Koopman Mode Analysis (KMA) as such a tool. KMA uncovers exponentially growing, decaying or oscillating collective patterns in dynamical data. We apply the methodology to two problems, both of which exhibit a bifurcation in dynamical behavior, but feature very different dynamics: Medical Treatment Facility (MTF) logistics and ship fueling (SF) logistics. The MTF problem features a transition between efficient operation at low casualty rates and inefficient operation beyond a critical casualty rate, while the SF problem features a transition between short mission life at low initial fuel levels and sustained mission beyond a critical initial fuel level. Both bifurcations are detected by analyzing the spectrum of the associated Koopman operator. Mathematical analysis is provided justifying the use of the Dynamic Mode Decomposition algorithm in punctuated linear decay dynamics that is featured in the SF problem.

Embedded System Verification Using Formal Model an Approach Based on the Combined Use of UML and Maude Language

The approach proposed in this article presents a formal verification of embedded systems. The method relies on an automated modeling and code generation based on the systems' behavior. The key concept is the combined use of a subset of UML behavior diagrams extended with timing annotations (Real-Time Statechart and Real-Time Collaboration diagrams) for system modeling and the Maude language for verification. First, UML modeling tools are developed. Then, an automatic generation of equivalent Maude specification is performed. The approach is based on code generation. This is why it is possible to use an available model checking tool to verify certain timed properties represented in Linear Temporal Logic (LTL). The meta-modeling tool ATOM3 is used. A case study is presented to illustrate the feasibility of the approach.

Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

A Posteriori Typing for Model-Driven Engineering

Model-Driven Engineering (MDE) is founded on the ability to create and process models conforming to a meta-model. In this context, classes in a meta-model are used in two ways: as templates to create objects and as (static) classifiers for them. These two aspects are inherently tied in most meta-modelling approaches, which results in unnecessarily rigid systems and hinders reusability of MDE artefacts. In this work, we discuss the benefits of decoupling object creation from typing in MDE. Thus, we rely on standard mechanisms for object creation, and propose a posteriori typing as a means to retype objects and enable multiple, partial, dynamic typings. This approach enhances flexibility; permits unanticipated reuse, as model management operations defined for a meta-model can be reused with other models once they get reclassified; and enables bidirectional model transformation by reclassification. In particular, we propose two mechanisms to realise model retyping and show their underlying theory and analysis methods. We show the feasibility of the approach by an implementation atop our meta-modelling tool M eta D epth and present several applications of retypings (transformations, reuse, and dynamicity).

Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation

Stochastic kriging (SK) methodology has been known as an effective metamodeling tool for approximating a mean response surface implied by a stochastic simulation. In this paper we provide some theoretical results on the predictive performance of SK, in light of which novel integrated mean squared error-based sequential design strategies are proposed to apply SK for mean response surface metamodeling with a fixed simulation budget. Through numerical examples of different features, we show that SK with the proposed strategies applied holds great promise for achieving high predictive accuracy by striking a good balance between exploration and exploitation.

Efficient VaR and CVaR Measurement via Stochastic Kriging

In this paper, stochastic kriging SK is considered as a metamodeling tool to capture risk-related properties of complex stochastic system outputs. To better assess the tail behavior of the underlyi...

Efficient VaR and CVaR Measurement via Stochastic Kriging

In this paper, stochastic kriging (SK) is considered as a metamodeling tool to capture risk-related properties of complex stochastic system outputs. To better assess the tail behavior of the underlying distribution of a system output, we specifically focus on global prediction of value-at-risk and conditional value-at-risk. Going beyond the standard SK framework intended for metamodeling of mean response surfaces, we rethink the original formulation to allow for the flexibility of utilizing different estimation methods for metamodel construction. The resulting impact on the predictive performance of SK is examined in detail. In parallel with the study by Chen et al. [Chen X, Ankenman BE, Nelson BL (2013) Enhancing stochastic kriging metamodels with gradient estimators. Oper. Res. 61(2):512–528], we further consider the situation in which noisy gradient information can be incorporated into SK metamodel construction and prediction. The theoretical results are illustrated by two numerical examples.

Modeling the change in particle size distribution in a gas-solid fluidized bed due to particle attrition using a hybrid artificial neural network-genetic algorithm approach

Particle size distribution (PSD) is an important parameter in gas-solid fluidized bed. The change in PSD due to particle attrition can affect the long-term performance of fluidized bed. In this study, artificial neural network (ANN) with genetic algorithm (GA) as a meta-modeling tool was employed to model the change in PSD during fluidization. Experiments were conducted using incineration bottom ash (IBA) as the fluidizing particles and different mass percentage of large and small glass beads were used as the grinding medium. Rosin–Rammler (RR) distribution was used to describe the IBA PSD. The ANN-GA models developed were subsequently used to study the effect of fluidization time, mass percentage of glass beads and size of glass beads used on the IBA particle attrition during fluidization.

Diagrammatic Development of Domain Specific Modelling Languages with WebDPF

Domain specific modelling languages reduce the effort required to specify systems by providing higher level abstractions. Although this is a promising approach for reducing the complexity of system specifications, creating a new domain specific modelling language is a complex task. WebDPF is a web-based metamodelling tool that aims to reduce the effort required to develop domain specific modelling languages. The proposed technique is based on model transformations which enhances a modelling language with auto-completion. WebDPF supports multilevel metamodelling, diagrammatic specification of model constraints, and development, simulation and analysis of model transformation systems. The authors study the termination of the underlying model transformation systems and provide sufficient condition for termination. The tool is equipped with a scalable model navigation facility that enables a modeler to deal with large models.

A hybrid switching predictive controller with proportional integral derivative gains and GMDH neural representation of automotive engines for coldstart emission reductions

In this research, a high-performance predictive controller is developed for automotive coldstart emission reductions. The proposed control scheme combines a hybrid switching predictive controller (HSPC) with proportional integral derivative (PID) gains to simultaneously minimize the cumulative hydrocarbon emissions (HCcum) and the control input variations for a given engine during the coldstart operation. It is essential to use a sufficiently accurate surrogate meta-representation of the real engine within this model-based controller to predict the states of the plant and impart proper control commands to the system. The existing studies in the research literature have clearly demonstrated that automotive engines have a highly transient nonlinear behavior during coldstart periods and different disturbances can affect their operations. To cope with the mentioned difficulties, several coldstart experiments are performed to capture a comprehensive database for the considered engine. Thereafter, a powerful knowledge-based black-box meta-modeling tool, known as group method data handling (GMDH), is adopted to have a neural representation of the engine׳s coldstart behavior. As a real-time controller, the proposed PID-based HSPC requires a fast and robust solver to calculate the gains of PID in a computationally efficient manner. Here, a multivariate quadratic fit-sectioning algorithm (MQFSA) is proposed to deterministically determine the control commands. Other than the considered online optimizer, a powerful chaos-enhanced evolutionary algorithm (CEA) is used to heuristically optimize the prediction horizon (HP) and control commands horizon (HU) to achieve the best results. It is demonstrated that using such an optimizer, instead of trial-and-errors, to heuristically set the control and plant prediction horizon lengths is an effective strategy. Finally, several comparative studies are conducted to further indicate the efficacy of the proposed PID-based HSPC for the automotive coldstart control problem.

Real-time simulation techniques for augmented learning in science and engineering

In this paper, we present the basics of a novel methodology for the development of simulation-based and augmented learning tools in the context of applied science and engineering. It is based on the extensive use of model order reduction, and particularly, of the so-called proper generalized decomposition method. This method provides a sort of meta-modeling tool without the need for prior computer experiments that allows the user to obtain real-time response in the solution of complex engineering or physical problems. This real-time capability also allows for its implementation in deployed, touch screen, handheld devices or even to be immersed into electronic textbooks. We explore here the basics of the proposed methodology and give examples on a few challenging applications never until now explored, up to our knowledge.

Comprehensive Tool Support for Enterprise Modeling and Evaluation

Enterprise modelling and evaluating subsequently created models is manifold. Enterprise modelling means capturing an organization's business processes, its organizational structure, its corporate strategy, or its supporting information systems in graphical conceptual models. Evaluating these models means assuring their structural and semantic quality as well as their user group adequacy. Enterprise modelling and model evaluation requires comprehensive tool support. Recently, a number of modelling tools have been proposed each addressing particular aspects of enterprise modelling or model evaluation. For example, meta-modelling tools allow for defining new and altering existing modelling languages. Meta-modelling tools are thus well suited for enterprise modelling as this requires a large number of different modelling languages that may need to be adapted to the particular enterprise. In contrast, some modelling tools provide mechanisms to structurally or semantically evaluate models of a predefined language. However, comprehensive tool support for enterprise modelling and model evaluation is missing up to now. With this paper, the authors aim at closing this research gap by proposing a tool combining meta-modelling capabilities with features to structurally and semantically evaluate models as well as to manage model perspectives. The tool thus covers the entire model lifecycle from defining a problem-suitable modelling language, creating models, configuring them for different user groups, and evaluating them in terms of structure and semantics.

Meta-Model Flexibility as a Significant Factor for Increasing End-User Involvement

This paper describes experiences of using meta-modelling tools supporting technology enhanced learning in European Commission's project NEXT-TELL. Due to a target group of the project (teachers with limited experience in modelling) it was very important to provide them with easy to use environment. This resulted in trying to identify least best fit of features needed by teachers while removing “distractions” - both in regards to tool functionality and modelling method applied. Such adjustments of the modelling tool were possible thanks to the underlying ADOxx® meta-modelling platform that allowed adjustment to feedback received from end users/subject matter experts in terms of functionality and usability.

From UML 2.0 diagrams to aspect oriented diagrams using graph transformation

Aspect-oriented modelling (AOM) is a paradigm aiming to deal with the limits of object-oriented modelling and for solving the problem of interaction or separation (crosscutting) between base (functional) and aspectual (not functional) model. In this paper, we propose an approach that transforms some UML 2.0 diagrams to aspect-oriented diagrams using graph transformation. To realise this work, we have proposed a meta-model for each UML diagram and Aspect models (diagram of aspect class, activity, and communication) and a graph grammar that performs the transformation process. We have used the meta-modelling tool ATOM3 and python language to implement our transformations. A detailed example is presented to illustrate our approach.