System Modeling Techniques Research Articles

An efficient MABC-ANN technique for optimal management and system modeling of micro grid

In this paper, a hybrid technique is proposed for the system modeling and energy management of Microgrid (MG) connected sources. The proposed hybrid technique is the hybridization of Artificial Bees Colony (ABC) with Bat Search Algorithm (BAT) and Artificial Neural Network (ANN). Here, the novelty of the proposed method is that the search space of the scout bees is gained with the utilization of the BAT and the optimal solution is found from this scout bees search space. The performance of the scout bees is generously enhanced by BAT. So the combination of ABC and BAT is called as MABC technique. The proposed algorithm optimizes the configuration of the MG sources combination by considering the multi-objective function, while fulfilling the load demand. The considered MG connected system such as Wind Turbine, Photovoltaic array, Fuel Cell, Micro Turbine, Diesel Generator and battery storage. Here, ANN is utilized for the prediction of the required load demand by using the inputs of MG connected sources. Based on the load demand, MABC technique is utilized to choose an optimal configuration of MG, i.e., fuel cost minimization, emission factors, operating, and maintenance cost. The proposed technique is executed in MATLAB/Simulink platform and compared with existing strategies. In this paper, the cost accuracy percentage (CAP) for proposed and the existing technique is also investigated, the proposed technique achieves 7.37880 %.

Influence of Power Take-Off Modelling on the Far-Field Effects of Wave Energy Converter Farms

The study of the potential impact of wave energy converter (WEC) farms on the surrounding wave field at long distances from the WEC farm location (also know as “far field” effects) has been a topic of great interest in the past decade. Typically, “far-field” effects have been studied using phase average or phase resolving numerical models using a parametrization of the WEC power absorption using wave transmission coefficients. Most recent studies have focused on using coupled models between a wave-structure interaction solver and a wave-propagation model, which offer a more complex and accurate representation of the WEC hydrodynamics and PTO behaviour. The difference in the results between the two aforementioned approaches has not been studied yet, nor how different ways of modelling the PTO system can affect wave propagation in the lee of the WEC farm. The Coastal Engineering Research Group of Ghent University has developed both a parameterized model using the sponge layer technique in the mild slope wave propagation model MILDwave and a coupled model MILDwave-NEMOH (NEMOH is a boundary element method-based wave-structure interaction solver), for studying the “far-field” effects of WEC farms. The objective of the present study is to perform a comparison between both numerical approaches in terms of performance for obtaining the “far-field” effects of two WEC farms. Results are given for a series of regular wave conditions, demonstrating a better accuracy of the MILDwave-NEMOH coupled model in obtaining the wave disturbance coefficient (Kd) values around the considered WEC farms. Subsequently, the analysis is extended to study the influence of the PTO system modelling technique on the “far-field” effects by considering: (i) a linear optimal, (ii) a linear sub-optimal and (iii) a non-linear hydraulic PTO system. It is shown that modelling a linear optimal PTO system can lead to an unrealistic overestimation of the WEC motions than can heavily affect the wave height at a large distance in the lee of the WEC farm. On the contrary, modelling of a sub-optimal PTO system and of a hydraulic PTO system leads to a similar, yet reduced impact on the “far-field” effects on wave height. The comparison of the PTO systems’ modelling technique shows that when using coupled models, it is necessary to carefully model the WEC hydrodynamics and PTO behaviour as they can introduce substantial inaccuracies into the WECs’ motions and the WEC farm “far-field” effects.

Evaluating the Cost of Prevention Programming and Universal Screening with Discrete Event Simulation.

Discrete Event Simulation (DES) is a novel system modeling technique that allows for the evaluation of the potential costs and personnel needed for mental health services in school. A case study is presented to illustrate how DES could be used by a school's decision makers to help plan for implementation of an integrated mental health service model. Discrete Event Simulation was used to model the personnel, time, and costs of an integrated mental health service model within a school setting. In addition, costs are calculated and then compared to a business as usual model. Data from the present investigation indicate substantial cost savings of implementing a prevention oriented mental health intervention model within a school setting. In a school of 1000 students, the prevention model could result in an annual cost savings of approximately $30,000 as well as a 50% reduction in disciplinary referrals and 22% reduction in suspensions. Results from the present investigation indicate substantial savings in financial resources and overall numbers of disciplinary infractions when implementing a prevention model. The DES allows for customization of personnel and time to modify the model and resulting output to local conditions. These data may allow school administrators to modify resources to meet student needs. In addition, cost data can help address some of the common implementation barriers associated with adoption of universal screening and preventative mental health services.

Experimental and Analytical Investigation of a RC Wall with a Gabion Cushion Subjected to Boulder Impact

An experimental investigation involving the use of a full-scale pendulum device to deliver solid object impact on a reinforced concrete (RC) barrier specimen that was fitted with a layer of gabion cushion is reported in this paper. The deflection behaviour of the stem wall of the RC barrier including the tensile strains developed in the longitudinal reinforcement was of interests. Results recorded from the tests are compared with results from control experiments which were without the protection of any cushion materials. The introduction of a layer of cushion is shown to be able to have the contact force reduced by more than 95% and deflection demand reduced by about 70%. An analytical procedure employing the Hunt and Crossley contact model, Swiss code model and two-degrees-of-freedom (2DOF) system modelling technique is presented for calculating the flexural response behaviour of the cushioned barrier and validated by comparison with experimental measurements. An important outcome from the study is a simple hand calculation procedure featuring the use of a closed form expression along with a design chart which is illustrated by a worked example, to facilitate uptake in design practices.

Evaluation of System Modelling Techniques for Waste Identification in Lean Healthcare Applications.

PurposeWaste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes.MethodsThis study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi.ResultsVSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste.ConclusionAs behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.

System model building and dynamic online control of traffic flow

ABSTRACT The Underground transportation system plays a vital role in public transportation and exhibits complicated dynamics. The model predictive control approach for underground train dispatching is proposed in this paper. The system modelling technique spatially aggregates trains to generate the traffic flow model in a network of interconnected control units. The state-space model for the underground train traffic of a metro line is investigated. Simulation results are reported to demonstrate the effectiveness of the optimization control model.

Learning Fuzzy Automaton's Event Transition Matrix When Post-Event State Is Unknown.

Compared to other system modeling techniques, the fuzzy discrete event systems (FDESs) methodology has the unique capability of modeling a class of event-driven systems as fuzzy automata with ambiguous state and event-invoked state transition. In two recent papers, we developed algorithms for online-supervised learning of the fuzzy automaton's event transition matrix using fuzzy states before and after the occurrence of fuzzy events. The post-event state was assumed to be readily available while the pre-event state was either directly available or estimatable through learning. This article is focused on algorithm development for learning the transition matrix in a different setting-when the pre-event state is available but the post-event state is not. We suppose the post-event state is described by a fuzzy set that is linked to a (physical) variable whose value is available. Stochastic-gradient-descent-based algorithms are developed that can learn the transition matrix plus the parameters of the fuzzy sets when the fuzzy sets are of the Gaussian type. Computer simulation results are presented to confirm the theoretical development.

Evaluation and prediction of COVID-19 in India: A case study of worst hit states.

In this manuscript, system modeling and identification techniques are applied in developing a prognostic yet deterministic model to forecast the spread of COVID-19 in India. The model is verified with the historical data and a forecast of the spread for 30-days is presented in the 10 most affected states of India. The major results suggest that our model can very well capture the disease variations with high accuracy. The results also show a steep rise in the total cumulative cases and deaths in the coming weeks.

Modelling evaporator in organic Rankine cycle using hybrid GD-LSE ANFIS and PSO ANFIS techniques

Internal combustion engines (ICEs) are likely to be used in heavy-duty applications for many years and it is important to continue improving their efficiency. One approach is to recover waste heat from the exhaust of heavy-duty diesel engines (HDDEs) using waste heat recovery (WHR) technologies. WHR based on Organic Rankine Cycle (ORC) is a promising technology, which offers potential to reduce the fuel consumption of HDDEs by transferring the wasted thermal energy to alternative useful electrical or mechanical energy. In the ORC, the evaporator is considered the most critical component, because it has a high thermal inertia. Previous numerical models of evaporator are computationally expensive due to non-linearity of evaporator governing equations and cannot be deployed for real-time control applications. This study uses an Adaptive Network-based Fuzzy Inference System (ANFIS) modelling technique to provide efficient control-oriented evaporator models for prediction of heat source and refrigerant temperatures at the evaporator outlet. Hybrid gradient decent, least square estimate (GD-LSE) and particle swarm optimization (PSO) algorithms for training the ANFIS model have been investigated and show that training ANFIS using the PSO method results in an improvement in accuracy. Furthermore, the systematic and adaptive approach of the ANFIS modelling technique makes the procedure of evaporator modelling less dependent on expert knowledge, reducing the modelling effort.

Approaches for Modelling the Physical Behavior of Technical Systems on the Example of Wind Turbines

Models of technical systems are an essential means in design and product-development processes. A large share of technical systems, or at least subsystems, are directly or indirectly connected with the generation or transformation of energies. In design science, elaborated modelling approaches were developed for different levels of product concretization, for instance, requirement models and function models, which support innovation and new product-development processes, as well as for energy-generating or -transforming systems. However, on one product-concretization level, the abstract level that describes the physical behavior, research is less mature, and an overview of the approaches, their respective advantages, and the connection possibilities between them and other modelling forms is difficult to achieve. This paper proposes a novel discussion structure based on modelling perspectives and digital-engineering frameworks. In this structure, current approaches are described and illustrated on the basis of an example of a technical system, a wind turbine. The approaches were compared, and their specific advantages were elaborated. It is a central conclusion that all perspectives could contribute to holistic product modelling. Consequently, combination and integration possibilities were discussed as well. Another contribution is the derivation of future research directions in this field; these were derived both from the identification of “white spots” and the most promising modelling approaches.

A System Dynamics Modeling and Computer-based Simulation in Forecasting Long-term Sufficiency: A Philippine Chicken Meat Sector Case Study

As the human population continues to grow, the global growth of the livestock sector will continue to rise as well. In the Philippines, the demand for chicken meat is projected to triple by 2050. In this study, the increasing consumption and long-term sufficiency were evaluated with the use of the system dynamics concept. With system modeling and computer-based simulation techniques, the available data on chicken meat supply chain were processed considering that factors behave dynamically. The simulated model facilitated the forecasting of key variables which may drop sufficiency from 87% in 2015 to 60% by 2050 if no proper actions take place in the areas of production and consumption. As a whole, this study developed and demonstrated preliminary system dynamics-based and computer based-approaches in order to understand the chicken meat sector. This showed that a dynamic systems-based paradigm shift in food and agricultural systems analysis can help address operational and strategic issues regarding food security.

From tolerance allocation to tolerance-cost optimization: a comprehensive literature review

It is widely acknowledged that the allocation of part tolerances is a highly responsible task due to the complex repercussions on both product quality and cost. As a consequence, since its beginnings in the 1960s, least-cost tolerance allocation using optimization techniques, i.e. tolerance-cost optimization, was continuously in focus of numerous research activities. Nowadays, increasing cost and quality pressure, availability of real manufacturing data driven by Industry 4.0 technologies, and rising computational power result in a continuously growing interest in tolerance-cost optimization in both research and industry. However, inconsistent terminology and the lack of a classification of the various relevant aspects is an obstacle for the application of tolerance-cost optimization approaches. There is no literature comprehensively and clearly summarizing the current state of the art and illustrating the relevant key aspects. Motivated to overcome this drawback, this article provides a comprehensive as well as detailed overview of the broad research field in tolerance-cost optimization for both beginners and experts. To facilitate the first steps for readers who are less familiar with the topic, the paper initially outlines the fundamentals of tolerance-cost optimization including its basic idea, elementary terminology and mathematical formulation. These fundamentals serve as a basis for a subsequent detailed discussion of the key elements with focus on the different characteristics concerning the optimization problem, tolerance-cost model, technical system model and the tolerance analysis model. These aspects are gathered and summarized in a structured mind map, which equips the reader with a comprehensive graphical overview of all the various facets and aspects of tolerance-cost optimization. Beside this, the paper gives a retrospect of the past fifty years of research in tolerance cost-optimization, considering 290 relevant publications. Based thereon, current issues and future research needs in tolerance-cost optimization were identified.

Malfunction analysis and safety of mathematical models of technical systems

In this article we discuss methods of computing the guaranteed values of the states of a technical system in order to estimate the safety of a technical system. For most models describing the problems of science and technology, it is necessary to analyze the effect of disturbances on the state of an object or system. It is necessary to evaluate the boundaries of the zones of dangerous states and the boundary values of the system parameters. The danger of a system functioning is a threat, possibility, probability of damage, system catastrophe, that is, potential damage to a technical system in certain conditions and situations. An analysis of the boundaries of the safety areas of technical systems helps to obtain quantitative estimates of the possibility of dangerous situations. Used methods are based on the approximation of the shift operator along the trajectory, and taking into account the influence of constantly acting perturbations on the solutions.

The Adjoint Petrov–Galerkin method for non-linear model reduction

We formulate a new projection-based reduced-order modeling technique for non-linear dynamical systems. The proposed technique, which we refer to as the Adjoint Petrov–Galerkin (APG) method, is derived by decomposing the generalized coordinates of a dynamical system into a resolved coarse-scale set and an unresolved fine-scale set. A Markovian finite memory assumption within the Mori–Zwanzig formalism is then used to develop a reduced-order representation of the coarse scales. This procedure leads to a closed reduced-order model that displays commonalities with the adjoint stabilization method used in finite elements. The formulation is shown to be equivalent to a Petrov–Galerkin method with a non-linear, time-varying test basis, thus sharing some similarities with the Least-Squares Petrov–Galerkin method. Theoretical analysis examining a priori error bounds and computational cost is presented. Numerical experiments on the compressible Navier–Stokes equations demonstrate that the proposed method can lead to improvements in numerical accuracy, robustness, and computational efficiency over the Galerkin method on problems of practical interest. Improvements in numerical accuracy and computational efficiency over the Least-Squares Petrov–Galerkin method are observed in most cases.

Machine learning based approach to detection of anomalous data from sensors in cyber-physical water supply systems

The paper proposes an approach to detection of anomalous data from sensors in cyber-physical systems on an example of a water supply supply system. The approach is based on methods of machine learning and modeling of technical systems. The primary data for machine learning was obtained on the developed software/hardware prototype of the water supply system by using a number of microcontrollers, sensors and actuators. The experiments confirmed the feasibility of the proposed approach. Several machine learning methods from scikit-learn library of the Python programming language were tested. As a result of the experiments we identified a learning method and its parameters ensuring the highest accuracy of the abnormal situations recognition.

ANFIS for predicting surface roughness in wire electric discharge machining of aerospace material

WEDM is an essential process for the manufacturing of complex aerospace components. In the present study, AA6063 is machined using four input machining variables namely pulse on time (Ton), pulse off time (Toff), peak current (Ip), and servo voltage (Vs) of WEDM. A full factorial design having four factors each at three levels is used to conduct the experiments. Surface roughness is employed as a process performance characteristic. An Adaptive Neuro-Fuzzy Inference System (ANFIS) modelling technique is used to predict the performance characteristic. Scanning electron microscope (SEM) is also used to examine the machined surfaces, machined at high discharge energy and low discharge energy.

Mathematical models of technical systems

Mathematical models of technical systems

Обеспечение производства безопасной цельносмолотой муки из пророщенного зерна пшеницы на основе принципов СМБПП

Современные производственные предприятия работают в условиях жесткой конкуренции. Для обеспечения стабильной работы и развития производства руководителям необходимо ставить задачи выпуска безопасной продукции при выполнении требований законодательства и поставки на рынок продукции, востребованной потребителем. Соблюдение требований ГОСТ Р ИСО 22000 Создание, внедрение, поддержка в рабочем состоянии системы менеджмента безопасности пищевых продуктов (СМБПП) особенно при производстве новых видов сырьевых ингредиентов и готовой продукции – это стратегическое решение, которое поможет предприятию вывести свою безопасную продукцию на продовольственный рынок и надежно закрепиться в качестве добросовестного производителя. В статье авторами предложено использование методологии моделирования IDEF0 для перевода стратегических планов на основе документов в формат моделей. Преимущества методологии IDEF0 заключается в хорошо протестированном языке и комплексной технике моделирования систем. Формируемые модели IDEF0 хорошо структурированы, просты для понимания, легко модифицируются и используются, при этом могут быть расширены до любой глубины детализации и позволяют комплексно структурировать весь процесс производства. Наличие модели IDEF0 облегчает изучение процесса производства, построение блок-схем, анализ рисков и разработку модели управления безопасностью производимых продуктов на основе принципов ХАССП. Авторами рассмотрен технологический процесс производства цельносмолотой муки из пророщенного зерна пшеницы с точки зрения безопасности получаемого сырьевого ингредиента. Разработана блок-схема процесса производства, на основе выявленных неприемлемых рисков были определены критические контрольные точки, каждая из которых задокументирована в виде плана ХАССП, что позволит производителю получать гарантированно безопасный сырьевой ингредиент и использовать его в производстве пищевых продуктов.
 Авторами рассмотрен технологический процесс производства цельносмолотой муки из пророщенного зерна пшеницы с точки зрения безопасности получаемого сырьевого ингредиента. Разработана блок-схема процесса производства, на основе выявленных неприемлемых рисков были определены критические контрольные точки, каждая из которых задокументированная в виде плана ХАССП, что позволит производителю получать гарантированно безопасный сырьевой ингредиент и использовать его в производстве пищевых продуктов.

Nowatorska metoda oceny niezawodności systemów wielostanowych w oparciu o algorytm uczenia struktury

Traditional reliability models, such as fault tree analysis (FTA) and reliability block diagram (RBD), are typically constructed with reference to the function principle graph that is produced by system engineers, which requires substantial time and effort. In addition, the quality and correctness of the models depend on the ability and experience of the engineers and the models are difficult to verify. With the development of data acquisition, data mining and system modeling techniques, the operational data of a complex system considering multi-state, dependent behavior can be obtained and analyzed automatically. In this paper, we present a method that is based on the K2 algorithm for establishing a Bayesian network (BN) for estimating the reliability of a multi-state system with dependent behavior. Facilitated by BN tools, the reliability modeling and the reliability estimation can be conducted automatically. An illustrative example is used to demonstrate the performance of the method.

Improving self-consumption of on-site PV generation through deploying flexibility in building thermal mass

As PV is considered prospective for decarbonizing the building sector, the self-consumption of on-site PV generation, which is susceptible to the temporal mismatch between solar radiation and energy demand, greatly affects the overall performance of improving building autonomy. As an important way to lessen the dependence of the decentralized PV energy system on the grid, internal flexibility from buildings is receiving increasing attention. This research sought to contribute to our understanding of the flexibility potential of Building Thermal Mass (BTM). In this aim, the load-shifting potential of BTM and the impacts on the self-consumption of rooftop PV generation were evaluated in a single-family residential building. To ensure a satisfying accuracy, this research proposed a novel framework that integrates multi-agent system and resistance-capacitance modelling techniques. The proposed methods represent the thermal network by means of circuit analogy and perform indoor thermal control based on stochastic occupant behaviours and appliance usage. The proposed BTM modelling framework is of great potential in flexibility estimation of BTM and could be used to assist the demand-side management.