Modular Modeling Approach Research Articles

Energy efficiency research of propulsion system for series-parallel hybridization of amphibious vehicles

The series-parallel hybrid system has attracted much attention from scholars for its effective integration of the power advantages and operating characteristics of different power sources, which is influenced by international emission regulations, energy-saving and emission reduction policies. As such, a series-parallel hybrid powertrain is introduced to the amphibious vehicle, and an innovative powertrain topology architecture is proposed. Meanwhile, the operation mode and energy efficiency characteristics are investigated during the working process. Firstly, the energy flow simulation model of a series-parallel gas-electric hybrid propulsion system is constructed using a modular modeling approach. Secondly, four operating modes, namely mechanical propulsion, electric propulsion, hybrid propulsion and charging mode, were formulated due to the fact that the propulsion system has multiple forms of power sources in the form of natural gas engine and reversible motor. Meanwhile, the energy flow states were investigated under different operating modes. Meanwhile, a comprehensive investigation of the energy efficiency associated with propulsion, storage and start-up energy was conducted for each specific mode. The results of the research indicated that the energy efficiency of the electric propulsion mode can reach up to 35.15 %, which is the gain from the wide operating range of the motor's high efficiency. The hybrid propulsion mode can obtain the highest energy efficiency of 35.88 %, which fully demonstrates the advantages of coordinating and complementing the two power sources, the natural gas engine and the reversible electric motor. This investigation also provides theoretical and empirical support for optimizing energy matching and formulating energy management strategies.

A new era of synthetic biology-microbial community design.

Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. Graphical Abstract.

Modular stochastic configuration network prediction interval for furnace temperature in municipal solid waste incineration

The high dimensionality of characteristic variables and the presence of numerous uncertain factors affecting furnace temperature during municipal solid waste incineration can lead to poor accuracy and generalization ability for furnace temperature prediction. This paper adopts the modular neural network modeling approach and incorporates Gaussian process regression analysis into stochastic configuration networks to propose a method for establishing a furnace temperature prediction interval model. First, a Gaussian mixture model is used to decompose the complex task into several subtasks. Then, considering the differences among the subtasks, Gaussian process regression with different kernel functions is combined with a stochastic configuration network to form corresponding base models, which are trained and learned. The prediction interval results are obtained through blending ensemble methods. Finally, the effectiveness of the proposed method is tested using historical data obtained from the municipal solid waste incineration process. The results indicate that the furnace temperature prediction model demonstrates advantages in terms of accuracy and generalization ability, making it applicable to the field of process parameter modeling.

Modelling and application of typical nonlinear torsional elements in vibro-impact analysis of pickup truck driveline system

The torsional element method for the dynamic modelling of torsional system was proposed by Crowther and Zhang, which provides a modular modeling approach with high modeling efficiency and has a good application prospect in vibro-impact analysis of vehicle driveline system. The main purpose of this paper is to further enrich the element types of the torsional element method and to establish a nonlinear dynamic model for a pickup truck driveline system by applying the torsional element method to analyze the vibro-impact phenomenon. A general nonlinear clearance element with different contact stiffness and damping, a nonlinear friction element with smooth Stribeck effect and a nonlinear multistage stiffness-damping element with different torque transfer characteristics are developed. A dynamic lumped parametric model with 10 degrees of freedom for a pickup truck driveline system is established by assembling the developed nonlinear torsional elements. The transient vibro-impact phenomenon caused by the clearance, the stick-slip phenomenon caused by the friction and the transient hysteresis cycle phenomenon caused by the nonlinear multistage damper during transient engagement of clutch are numerically investigated. Furthermore, a real-vehicle experimental study is conducted to confirm the transient vibro-impact phenomenon in pickup truck driveline system during transient clutch engagement and to verify the correctness of the dynamic lumped parameter model. On these bases, the influence of the key parameters of the nonlinear torsional element on the transient vibro-impacts are investigated, the effective engineering countermeasures to reduce the vibro-impact of the vehicle driveline system are put forward.

A modular ontology modeling approach to developing digital product passports to promote circular economy in the built environment

The significant impact of the built environment on resource consumption and waste production has led to calls for a shift towards a circular economy model that maximizes the efficient use of resources. This study explores the use of digital product passports (DPPs) to improve how we manage products throughout their lifecycle. However, dealing with the complexity and large volume of data in DPPs can be challenging in terms of effective information management and utilization. We address this issue by adopting a modular ontological approach to systematically capture product lifecycle information from its origin to its end-of-life phase. To ensure interoperability and reusability of the ontology, we annotate key concepts and relationships using International Organization for Standardization (ISO) standards that promote circular economy. Our research led to the development of several ontology modules derived from literature reviews and interviews conducted with industry and academia experts who specialize in sustainability. These modules were then integrated to create a digital product passport ontology. The study demonstrates the feasibility of using a modular ontology approach to manage the complex information inherent in DPPs paving the way for more sustainable management practices in the built environment sector.

Modelling and fault analysis of APROS-based moisture separation reheater

A modular modelling approach was adopted to build a dynamic simulation model of the moisture separation reheater on a high-precision APROS real-time platform to investigate the dynamic operating characteristics of the moisture separation reheater. The model was subjected to steady-state and step dynamic tests at different loads. The results show that the established model can correctly reflect the steam flow and heat transfer properties and can meet the requirements of the simulation. Using the one-dimensional distribution of the model, the effect of a small breakage failure in the heat exchanger bundle was further investigated. The results showed that the heat transfer tube bundle had leaks, reducing the heat transfer capacity and affecting the quality of the steam used to work in the low-pressure turbine. Simultaneously, the effect on steam outlet parameters increased with the degree of leakage in the heat exchanger.

Modular Dynamic Modeling for On-Orbit Assembly of Large-Scale Space Structures

The large-scale space structure during on-orbit assembly is a time-varying system. The dynamic modeling problem of such incrementally increasing space structure is investigated, and a modular dynamic modeling approach is proposed in this paper. The dynamic model of each substructure is first established, and then, a database is designed to store substructure models, which is used for subsequent dynamic modeling in the assembly process. The fixed connection relationship between adjacent substructures is described by constraint conditions, which lead to the coefficient matrices of adjacent substructures being decoupled. The substructures are assembled in a given sequence, and then, the dynamic modeling, to describe the large-scale space structure on-orbit assembly, is gradually completed via using the proposed modeling approach. The numerical simulation is finally presented. The results demonstrate that the extra calculation resulting from the coefficient matrices coupling of adjacent substructures is avoided. Moreover, the proposed dynamic model can accurately describe the dynamic characteristics of the large-scale space structure during on-orbit assembly.

Modular models for systems based on multi-tenant services: A multi-level petri-net-based approach

Recently, the emergence of multi-tenant services-based systems (SBS) in cloud has gained significant attention. These systems, commonly adopted by Cloud software vendors, offer the advantage of building software as a service (SaaS) in a multi-tenant environment with distinct variability plans. However, the task of managing the SBS is challenging due to the interdependence among its diverse components. The difficulty primarily stems from the specific performance requirements of different variability plans and the interdependence between these plans. The main concerns involve effectively representing the components and levels of this system, and establishing synchronization rules to ensure its seamless operation. Refining these rules is crucial for improving coordination among the various components and levels of the SBS. However, it becomes particularly challenging when there are correlation issue among services that fall under different plan characteristics. In this context, it is vital to guarantee performance differentiation for each tenant by adhering to predefined quality of service (QoS) specified in Service Level Agreements (SLAs).To address these challenges, we propose a modular modeling approach based on multi-level Petri-Nets (n-NLS). By adopting these approach, the ambiguity of SBS system can be significantly reduced. An example of a SBS application illustrates our modular modeling approach using n-NLS.

A spatiotemporally explicit modeling approach for more realistic exposure and risk assessment of off-field soil organisms.

Natural and seminatural habitats of soil living organisms in cultivated landscapes can be subject to unintended exposure by active substances of plant protection products (PPPs) used in adjacent fields. Spray-drift deposition and runoff are considered major exposure routes into such off-field areas. In this work, we develop a model (xOffFieldSoil) and associated scenarios to estimate exposure of off-field soil habitats. The modular model approach consists of components, each addressing a specific aspect of exposure processes, for example, PPP use, drift deposition, runoff generation and filtering, estimation of soil concentrations. The approach is spatiotemporally explicit and operates at scales ranging from local edge-of-field to large landscapes. The outcome can be aggregated and presented to the risk assessor in a way that addresses the dimensions and scales defined in specific protection goals (SPGs). The approach can be used to assess the effect of mitigation options, for example, field margins, in-field buffers, or drift-reducing technology. The presented provisional scenarios start with a schematic edge-of-field situation and extend to real-world landscapes of up to 5 km × 5 km. A case study was conducted for two active substances of different environmental fate characteristics. Results are presented as a collection of percentiles over time and space, as contour plots, and as maps. The results show that exposure patterns of off-field soil organisms are of a complex nature due to spatial and temporal variabilities combined with landscape structure and event-based processes. Our concepts and analysis demonstrate that more realistic exposure data can be meaningfully consolidated to serve in standard-tier risk assessments. The real-world landscape-scale scenarios indicate risk hot-spots that support the identification of efficient risk mitigation. As a next step, the spatiotemporally explicit exposure data can be directly coupled to ecological effect models (e.g., for earthworms or collembola) to conduct risk assessments at biological entity levels as required by SPGs. Integr Environ Assess Manag 2024;20:263-278. © 2023 Applied Analysis Solutions LLC and WSC Scientific GmbH and Bayer AG and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A Modular Circuit Synthesis Oriented Modelling Approach for Non-Isolated DC-DC Converters in CCM

The continued commissioning of DC microgrids in an effort to achieve net-zero carbon levels in the atmosphere demands the large-scale deployment of converters to make the power from renewable energy sources, such as solar PV, usable. To control these inherently non-linear converters using classical linear control methods, averaged modelling techniques are employed. These methods are laborious and easily become intractable when applied to converters with increased energy storage elements. A modular modelling approach is proposed. This approach is based on the synthesis of converters using refined basic building blocks. The refined basic building blocks are independently modelled as two-port networks and used in a circuit synthesis-oriented manner to derive power stage models of commonly used DC-DC converters. It is found that most of the converters considered in the study can be described as a cascade combination of these basic building blocks. As such, transmission parameters are mainly used to model the two-port networks. Moreover, it is also found that using this modelling technique enables the computation of generalized expressions for all power stage models of interest. The use of two-port networks curtails the size of the matrices describing the basic building blocks to 2 × 2, and thus simplifies the entire modelling procedure. Additionally, two-port network analysis makes this modelling technique modular, thus making it more suited to be employed in DC microgrids. The independence of the two-port models on the circuit topology and functionality makes it possible to even model new converters containing the described basic building blocks solely based on circuit connection.

Modular extensible transformable modeling approach for CNC equipment

With the rapid development of computer technology and the urgent need for the frequent modification of CNC equipment, one of the current research hotspots is the encapsulation of existing design results into modules and modeling the modules to make them extendable. On this basis, the rapid development and configuration of CNC equipment can be realized. Therefore, this paper proposes an extensible transformable model based on object-oriented product modules. Extensible transformable methods, instantiated methods, and extensible sets of modules are also studied. A general module design process is devised. This model simplifies the product configuration process, improves the utilization of design knowledge, and solves the compatibility problem of contradictory configurations.

A novel modular modeling approach for understanding different electromechanics between left and right heart in rat.

While ion channels and transporters involved in excitation-contraction coupling have been linked and constructed as comprehensive computational models, validation of whether each individual component of a model can be reused has not been previously attempted. Here we address this issue while using a novel modular modeling approach to investigate the underlying mechanism for the differences between left ventricle (LV) and right ventricle (RV). Our model was developed from modules constructed using the module assembly principles of the CellML model markup language. The components of three existing separate models of cardiac function were disassembled as to create smaller modules, validated individually, and then the component parts were combined into a new integrative model of a rat ventricular myocyte. The model was implemented in OpenCOR using the CellML standard in order to ensure reproducibility. Simulated action potential (AP), Ca2+ transient, and tension were in close agreement with our experimental measurements: LV AP showed a prolonged duration and a more prominent plateau compared with RV AP; Ca2+ transient showed prolonged duration and slow decay in LV compared to RV; the peak value and relaxation of tension were larger and slower, respectively, in LV compared to RV. Our novel approach of module-based mathematical modeling has established that the ionic mechanisms underlying the APs and Ca2+ handling play a role in the variation in force production between ventricles. This simulation process also provides a useful way to reuse and elaborate upon existing models in order to develop a new model.

A constraint embedding approach for dynamics modeling of parallel kinematic manipulators with hybrid limbs

Parallel kinematic manipulators (PKM) are characterized by closed kinematic loops, due to the parallel arrangement of limbs but also due to the existence of kinematic loops within the limbs. Moreover, many PKM are built with limbs constructed by serially combining kinematic loops. Such limbs are called hybrid, which form a particular class of complex limbs. Design and model-based control requires accurate dynamic PKM models desirably without model simplifications. Dynamics modeling then necessitates kinematic relations of all members of the PKM, in contrast to the standard kinematics modeling of PKM, where only the forward and inverse kinematics solution for the manipulator (relating input and output motions) are computed. This becomes more involved for PKM with hybrid limbs. In this paper a modular modeling approach is employed, where limbs are treated separately, and the individual dynamic equations of motions (EOM) are subsequently assembled to the overall model. Key to the kinematic modeling is the constraint resolution for the individual loops within the limbs. This local constraint resolution is a special case of the general constraint embedding technique. The proposed method finally allows for a systematic modeling of general PKM. The method is demonstrated for the IRSBot-2, where each limb comprises two independent loops.

An Approach to Modeling Percussive Drilling Systems

AbstractA framework for modeling the transient response of percussive drilling systems is presented. The proposed approach is based on the distributed transfer function method (DTFM), which is a semi-analytical modeling technique. Experimental results obtained from a percussion testbed for the Regolith and ice drill for the exploration of new terrains (TRIDENT) were incorporated into this modeling technique. DTFM is shown to be a convenient, modular modeling approach, capable of handling complex boundary conditions and drill rod geometries. Moreover, this technique is computationally simple and allows for straightforward incorporation of experimentally measured boundary forcing via numerical convolution, as well as control of the frequency content in the transient response. An experimental study is used to demonstrate the ability of the proposed approach to characterize unknown boundary conditions.

Open‐Source vapor compression library (VCLib): Heat pump modeling for education and research

AbstractVapor compression cycles have gained high importance due to increasing concerns regarding the decarbonization of heating and cooling applications. Typically, vapor compression cycles operate under dynamic conditions, making it difficult to predict their performance accurately. Dynamic simulation models present a decent solution for increasing the reliability of the performance assessment. However, the implementation of dynamic simulation models is a complex task, requiring a uniform description and model development. Therefore, this article introduces an open‐source Vapor Compression Library called VCLib based on a literature review of existing libraries for modeling and simulation of vapor compression cycles. The VCLib combines simulation models from the refrigerant through the component to the system level. The simulation models in the VCLib strictly follow a modular and scalable modeling approach. Class diagrams vividly describe the structure of models, making the VCLib easy to use for new users, especially students. The VCLib is open‐source and accessible to support educational and research purposes: https://github.com/RWTH-EBC/X-HD/

A mathematical model for vibration analysis of a parallel hybrid electric bus powertrain

This paper presents a parallel hybrid electric bus equipped with an automated manual transmission (AMT) and a mathematical model of the hybrid powertrain is developed for vibration analysis. The powertrain dynamic model is established by a modular modelling approach. A detailed AMT dynamic model, considering gear time-varying meshing stiffness, shaft elastic deformation, and bearing elastic support, is incorporated into the powertrain dynamic model. The damping and gyroscopic effect of gear-rotor in the AMT are considered as well. The AMT dynamic model is validated by experiment data from the time and frequency domain comparisons. Finally, the parameter analysis of the dual-mass flywheel (DMF) is utilised to illustrate how to use the proposed powertrain dynamic model for vibration reduction. The influence of the DMF parameters on vibration responses of the system with varying engine rotation speed is investigated. This study provides a basis for further vibration control of the hybrid powertrain during the engine driving mode.

Nonlinear proper orthogonal decomposition for convection-dominated flows

Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical systems when integrated with a time series predictive model. In this Letter, we put forth a nonlinear proper orthogonal decomposition (POD) framework, which is an end-to-end Galerkin-free model combining autoencoders with long short-term memory networks for dynamics. By eliminating the projection error due to the truncation of Galerkin models, a key enabler of the proposed nonintrusive approach is the kinematic construction of a nonlinear mapping between the full-rank expansion of the POD coefficients and the latent space where the dynamics evolve. We test our framework for model reduction of a convection-dominated system, which is generally challenging for reduced order models. Our approach not only improves the accuracy, but also significantly reduces the computational cost of training and testing.

Experimental characterization and modular modeling of hystereses for smart material actuators

In this article, we present a novel modular modeling approach to describe the hystereses for piezoelectric, magnetostrictive and shape memory alloy (SMA) actuators. For the above actuators, the output vs. input loops exhibit varying hystereses under the input signals of the different frequencies and amplitudes. To this end, the experimental characterization is conducted and hysteresis modeling approach is studied. Two characteristic indexes, i.e. loop relative width, loop asymmetry coefficients, are quantitatively analyzed according to the open-loop experiments for the three actuators. Based on the hysteresis phenomenon analyses, different submodels are selected to describe those phenomena. The Prandtl-Ishlinskii submodel is applied for symmetry rate-independent hysteresis identification; the arctangent-polynomial modified Prandtl-Ishlinskii submodel is proposed for asymmetry rate-independent hysteresis identification; infinite impulse response submodel is used for rate-dependent hysteresis identification. Those submodels are selected to construct a cascaded overall model to describe the hysteresis of piezoelectric, magnetostrictive, and SMA actuators. The hysteresis experimental identification results show that, with the proposed phenomenon-based hysteresis modular modeling approach, better performance can be obtained in terms of modeling accuracy and computation time than some other approaches.

Modular Vector Control of Multi-Three-Phase Permanent Magnet Synchronous Motors

Recent developments in power electronics are making the multiphase machines a competitive alternative to the conventional three-phase counterparts. Due to their fault-tolerant features, multiphase drives represent a robust technology in high-power/high-current, safety-critical applications. Besides, their introduction to transportation electrification is gaining importance. Among the multiphase solutions, the multi-three-phase machines are receiving a lot of attention by the industry since they use the well-consolidated three-phase technology, thus reducing the design time and also the cost. Therefore, this article proposes a modular vector control scheme for multi-three-phase permanent magnet synchronous motors. The proposed solution uses a modular modeling approach for the independent and decoupled torque control of each three-phase unit, allowing the implementation of torque-sharing strategies among the three-phase sets of the machine. The developed modular control has been validated on a nine-phase permanent magnet machine.

A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle.

Skeletal muscle is the principal contributor to exercise-induced changes in human metabolism. Strikingly, although it has been demonstrated that a lot of metabolites accumulating in blood and human skeletal muscle during an exercise activate different signaling pathways and induce the expression of many genes in working muscle fibres, the systematic understanding of signaling–metabolic pathway interrelations with downstream genetic regulation in the skeletal muscle is still elusive. Herein, a physiologically based computational model of skeletal muscle comprising energy metabolism, Ca2+, and AMPK (AMP-dependent protein kinase) signaling pathways and the expression regulation of genes with early and delayed responses was developed based on a modular modeling approach and included 171 differential equations and more than 640 parameters. The integrated modular model validated on diverse including original experimental data and different exercise modes provides a comprehensive in silico platform in order to decipher and track cause–effect relationships between metabolic, signaling, and gene expression levels in skeletal muscle.