Modular Simulation Model Research Articles

Dynamic joint decision of matching parameters and relocation strategies in ride-sourcing systems interacting with traffic congestion

As the ride-sourcing market expands, ride-sourcing fleets have increased urban traffic congestion, and in turn, road congestion is affecting ride-sourcing operations. It is crucial to incorporate this interaction mechanism into the operational decision of ride-sourcing systems. This mechanism can be described in detail by simulation models. Applying simulation-based optimization methods to the real-time operation of ride-sourcing systems is challenging due to the limitations of simulation efficiency. To tackle this issue, this study first proposes a modular simulation model combining time-driven and event-driven mechanisms. The simulation model comprises two layers: the traffic flow module at the bottom, defined by trip-based and multi-region macroscopic fundamental diagrams and cell transmission models, and the ride-sourcing operation module at the top, which considers the interaction between the traffic congestion and ride-sourcing operation and models the random behavior of passengers and drivers. We verify the efficiency and accuracy of the simulation model. The simulation model is utilized as an evaluator of the objective functions and performance measures. Then, we integrate the Bayesian optimization, parallel sampling, and rolling horizon approaches to develop an efficient and effective simulation-based optimization framework for the dynamic joint decision-making of matching radius, matching intervals, and threshold relocation strategies. Six groups of experiments for the traffic scenario in Berlin reveal some interesting findings. (1) The matching rate first increases and then decreases with the matching radius (a concave function), while the closest study does not think so. (2) As the fleet size grows, passenger waiting time reaches its minimum and then rises because of the increased endogenous congestion generated by ride-sourcing fleets. (3) The optimal operation strategy considering endogenous congestion not only leads to an increase in the system revenue and efficiency but also leads to congestion relief, even though the latter was not our initial objective. This benefit is the most pronounced when the fleet size and background traffic are moderate.

On the Assessment of the Chilean Solar Thermal Regulation Using a Modular Simulation Model Coupled to a Multiobjective Optimization Algorithm

In Chile, the solar thermal regulation DS331, which utilizes a global modeling approach, governs the deployment of solar thermal systems (STSs) across highly variable climatic zones. This regulation’s one‐size‐fits‐all approach often misrepresents the solar potential and economic feasibility in different regions. To address these limitations, we introduce a refined modular energy model that incorporates a 1D multinode stratification technique for hot water storage. This model is associated with a multiobjective optimization process using the NSGA‐II algorithm, focusing on optimizing the solar collector area and storage volume to maximize solar fraction and life cycle savings (LCSs) across 20 major Chilean cities. Our results demonstrated that the optimized systems achieve solar fractions ranging from 0.92 to 1.00, significantly improving upon the current regulation’s performance, particularly in southern regions where solar radiation is lower. Notably, the optimized configurations suggested a potential reduction in collector areas by up to 20% and storage volumes by up to 15% compared to those recommended by DS331, while still exceeding the legal requirements for the solar fraction. This optimization made it possible to increase LCS by ~25%–30% across various scenarios, indicating a substantial improvement in cost‐effectiveness. Based on these findings, existing solar thermal regulations should be revised to take into account local climatic and consumption data. Such adjustments would ensure more accurate sizing of STS, enhanced economic viability, and greater incentive alignment for widespread adoption. This study underlines the critical role of detailed, location‐specific energy modeling in shaping effective energy policies and advancing the deployment of renewable technologies in diverse environmental contexts.

Ecology-habitat-flow modular simulation model for the recommendation of river ecological flow combination

Ecology-habitat-flow modular simulation model for the recommendation of river ecological flow combination

Aquatic Risks at the Landscape Scale: A Case Study for Pyrethroid Use in Pome Fruit Orchards in Belgium.

Procedures for environmental risk assessment for pesticides are under continuous development and subject to debate, especially at higher tier levels. Spatiotemporal dynamics of both pesticide exposure and effects at the landscape scale are largely ignored, which is a major flaw of the current risk assessment system. Furthermore, concrete guidance on risk assessment at landscape scales in the regulatory context is lacking. In this regard, we present an integrated modular simulation model system that includes spatiotemporally explicit simulation of pesticide application, fate, and effects on aquatic organisms. As a case study, the landscape model was applied to the Rummen, a river catchment in Belgium with a high density of pome fruit orchards. The application of a pyrethroid to pome fruit and the corresponding drift deposition on surface water and fate dynamics were simulated. Risk to aquatic organisms was quantified using a toxicokinetic/toxicodynamic model for individual survival at different levels of spatial aggregation, ranging from the catchment scale to individual stream segments. Although the derivation of landscape-scale risk assessment end points from model outputs is straightforward, a dialogue within the community, building on concrete examples as provided by this case study, is urgently needed in order to decide on the appropriate end points and on the definition of representative landscape scenarios for use in risk assessment.

Proposal of a module-driven architecture for building simulation models devoted to container terminals: dilemmas in applying generic, flexible, and modular principles

Container terminals are complex systems where containerized cargo undergoes a set of processing and handling operations to be delivered to their outgoing modes. A pool of decision support methods and simulation models has been developed to assist planners and managers in making decisions about daily operations. Nevertheless, most are designed for a particular terminal and not generic types. Indeed, a generic model serves as a conceptual factory to create specific models which greatly reduces the time and efforts of development; however, building such a model is no mean feat. To this aim, the paper on hand discusses the complexity of applying genericity, flexibility, and modularity in system modeling and proposes a generic architecture to build modular and flexible simulation models for container terminals. This architecture is split into a set of smaller, manageable, well-connected, and generic modules that facilitate the creation of highly parametrized specific models. An illustrative example of the architecture usage is presented in a case study, the new container terminal of Stockholm, and the resulting models were validated by subject matter experts. Finally, to prove its efficiency, a numerical study fed with real data is conducted to investigate the handling capacity of the studied system under different handling and flow scenarios. The obtained results show that the terminal handling capacity can be increased by around 50% if three to four more straddle carriers are added to the existing fleet.

Evaluation of Transmission Losses of Various Battery Electric Vehicles

Transmission losses in battery electric vehicles have compared to internal combustion engine powertrains a larger share in the total energy consumption and play therefore a major role. Furthermore, the power flows not only during propulsion through the transmissions, but also during recuperation, whereby efficiency improvements have a double effect. The investigation of transmission losses of electric vehicles thus plays a major role. In this paper, three simulation models of the Institute of Automotive Engineering (the lossmap-based simulation model, the modular simulation model, and the 3D simulation model) are presented. The lossmap-based simulation model calculates transmission losses for electric and hybrid transmissions, where three spur gear transmission concepts for battery electric vehicles are investigated. The transmission concepts include a single-speed transmission as a reference and two two-speed transmissions. Then, the transmission lossmaps are integrated into the modular simulation model (backward simulation) and in the 3D simulation model (forward simulation), which improves the simulation results. The modular simulation model calculates the optimal operation of the transmission concepts and the 3D simulation model represents the more realistic behavior of the transmission concepts. The different transmission concepts are investigated in Worldwide Harmonized Light Vehicle Test Cycle and evaluated in terms of transmission losses as well as the total energy demand. The map-based simulation model allows the transmission losses to be broken down into the individual component losses, thus allowing transmission concepts to be examined and evaluated in terms of their efficiency in the early development stage to develop optimum powertrains for electric axle drives. By considering transmission losses in detail with a high degree of accuracy, less efficient concepts can be eliminated at an early development stage. As a result, only relevant concepts are built as prototypes, which reduces development costs.

IFogSim2: An extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments

Internet of Things (IoT) has already proven to be the building block for next-generation Cyber–Physical Systems (CPSs). The considerable amount of data generated by the IoT devices needs latency-sensitive processing, which is not feasible by deploying the respective applications in remote Cloud datacentres. Edge/Fog computing, a promising extension of Cloud at the IoT-proximate network, can meet such requirements for smart CPSs. However, the structural and operational differences of Edge/Fog infrastructure resist employing Cloud-based service regulations directly to these environments. As a result, many research works have been recently conducted, focusing on efficient application and resource management in Edge/Fog computing environments. Scalable Edge/Fog infrastructure is a must to validate these policies, which is also challenging to accommodate in the real-world due to high cost and implementation time. Considering simulation as a key to this constraint, various software have been developed that can imitate the physical behavior of Edge/Fog computing environments. Nevertheless, the existing simulators often fail to support advanced service management features because of their monolithic architecture, lack of actual dataset, and limited scope for a periodic update. To overcome these issues, we have developed modular simulation models for service migration, dynamic distributed cluster formation, and microservice orchestration for Edge/Fog computing based on real datasets and extended the basic components of iFogSim, a widely used Edge/Fog computing simulator for their ease of adoption as iFogSim2. The performance of iFogSim2 and its built-in service management policies are evaluated using three use case scenarios and compared with the contemporary simulators and benchmark policies under different settings. Results indicate that our simulator consumes less memory and minimizes simulation time by an average of 28% when compared to other simulators.

Simulation model for energy consumption and acoustic underwater communication of autonomous underwater vehicles

Recently, cooperative autonomous underwater vehicles (AUVs) have been deployed in application areas such as surveillance and protection of maritime infrastructures for inspection and monitoring purposes. These cooperative methodologies require wireless transmission of data between the different AUVs operating in the underwater environment. Communication over ranges exceeding 100 m exclusively relies on underwater acoustic communication. However, the propagating acoustic waves suffer from several challenges due to the presence of path loss, multi-path propagation, the slow and variant propagation speed, background noise, and Doppler distortion. Since the power supply of the AUVs is limited, communication must be very energy efficient and energy constraints have to be known to be able to plan the mission of AUVs. Due to the difficulties of real experiments, the modeling and simulation of the energy consumption and underwater acoustic communication play an essential role in studying and developing these systems. We provide a modular simulation model for the energy consumption and acoustic underwater communication of AUVs implemented in the network simulator OMNeT++ using the INET framework. More specifically, we extend several INET modules in such a way as to reflect the characteristics of AUVs and underwater communication. We study and analyze the AUVs’ energy consumption and dependence of the message quality on different properties such as those mentioned above.

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus.

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

Utilizing In-Hospital Fabrication to Decrease Simulation Costs

Two restrictive factors for surgical training through simulation, are the cost of and accessibility to materials and consoles for simulation models. Commercial surgical simulation models continue to maintain high prices with a wide range of fidelity levels. We believe that by utilizing in-house fabrication, these barriers can be decreased while maintaining and even improving the functionality of surgical simulation models as well as increase their individualization and customization. By using a combination of digital and manual fabrication techniques such as 3D printing and basic mold making methods, we were able to create models equivalent to current commercial products by utilizing the first of its kind MakerHEALTH space and collaborating with our surgical simulation staff. We then compared our research and development, start-up, materials, operational, and labor costs to buying comparable commercial models with the simulation usage rates of our institution. We were able to decrease the costs of a 6 model simulation sample set (appendectomy, cholecystectomy, common bile duct exploration, ventral hernia, chest tube insertion, and suture pads) at our institution from $99,646.60 to $13,817.21 for a medical student laborer, $14,500.56 for a surgical resident laborer, $15,321.08 for a simulation staff laborer, and $18,984.48 for an attending physician laborer. We describe successful approaches for the creation of cost-effective and modular simulation models with the aim of decreasing the barriers to entry and improving surgical training and skills. These techniques make it financially feasible for learners to train during larger faculty-led workshops and on an individual basis, allowing for access to simulation at any time or place.

Harnessing the Power of Data

Research capacity is the basic currency of a sentencing commission, and the gold standard is data, with its capability of being transformed into meaningful information to support policy development and guide sentencing decisions. This paper investigates the science of best practices in Pennsylvania and the roles of the Pennsylvania Commission on Sentencing in collecting, preparing, and disseminating sentencing and resentencing data; in collaborating with other agencies to integrate justice-related data; and in establishing partnerships to expand the research capacity of the Commission to use these data. The application of evidence-based practices—of harnessing the power of data to develop policy and support decision making—will be demonstrated through a discussion of four Commission projects: construction and validation of a sentence risk assessment instrument; comprehensive review of the use of criminal history in the Pennsylvania Sentencing Guidelines; establishment of state parole guidelines; and design of a modular population projection simulation model.

Study of Series-Parallel and Power-Split DHT for Hybrid Powertrains

Dedicated hybrid transmission (DHT) is the developing trend of hybrid transmissions. This paper studies different types of DHTs regarding the structure, function, and performance. Firstly, the structure and function of different well-known DHTs in the market are discussed and analyzed. Through the analysis, the reasonability and necessity of the different modes and the electric motor power requirements that lie behind different DHTs are derived. Secondly, the dynamics of different DHTs are further compared with the driving areas in different modes under different driving conditions. Then, the basic or minimum dynamic requirements are defined and taken into consideration before the economic comparison. To ensure the effectiveness of the comparison, the optimization of the configurations for each kind of DHT is carried out with the modular simulation model. The economic comparison is conducted under the premise of excluding the influences of the configurations in the results. Finally, the sensitivity and influence of different engine characteristics for different DHTs are studied to find out the sensibilities of the two kinds of DHTs with engine technology. Through these studies, the essential differences and features of different DHTs can be derived to help better understand the decision in choosing the technical route for the original equipment manufacturers.

Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics

A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.

Virtual Capacitor Concept for Computationally Efficient and Flexible Real-Time MMC Model

This paper demonstrates the splitting of the modular multilevel converter real-time simulation model into several independent parts through the use of the virtual capacitor concept. As a result, the number of state-space matrices the real-time solver needs to take into account gets significantly reduced, offering the possibility for substantial reduction of the simulation step size. Consequently, real-time simulation quality increases. The proposed concept was verified on a large-scale hardware-in-the-loop system comprising seven RT Boxes, where the model of the physical system is deployed, and ABB PEC800 industrial controller, where control algorithms of the real power hardware are deployed and executed.

High fidelity simulation of ultrafine PM filtration by multiscale fibrous media characterized by a combination of X-ray CT and FIB-SEM

Air filtration mechanisms in the composite filter media used in practical applications are important and challenging to understand because the component fibers could have various size scales and morphologies. In this work, a three-dimensional digital model of nanofiber-based filter media was reconstructed for the first time based on the X-ray tomography data for the cellulose substrate and the Focused Ion Beam-Scanning Electron Microscope (FIB-SEM) image analysis for the several micrometers thick (3.82–7.90 μm) electrospun polyvinylidene fluoride (PVDF) nanofiber membrane. Besides the high-resolution model where the details of the fibrous structures were fully resolved, another low-resolution model with approximated unresolved structures was also established. Filtration simulations utilizing these models were conducted considering the drag force, Brownian diffusion and aerodynamic slip. The simulated filtration efficiencies agreed well with the experiments for particles of 70–400 nm, including the most penetrating particle size (MPPS, 100–200 nm). Moreover, the structure-resolved models had higher accuracy but higher computational costs, while the unresolved simulations saved much running time but over-predicted the filtration efficiency, especially for smaller particles (<100 nm). Our study presents a comprehensive strategy for investigating the composite filter media with multiscale complex structures using a combination of advanced characterization technologies and modular simulation models.

Marine powertrain simulation for design and operational performance evaluation

This research investigates the performance of a propulsion system and the emissions parameters of a merchant ship by using the consequences derived via a modular ship simulation model that is executed in MATLAB/Simulink. In the model, numerous components of the drive system such as engine, shafting framework, propeller, and vessel are shown by separated blocks whereas proper interfaces and connections are utilised to swap the desired factors between the blocks. Therefore, the created model can be used to explore the steady state and transient behaviour of the ship propulsion system and investigate the engine/propeller/shafting framework and vessel connections. Using this model the performance parameters of a ship in normal speed and slow steaming were calculated and investigated. In addition, the ship's powertrain system was investigated using distributed-lumped parameter model. The shear stress analysis of the propeller shaft was performed using a distributed-lumped model and compared in two normal speed and slow steaming modes. Due to the model's modular structure, its components can be modified and extended to be used in future new technologies and fuel evaluation as a part of the ship designing process.

Manoeuvring simulations in adverse weather conditions with the effects of propeller and rudder emergence taken into account

Manoeuvring simulations in realistic sea conditions, i.e. in wind and waves, have recently got the attention due to several reasons such as stringent EEDI regulations and associated minimum power guidelines, optimization of ship design with respect to the real operating conditions, assessment of safety performance in extreme weather conditions etc. In this paper, a modular simulation model for manoeuvring in adverse weather conditions is proposed. The effects of propeller and rudder emergences on ship behaviour and propulsion engine's dynamics are also taken into account by integrating the first order 6DOF ship motions into the manoeuvring model. The engine's load limits are also explicitly considered using the fuel limiter inside the governor. Various manoeuvring simulations are investigated by using the different initial conditions, propulsion engines and environmental conditions. The difference in the initial forward speeds of time-domain simulations is found to have the significant effects on ship trajectories although the final equilibrium ship speeds are the same. The choice of engine's SMCR with higher light running margin (LRM) is found to have the substantial advantageous over ship speed and safety in adverse weather conditions but with some compromises in calm water ship speed, calm water SFOC and EEDI values.

Modular simulation model for falling film evaporators with novel approach to manage dominant time-varying transport delays

A modular simulation model of the evaporator is developed by dividing it into design-independent subsystems. By introducing this modularity, numerous different evaporator designs, system parameters, operating points or control concepts can be configured and simulated. The plant operation or design can therefore be easily tested and costs for staff and experiments can be particularly reduced.A large challenge to create valid simulation tools consists of modeling the product transport within connecting pipes and during the evaporation process within the tubes, which in a dynamic process both lead to time-varying delays. As previously published models only include constant transport delays, they are disadvantageous to simulate real plant operation. In order to improve the simulation validity, a detailed dynamic full plant model is developed, which is able to illustrate dominant time-varying transport delays. A comparison with measured data of an existing falling film evaporator shows the validity of the proposed model.

Optimum Efficiency of PV Panel Using Genetic Algorithms to Touch Proximate Zero Energy House (NZEH)

By optimizing the efficiency of a modular simulation model of the PV module structure by genetic algorithm, under several weather conditions, as a portion of recognizing the ideal plan of a Near Zero Energy Household (NZEH), an ideal life cycle cost can be performed. The optimum design from combinations of NZEH-variable designs, are construction positioning, window-to-wall proportion, and glazing categories, which will help maximize the energy created by photovoltaic panels. Comprehensive simulation technique and modeling are utilized in the solar module I-V and for P-V output power. Both of them are constructed on the famous five-parameter model. In addition, the efficiency of the PV panel is established by the genetic algorithm under the standard test conditions (STC) and a comparison between the theoretical and experimental results is done to achieve maximum performance ranging from 0.15 to 0.16, particularly with an error of about - 0.333 for an experimental power of 30 Watts compared with the theoretical power of 30.1 Watts. The results obtained by the genetic algorithm give the best value for efficiency at the range of 16% to 17% of solar radiation, from 500–600 W/m2. These values are almost identical to the efficiency obtained from the results of the operation, where the best value for efficiency in the experimental results was seen to be 15.7%.

Evaluation of transmission losses of various Dedicated Hybrid Transmission (DHT) with a lossmap-based simulation model

This paper presents a simulation methodology developed at the Institute of Automotive Engineering (IAE). With this simulation method, transmission lossmaps can be calculated for Dedicated Hybrid Transmission (DHT). These transmission lossmaps are considered within the control strategy section of a modular simulation model, which can lead to more realistic simulation results. Four DHT concepts are investigated using this simulation methodology: three multi-mode DHT (MM-DHT) transmission concepts and a Power Split DHT (PS-DHT) concept. All concepts have two electric machines (EM). The first concept is a MM-DHT, which was developed with the transmission synthesis of the IAE. This DHT concept has two planetary gear sets (PGS), four shifting elements, and two EM. The other MM-DHT concepts are based on the Voltec 2 and the CT6. They also have two EM. The Voltec 2 has two PGS and three shifting elements. The CT6 has three PGS and five shifting elements. The power split DHT has one PGS and no shifting elements. For the evaluation of the transmission concepts, consistent and representative performance requirements are first defined, which must be fulfilled by all concepts. Afterwards, the transmission losses of the individual concepts can be subdivided via the lossmap-based simulation model for the different loss components. Finally, the DHT concepts are examined and evaluated with regard to mechanical efficiency and fuel consumption in the WLTC.