Modular Model Research Articles

Modular Model Reduction of Interconnected Systems: A Top-Down Approach

Complex systems often involve interconnected subsystem models developed by multi-disciplinary teams. To simulate and control such systems, a reduced-order model of the interconnected system is required. In the scope of this paper, we pursue this goal by subsystem reduction to warrant modularity of the reduction approach. However, reducing subsystem models affects not only the subsystems accuracy, but also the interconnected model accuracy, making it difficult to predict a priori the accuracy impact of subsystem reduction. To address this challenge, we introduce a top-down approach using mathematical tools from robust performance analysis, enabling the translation of accuracy requirements from the interconnected model to the subsystem level. This enables independent subsystem reduction while ensuring the desired accuracy of the interconnected model. We demonstrate the effectiveness of our approach through a structural dynamics case study.

An Analysis of the Status of Teacher Certification in the Australian Capital Territory Following the Introduction of the Modular Model

An Analysis of the Status of Teacher Certification in the Australian Capital Territory Following the Introduction of the Modular Model

Modular Supervisory Control for the Coordination of a Manufacturing Cell with Observable Faults

In the present paper, a manufacturing cell in the presence of faults, coming from the devices of the process, is considered. The modular modeling of the subsystems of the cell is accomplished using of appropriate finite deterministic automata. The desired functionality of the cell as well as appropriate safety specifications are formulated as eleven desired languages. The desired languages are expressed as regular expressions in analytic forms. The languages are realized in the form of appropriate general type supervisor forms. Using these forms, a modular supervisory design scheme is accomplished providing satisfactory performance in the presence of faults as well guaranteeing the safety requirements. The aim of the present supervisor control scheme is to achieve tolerance of basic characteristics of the process coordination to upper-level faults, despite the presence of low-level faults in the devices of the process. The complexity of the supervisor scheme is computed.

Pemodelan Desain Modular Hunian Sementara Berbasis Lokalitas Material di Desa Hargotirto, Kecamatan Kokap, Kabupaten Kulon Progo

Title: Modeling Temporary Shelter Modular Design Based on Material Locality in Hargotirto Village, Kokap District, Kulon Progo Regency Hargotirto village has an area that is prone to landslides. Several times the landslides caused the affected communities to be forced to flee to temporary shelters with several problems, such as the absence of private space for refugees and the lack of supporting facilities. The modular temporary shelter model developed through this research is expected to create advantages in terms of provision, time, and quality. The modeled temporary housing also considers local elements such as local building architecture, craftsman skills, and local materials obtained from local raw materials. Material locality data were collected through field observation surveys and guided interviews with residents and builders. The research method used is simulation and modeling research through modeling the form of temporary housing that considers the use of local materials such as wood and bamboo, the strength of modular construction, and variations in space requirements. To check the structural strength and construction details of each developed model using SAP2000 software. Several alternative models have been developed, taking into account the availability of local materials, the condition of the land area, and a series of modules in the form of single or row shelters that provide shared space for refugees.

An agent-based modelling framework for the simulation of large-scale consumer participation in electricity market ecosystems

The role of consumers as price-sensitive participants in electricity markets is considered essential to ensure efficient and secure operations of electricity systems. Yet the uncertain or unknown consequences of active market participation remain a large barrier for active consumer-side market participation. Simulations are a powerful tool to reduce this uncertainty by giving consumers an insight on the potential benefits and costs of market participation. However, the simulation setup must be adapted to each market context and each consumer market participation strategy. To simplify the simulation development process and improve the comparability of simulation results, this paper proposes a modular yet systematic electricity market modelling framework. The framework applies object-oriented programming concepts for business ecosystem modelling presented in previous works to develop an agent-based model of a consumer-centric electricity market ecosystem. The market ecosystem is represented by a multitude of interacting submarkets with their own logic. Within submarkets, context-independent and context-dependent elements are distinguished to provide model abstraction which can be adapted to different contexts. This framework is illustrated by applying it to three different submarkets in the Western Danish electricity market context: the Nordpool day-ahead market, the Nordpool intraday market, and the Frequency Containment Reserve market. The submarket role abstractions allow to benefit from the commonalities between the analysed submarkets during model implementation, while the role parametrisations allow to quickly adapt the roles to each market context. The implementation of the modelling framework in the Nordic context highlights the benefits of a modular approach in a liberalised and unbundled market context.

Analysis of a spoke‐type ferrite PM motor with assisted poles based on equivalent magnetic network with hybrid cross‐shaped mesh elements

AbstractThis article proposes an equivalent magnetic network (EMN) model to improve the accuracy of electromagnetic analysis, where a modular modelling of hybrid cross‐shaped mesh (CS‐M) elements is introduced. By applying hybrid CS‐M elements, the modelling of air gap and its surrounding regions is regulated into an identical width. As a result, the relative motion of the motor is simpler to simulate by changing the nodal connection without involving the non‐linear permeance. The airgap flux density is solved more accurately than the regular CS‐M elements as well. A 12‐slot/10‐pole ferrite permanent magnet (FPM) motor with assisted poles of unequal pole‐arc ratios is selected as an example to be built with the proposed EMN model. After solving the EMN model, the effectiveness and accuracy of the proposed EMN model are verified by comparing it with the FEA model and experiments. Additionally, based on the frozen permeability (FP) method, transient torque components can be separated accurately with a transient torque solution related to virtual work principle (VWP) for the EMN model.

MISSILES: An efficient resolution of the co-simulation coupling constraint on nearly linear differential systems through a global linear formulation

In a co-simulation context, interconnected systems of differential equations are solved separately but they regularly communicate data to one another during these resolutions. Iterative co-simulation methods have been developed in order to enhance both stability and accuracy. Such methods imply that the systems must integrate one or more times per co-simulation step (the interval between two consecutive communications) in order to find the best satisfying interface values for exchanged data (according to a given coupling constraint). This requires that every system involved in the modular model is capable of rollback: the ability to re-integrate a time interval that has already been integrated with different input commands. In a paper previously introduced by Eguillon et al. in 2022, the COSTARICA process is presented and consists in replacing the non-rollback-capable systems by an estimator on the non-last integrations of the iterative process. The MISSILES algorithm, introduced in this paper, consists in applying the COSTARICA process on every system of a modular model simulated with the IFOSMONDI-JFM iterative co-simulation method (introduced by Eguillon et al. in 2021). Indeed, in this case, the iterative part on the estimators of each system can be avoided as the global resolution on a co-simulation step can be written as a single global linear system to solve. Consequently, MISSILES is a non-iterative method that leads to the same solution than the IFOSMONDI-JFM iterative co-simulation method applied to systems using the COSTARICA process to emulate the rollback.

Mathematical modeling of antihypertensive therapy.

Hypertension is a multifactorial disease arising from complex pathophysiological pathways. Individual characteristics of patients result in different responses to various classes of antihypertensive medications. Therefore, evaluating the efficacy of therapy based on in silico predictions is an important task. This study is a continuation of research on the modular agent-based model of the cardiovascular and renal systems (presented in the previously published article). In the current work, we included in the model equations simulating the response to antihypertensive therapies with different mechanisms of action. For this, we used the pharmacodynamic effects of the angiotensin II receptor blocker losartan, the calcium channel blocker amlodipine, the angiotensin-converting enzyme inhibitor enalapril, the direct renin inhibitor aliskiren, the thiazide diuretic hydrochlorothiazide, and the β-blocker bisoprolol. We fitted therapy parameters based on known clinical trials for all considered medications, and then tested the model's ability to show reasonable dynamics (expected by clinical observations) after treatment with individual drugs and their dual combinations in a group of virtual patients with hypertension. The extended model paves the way for the next step in personalized medicine that is adapting the model parameters to a real patient and predicting his response to antihypertensive therapy. The model is implemented in the BioUML software and is available at https://gitlab.sirius-web.org/virtual-patient/antihypertensive-treatment-modeling.

The Research of Online and Offline Mixed Teaching Practice Based on POA + BOPPPS Teaching Mode

Taking Advanced Japanese course as an example, this paper carried out a research on the blended online and offline teaching reform of advanced Japanese based on POA philosophy and BOPPPS teaching model. Aiming at the current situation of Japanese teaching in colleges and universities, the teaching objectives of the course are formulated in combination with the training objectives of Japanese majors in colleges and universities. On this basis, combined with the characteristics of the course and the learning situation, an effective six-step teaching method based on POA teaching theory and BOPPPS is proposed. Combined with the network teaching platform, the blended online and offline teacher-student co-construction teaching mode is constructed in the whole process of before, during and after class. This paper discusses the key issues in the teaching design and implementation of the model, and gives specific suggestions for each step of the teaching process combined with specific teaching cases, in order to explore and practice how to form a more systematic and modular effective teaching model.

Expert-Based Modular Simulator for Municipal Waste Processing Technology Design

One of the significant problems in our society is the handling and processing of the vast amount of waste produced by households and industrial processes. Nowadays, packaging material regulations are constantly changing, which can significantly impact the quality of municipal waste, requiring the continuous development and redesign of waste processing plants. Since only a few uncertain measurements (composition, mass, etc.) are available for this task, analysing and redesigning waste processing technologies is challenging. This research aims to develop a modelling and simulation concept that can integrate all the available information and can also handle the uncertainty of the measurements. The proposed modular modelling framework can serve as a basis for designing and redesigning the technologies needed to process ever-changing municipal waste. The most important steps of the framework are as follows: identifying the typical equipment, these are the elements; building models of the elements; determining the characteristic parameters of the equipment; exploring the possible relationships between the elements. For example, the information needed to define the model parameters can be gathered from measurements, industrial experience, and expert knowledge. In many cases, the data obtained represent ranges. The stationary model framework applies efficiency factors and divides the solids into substreams based on expert knowledge. Furthermore, a modular simulator framework was developed to simulate the technological schemes with various connections. The specifications for all widely used waste industrial equipment (shredders, air separators, sieves, magnetic-, eddy current-, optical-, and ballistic separators) were used to construct the developed simulator. This simulator can open new opportunities for the design of waste sorting technological networks. The model was calibrated based on expertise gained from operating the studied technology. The changes in the material parameters can be considered, and the modular simulator can lead to flexible waste sorting technologies capable of adapting to governmental and environmental regulations changes. The main result of the work is that a methodology for designing a modular simulator, model development, and a validation method has been proposed, which provides the possibility to deal with uncertainty. All this is successfully presented through the analysis of an operating waste separation system.

Macadamia Nuts (Macadamia integrifolia) Value Chain and Technical Efficiency among the Small-scale Farmers in Zimbabwe

Agriculture value chains are an indispensable tool for stakeholder collaboration, improving market access, foreign currency generation and increase in macadamia nut production in most developing countries. In Zimbabwe macadamia nut is a relatively new crop that has the potential to alleviate poverty and enhance livelihoods. Zimbabwe’s macadamia nut industry is still in its infancy and remains an under-researched area. Despite this, the technical efficiency of macadamia nuts production systems as proxied by production levels among small-scale farmers in the country is least understood. This study analyses the existing value chains by scrutinizing every step required in the production and marketing of macadamia nuts focusing on the technical efficiency of value chain nodes. The macadamia nut value chain in Zimbabwe is predominantly an emergency value chain system that does not embrace the modular value chain model and associated networks to enhance technical efficiency and overall welfare. The value chain system is characterised by imperfect market conditions with limited knowledge of exact specifications, attributes and prices of macadamia nuts. The existing value chain models for macadamia nut need to be re-aligned to accommodate all stakeholders, hence widening resource and knowledge-sharing platforms. There is a need for the Zimbabwe macadamia nut value chain to shift towards captive networks which embrace and scale out practices such as out-grower contractual arrangements. There is potential to design macadamia nut value chain models in Zimbabwe to increase the technical efficiency of the industry.

Performance simulation analysis of new train control system safety communication protocol based on Matlab/SimEvents

The vehicle-ground safety communication of the New Train Control System adopts a brand-new underlying network and a communication mechanism of safe and reliable transmission. Based on the GPRS packet switching network, the data link layer adopts the PPP protocol, and the TCP protocol is adopted in the transmission layer to provide reliable transmission services. According to the GPRS network service quality index and the retransmission mechanism of the TCP protocol in the transport layer, this paper adopts the Matlab/SimEvents discrete event simulation method and uses the entity as the communication carrier to conduct a hierarchical and modular system-level modeling of New Train Control System vehicle-ground communication protocol. It simulates the communication process of the message from the application layer, the security layer to the transport layer, and finally transmitted on the GPRS network channel with random delay and packet loss probability. From the quantitative point of view, this paper analyzes the safety connection establishment time and success rate of the new train control system vehicle-ground wireless communication under different channel conditions, the timeliness of the non-periodic message receiving a reply to complete the specific scene function, and the reliability of the periodic message receiving delay. The analysis results show that the new train control system vehicle-ground wireless communication adopts GPRS network and TCP protocol retransmission mechanism to improve the real-time and reliability of communication.

Doing more with less: How to design a good subgroup governance model for the air pollution transport network in “2+26” cities of China?

Air pollution shares the attributes of significant spatial spillover effects and environmental public goods, leading to the territorial governance model that easily falls into a state of failure. Despite a growing number of studies on the local spatial spillover effect of air pollution, scant evidence currently exists on its global spatial association effect and a good subgroup governance model. Based on a panel data set of the daily prefecture-level city data on air quality measured by the air quality index (AQI) in “2 + 26” cities of China in 2015 and 2018, this study first builds an air pollution transport network (APTN), i.e., the cities as the nodes and the association relationships between the nodes as the edges. Furthermore, this paper reveals the spatial association effect and the temporal lagged attribute of the APTN using the Social network analysis (SNA) and the Generalized impulse response function (GIRF). The results are summarized as follows. (1) Every city has significant spatial association effects of air pollution with at least another city in the APTN, and northern APTN affects most to the air pollution of other cities, while southern APTN is obviously always affected by air pollution in other cities. (2) Transport strength peaks on the second day of an air pollution transport process, and the transport process lasts for 7–12 days. (3) The APTN is divided into four subgroups: Sycophants, Primary, Bidirectional, and Brokers, with Baoding, Zhengzhou, Heze, and Hengshui as the central cities of each group, respectively. Overall, our study provides a networked, modular, and early-warning governance model for policymakers.

Modeling and Simulation of Electric Thrust Reverser Actuator Control Based on Simulink

According to the structural characteristics and working principle of the electric actuator system of the aircraft thrust reverser, the mathematical control model of the thrust reverser actuator is proposed for a certain type of electric thrust reverser actuator system. The model is established and simulated by the modular modeling method based on Simulink software. The actuator’s load force, transmission, displacement, and velocity relationship are analyzed. The simulation results show that the model can simulate the deployment and stow of the actuator under different conditions and provide a platform for the control logic design, force analysis, and fault diagnosis of the electric thrust reverse actuator system.

A social media-based framework for quantifying temporal changes to wildlife viewing intensity

Documenting how human pressure on wildlife changes over time is important to minimise potential adverse effects through implementing appropriate management and policy actions; however, obtaining objective measures of these changes and their potential impacts is often logistically challenging, particularly in the natural environment. Here, we developed a modular stochastic model that infers the ratio of actual viewing pressure on wildlife in consecutive time periods (years) using social media, as this medium is widespread and easily accessible. Pressure was calculated from the number of times individual animals appeared in social media in pre-defined time windows, accounting for time-dependent variables that influence them (e.g. number of people with access to social media). Formulas for the confidence intervals of viewing pressure ratios were rigorously developed and validated, and corresponding uncertainty was quantified. We applied the developed framework to calculate changes to wildlife viewing pressure on loggerhead sea turtles (Caretta caretta) at Zakynthos island (Greece) before and during the COVID-19 pandemic (2019–2021) based on 2646 social media entries. Our model ensured temporal comparability across years of social media data grouped in time window sizes, by correcting for the interannual increase of social media use. Optimal sizes for these windows were delineated, reducing uncertainty while maintaining high time-scale resolution. The optimal time window was around 7-days during the peak tourist season when more data were available in all three years, and >15 days during the low season. In contrast, raw social media data exhibited clear bias when quantifying changes to viewing pressure, with unknown uncertainty. The framework developed here allows widely-available social media data to be used objectively when quantifying temporal changes to wildlife viewing pressure. Its modularity allowed viewing pressure to be quantified for all data combined, or subsets of data (different groups, situations or locations), and could be applied to any site supporting wildlife exposed to tourism.

Modified transfer matrix method for vibration analysis of beam structures including branches and rigid bodies

Beam frames can be found in many application scenarios, ranging from big-scale structure engineering of buildings, bridges to small systems of micromachines, sensors, robotics and metamaterials. However, several challenging issues, e.g. serial-parallel configurations, beam and rigid body coupling along with inconsistent dimension of state variables, often lead to a complicated dynamic modeling. In this paper, we show that the free vibration analysis of a class of beam structures with serial-parallel branches and rigid bodies can be significantly streamlined by modifying the transfer matrix method in a graphic manner. Treating general beam structures as a topological graph with standard building blocks enables a modular modeling process and hence offers a new way to divide the complex modeling issue into much easier steps. The transfer matrix of typical building blocks, such as a beam connected to rigid bodies, tree branches and parallel clamped sub-chains, are derived and standardized providing new perspectives and distinct forms in contrast to previous studies. The transfer matrix method exhibits several advantages of easy programming, accurate solution and small degrees-of-freedom for serial structures. The presented approach extends these characteristics to a range of multi-body systems consisting of rigid bodies and beams. Comparative validation with four application case studies confirms these advantages.

Exploration of the optimal modularity in assembly line design

It is widely accepted that a proper structural modularity degree of assembly processes in terms of mass customization has a positive effect on their efficiency because it, among other things, increases manufacturing flexibility and productivity. On the other hand, most practical approaches to identify such a degree is rather based on intuition or analytical reasoning than on scientific foundations. However, the first way can be used for simple assembly tasks, but in more complex assembly processes, this method lags behind the second. The purpose was to create a methodology for selection of optimal modular assembly model from among a predefined set of alternatives. The methodology is based on exploration of the relations between modularity measures and complexity issues as well as the relationship between structural modularity and symmetry. Especially, the linkage between modularity and complexity properties has been explored in order to show how modularization can affect distribution of the total structural complexity across the entire assembly line. To solve this selection problem, three different methods are preliminary suggested and compared via a series of numerical tests. The two of them present the novel contribution of this work, while the third method developed earlier for the purpose of finding and evaluating community structure in networks was adapted for a given application domain. Based on obtained results, one of these method is prioritized over another, since it offers more promising results and precision too.

Effects of steady wave forces on course-keeping manoeuvres of full- and model-scale ships moving obliquely in short waves

ABSTRACT We investigated the effects of the steady wave force variations generated by oblique motions upon ship manoeuvre estimations, using a numerical simulation that incorporated only steady wave forces into a three-degrees-of-freedom conventional modular mathematical model in calm water. The investigation was conducted for a very large crude carrier undergoing course-keeping manoeuvres in regular short waves. The steady wave forces in the simulation were provided via two methods, depending on the experimental data and the presence or absence of a ship drift angle. The simulation was validated via a free-running model test; this involved various rudder effectiveness conditions for the ship model and full-scale ships, both with and without engine limits. Through this validation, we showed that the steady wave force variations generated by oblique motions are non-negligible when accurately estimating the manoeuvres of full-scale ships in short waves, although they are negligible for ship models.

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise**

AbstractPhysico‐chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black‐box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics‐based features, our data‐driven approach uses orders of magnitude less simulations. For validation, we process full‐cell GITT measurements to characterize the diffusivities of both electrodes non‐destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases.

Rectifying Ill-Formed Interlingual Space: A Framework for Zero-Shot Translation on Modularized Multilingual NMT

The multilingual neural machine translation (NMT) model can handle translation between more than one language pair. From the perspective of industrial applications, the modularized multilingual NMT model (M2 model) that only shares modules between the same languages is a practical alternative to the model that shares one encoder and one decoder (1-1 model). Previous works have proven that the M2 model can benefit from multiway training without suffering from capacity bottlenecks and exhibits better performance than the 1-1 model. However, the M2 model trained on English-centric data is incapable of zero-shot translation due to the ill-formed interlingual space. In this study, we propose a framework to help the M2 model form an interlingual space for zero-shot translation. Using this framework, we devise an approach that combines multiway training with a denoising autoencoder task and incorporates a Transformer attention bridge module based on the attention mechanism. We experimentally show that the proposed method can form an improved interlingual space in two zero-shot experiments. Our findings further extend the use of the M2 model for multilingual translation in industrial applications.