Simulation Of System Performance Research Articles

Comprehensive modeling and simulation of photovoltaic system performance by using matlab/simulink: integrating dynamic meteorological parameters for enhanced accuracy

Comprehensive modeling and simulation of photovoltaic system performance by using matlab/simulink: integrating dynamic meteorological parameters for enhanced accuracy

Simulation of Hybrid Microbial Fuel Cell-Adsorption System Performance: Effect of Anode Size on Bio-Energy Generation and COD Consumption Rate

Simulation of Hybrid Microbial Fuel Cell-Adsorption System Performance: Effect of Anode Size on Bio-Energy Generation and COD Consumption Rate

Development of a dynamic traffic microsimulator for on-demand transit operations within an integrated modelling system

ABSTRACT This study develops an intelligent dynamic agent-based microsimulation (iDAMS) module for on-demand transit (ODT) operations within an integrated transport, land-use and energy (iTLE) model. A real-time optimization component within the iDAMS is formulated by the utilization of travelling salesman problem and simulated annealing metaheuristics that perform dynamic passenger-vehicle assignment. It simulates ODT operations for meeting the 24-hour auto-trip demand of Halifax, Canada, to compare the performance of the proposed system with personal cars (PCs). The optimization objectives are to determine the optimal fleet size and seat capacity that satisfies maximum trip requests while minimizing waiting time, travel time and vehicle kilometres travelled (VKT). Simulation results indicate that the ODT system can deliver service similar to PCs in Halifax while decreasing cost and emissions (13% reduction in VKT). The tools developed in this research will provide transit planners ability to conduct ODT scenario simulation and test system performance in real time.

IoT based Efficient and Secure Building Architecture with Constrained Application Protocol (CoAP)

The Internet of Things (IoT), one of the newest technologies, has great potential for many industry businesses. Smart building technology is one of the biggest IoT industries. IoT is used on a regular basis in many different industries. It is used in a variety of contexts, including physical defense, e-health, smart buildings, grids, cities, homes, asset management, and transportation management. IoT-level, networked smart buildings offer affordable assistance. Comfort, accessibility, security, and energy management are necessary for commercial space. Naturally, IoT-based solutions can satisfy these requirements. With the depletion of the energy supply and the increase in demand for energy, there has been a growing focus on energy usage and building maintenance. This paper uses emerging IoT technology to demonstrate a smart building design that is both safe and energy-efficient. Constrained Application Protocol (CoAP) is a crucial internet transmission protocol that assigns a unique address to every device. Data is transferred using this application layer protocol outside of secure channels. While there are several recommended techniques to protect sensitive data transfer, one likely way to improve the security aspect of CoAP for authentication and end-to-end security is through the use of Datagram Transport Layer Protection (DTLS).This paper propose a smart building design in which the Internet of Things is used to control the operation of all technological components. The simulation results show that compared to the situation of employing Message Queuing Telemetry Transport (MQTT), adopting CoAP in a smart building saves energy use by around 34.86%. In order to increase security in a smart building, this article also explains how to include the DTLS protocol inside of CoAPutilising enhancements from the Certificate Authority (CA).Finally, using the ContikiCooja (CC) simulator to simulate system performance and data collecting in terms of latency, throughput, and resource consumption, CoAP is utilized to modify orderer and peer nodes for performance research in smart building.

Synchronous integration method of mechatronic system design, geometric design, and simulation based on SysML

The benefits of integrated design using the Model Based System Engineering (MBSE) approach in the design process of mechatronic systems have gradually become apparent. The automatic generation of simulation models and geometric models based on System Modeling Languages (SysML) models enables system engineers to swiftly analyze and simulate system performance, visually depict design outcomes, and expedite the product development process. Due to the current system modeling's lack of model integration and geometric design functions, this paper proposes an integrated design and simulation method for mechatronic systems that can carry out complete model synchronization and verification, rapid geometric solution generation, and visual representation. Furthermore, a corresponding model synchronization integration framework is established. This framework primarily encompasses system de-sign, system simulation, and geometric design, with its model integration method being model transformation and model synchronization. The paper concludes with an example of the design process of a quadruped robot to validate the framework and its supported methods, providing a reference for other system design and integration endeavors.

Unraveling fundamental properties of power system resilience curves using unsupervised machine learning

Power system is vital to modern societies, while it is susceptible to hazard events. Thus, analyzing resilience characteristics of power system is important. The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades. However, the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves (e.g., residual performance and duration of recovery). Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. There is a dire dearth of empirical studies in the field, which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States. Through the use of unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power grid resilience curves, triangular curves, and trapezoidal curves. Triangular curves characterize resilience behavior based on three fundamental properties: 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

Deep Neural Network-Based Cloth Collision Detection Algorithm

The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations.

Development Of Digital Twin Technology in Hydraulics Based on Simulating and Enhancing System Performance

DT digital twin technology has become an essential tool in hydraulic systems. It not only offers a virtual representation of the actual plant, but also real-time monitoring and optimization of that same machinery. Digital Twin (DT) technology has become a cornerstone in the optimization of industrial processes, particularly in the domain of hydraulic systems. For example, this research aims to use digital twin technology to detect and fix leaks in hydraulic systems. By integrating advanced simulation algorithms for accurate leak detection and performance enhancement, this study presents a comprehensive framework. Combining techniques developed from both data-driven and state-of-the-art optimization methods our approach looks to change how leaks are detected in hydraulics. Our test introduces a comprehensive framework that not only accurately identifies leaks but also employs advanced simulation algorithms for subsequent performance enhancement. By bringing together data-driven insights and cutting-edge optimization methods, our work at the frontier of revolutionizing leak detection in hydraulic systems.

Design and verification of low-friction and high-performance monotube magnetorheological damper

The frictional force of the suspension damper is an essential factor affecting vehicle ride comfort. As an important basic component of intelligent suspension, magnetorheological (MR) damper has the advantages of simple structure and adjustable damping performance. However, the frictional force of the MR damper is excessively high, due to the high-pressure gas to avoid the cavitation effect. If the frictional force of MR damper can be reduced, the vibration isolation performance of the intelligent suspension based on the MR damper will be further improved. In this study, a monotube MR damper with low friction and high performance is proposed and investigated. Firstly, the structural design of the low-friction MR damper is carried out, and the mathematical model of its controllable damping force and uncontrollable damping force is established. Secondly, the key structural parameters of the MR piston are optimized, and the valve system is matched based on the optimized mechanical characteristics of the piston to avoid the cavitation effect. Thirdly, bench tests are carried out on the developed low-friction MR damper. The experimental results show that the frictional force of the MR damper is very low, and no cavitation effect occurs. Finally, the system performance simulation analysis is carried out on the 1/4 vehicle semi-active suspension using the MR damper, and the influence of frictional force on the performance evaluation indexes of passive and semi-active suspensions is investigated. The simulation results indicate that the influence of frictional force on semi-active suspension is higher than that of passive suspension, and reducing the frictional force of the MR damper is of great significance to improve the performance of the semi-active suspension.

Investigation of the Energy Storage Simulation and Evaluation Index System Performance Considering Network Transmission Constraints and Peak Shaving Imitation

Connecting a new type of Energy Storage with renewable energy is one of the solutions to promote low-carbon energy systems and improve energy flexibility. However, the evaluation index system for large-scale new types of Energy Storage is not yet sound. This paper takes the power export base of a high proportion of new energy as an example. They are combined with actual operational and historical statistical data from Shanxi. The construction of operation mathematical models is refined for energy storage. The configuration of energy storage is analyzed and considered under network transmission constraints and system peak-shaving constraints. Strong support is provided for China’s development vision of achieving dual carbon goals and building a new power system.

Selection of relevant features to detect and diagnose single and multiple simultaneous soft faults in air-source heat pumps

Soft faults in air-source heat pump systems can cause various issues, such as decreased performance, increased energy consumption, and long-term damage. This paper presents a comprehensive modeling approach of air-source heat pumps that considers individual and simultaneous soft faults validated against empirical experiments from the literature, a perspective limited in previous research. The methodology is based on the simulation of fault-free and faulty system performance utilizing heat pump modeling software. The results are a selection of variables and their trends to select the appropriate features to detect and diagnose single and double soft faults. The model also computes the impacts of these combined faults on COP, revealing significant reductions. Specifically, combinations of overcharge with condenser fouling and undercharge with compressor valve leakages induce COP reductions ranging from 17% to 38%, depending on operating conditions. The model can be used to develop faulty heat pump data, which can be adapted to other system typologies and conditions and used as input for data-driven Fault Detection and Diagnosis (FDD) methodologies.

Analisis Tarif Air PDAM Untuk Kelayakan Penerapan Sistem Pemanenan Air Hujan pada Skala Rumah Tangga

The limited availability of clean water is a problem that often occurs lately. Water use continues to increase due to population growth and technological developments. The availability of abundant rainwater and the application of a rainwater harvesting system are alternatives for water fulfillment as water supplies are limited. Rainwater harvesting system performance simulation modeling is essential in deciding the suitability of rainwater harvesting systems. In this study, the rainwater harvesting system simulation modeling was carried out using the Yield Before Spillage (YBS) algorithm in 3 cities in Indonesia (Palembang City, Bogor City, and Mataram City). This assessment considers 5 combinations of demand, 5 combinations of catchment area, 5 tank capacities, and 3 applicable water rates, resulting in 1,875 different value configurations. The performance and potential assessment of the rainwater harvesting system are determined by Water Saving Efficiency and Timetric Reliability, while the financial assessment is determined using the payback period and benefit-cost ratio. Benefits are derived from the potential for water savings with a rainwater harvesting system which is converted into financial savings according to the prevailing water rates and costs are determined based on the initial installation costs of the rainwater harvesting system. The results of this study indicate that the city of Bogor with an average rainfall of 3805.95 mm/year has the highest WSE rate of 100% and the fastest payback period is 4 years. Palembang City with an average rainfall of 2551.57 mm has the highest WSE rate of 99,273% and the fastest payback period is 5.42 years. Mataram, with an average rainfall of 1661.79 mm, has the highest WSE rate of 91.752% and the fastest payback period is 8.17 years. Topographical conditions and rainfall greatly affect the performance of the rainwater harvesting system.

Operation characteristics based on a novel performance model based on capacity utilization rate of a variable refrigerant flow air conditioning system

In the context of a large market share, enhancing the actual performance of variable refrigerant flow (VRF) air-conditioning systems is of considerable importance. The operating states of indoor units (IUs) significantly influence the energy efficiency of a VRF system with multiple indoor terminals. Therefore, it is challenging to address accurate and concise representation of the operational characteristics of IUs for analyzing their impact on system performance. This study explored a novel performance index called capacity utilization rate (CUR) to illustrate the effect of IUs on system performance and developed a performance model based on this index. Simulation and experimental results demonstrated that energy efficiency and evaporating temperature decrease with a reduction in CUR under specific ambient temperatures and part-load rates. The proposed performance model was applied to real buildings in field tests to examine the operational characteristics of the VRF system. This research offers valuable insights for system design, performance simulation, and performance evaluation of VRF systems.

Characteristics of automatic emergency braking responses in passenger vehicles evaluated in the IIHS front crash prevention program

Researchers can estimate the potential safety benefits of front crash prevention (FCP) systems by simulating system performance in rear-end crash scenarios reported to police or captured during naturalistic driving. Data to support assumptions about FCP systems in production vehicles, particularly automatic emergency braking (AEB), are limited. This study used detailed information from the Insurance Institute for Highway Safety’s (IIHS's) FCP evaluation to characterize interventions in vehicles that performed well (superior-rated vehicles) and those that did not perform as well (basic/advanced-rated vehicles) when approaching a stationary surrogate vehicle on a test track at 20 and 40 km/h, and estimated performance in similar conditions at higher speeds.Vehicle and video data from 3,231 IIHS FCP tests conducted at 20 and 40 km/h and 51 IIHS FCP research tests conducted at 50, 60, and 70 km/h with AEB responses were analyzed. Forward collision warning (FCW) and AEB time-to-collision (TTC), mean deceleration, maximum deceleration, and maximum jerk from the beginning of automatic braking to the end of braking or impact were computed for each test. Each dependent measure was modeled with test speed (20 km/h, 40 km/h), IIHS FCP test rating (superior, basic/advanced), and the interaction between test speed and rating. The models were used to estimate each dependent measure at 50, 60, and 70 km/h, and model predictions were compared with the observed performance of six vehicles in IIHS research test data.Vehicles with superior-rated systems warned and began braking earlier, had a greater average rate of deceleration, reached a higher peak deceleration, and had greater jerk than vehicles with basic/advanced-rated systems, on average. The interaction between test speed and vehicle rating was significant in each linear mixed-effects model, indicating that these differences changed with test speed. FCW and AEB in superior-rated vehicles occurred 0.05 and 0.10 s earlier, respectively, per 10-km/h increase in test speed compared with basic/advanced-rated vehicles. Mean deceleration and maximum deceleration for FCP systems in superior-rated vehicles increased 0.65 m/s2 and 0.60 m/s2 more, respectively, per 10-km/h increase in test speed than for systems in basic/advanced-rated vehicles. Maximum jerk increased 2.78 m/s3 per 10-km/h increase in test speed for basic/advanced-rated vehicles but decreased 0.25 m/s3 for systems in superior-rated vehicles. The root mean square error between the observed performance and estimated values at 50, 60, and 70 km/h indicated that the linear mixed-effects model had reasonable prediction accuracy for every measure except jerk at these out-of-sample data points.The findings from this study provide insight into the characteristics that make FCP effective for preventing crashes. Based on performance in the IIHS FCP test, vehicles with superior-rated FCP systems had earlier TTC thresholds and braked with greater deceleration that increased with speed compared with basic/advanced-rated systems. The linear mixed-effects models that were developed can guide assumptions about AEB response characteristics for superior-rated FCP systems in future simulation studies.

Multi-step depth model predictive control for photovoltaic maximum power point tracking under partial shading conditions

This work avoids the photovoltaic array operating point falling into local maximum power points (MPPs) and optimizes the dynamic process of photovoltaic array global MPP tracking under partial shading conditions (PSCs) by the proposed multi-step depth model predictive control (MDMPC). The MDMPC updates the dynamic conductance at different moments to achieve multi-step accurate predictions of the system state. Combining deep neural networks and the global MPP calculation model can rapidly estimate the global MPP under different topologies. The proposed two-stage optimization adaptively adjusts the optimization function according to the purpose of different stages. The simulated system response curves and performance indexes under different PSCs show that the MDMPC can avoid the operating point falling into local MPPs and accurately rapidly track to the global MPP with higher power generation efficiency. In addition, the hardware-in-the-loop experimental results verify that MDMPC can rapidly and accurately track the global MPP under three PSCs.

Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof.

As the main food source of the world's population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

Simulation and prediction of residential grid-connected photovoltaic system performance

This paper presents the analysis of the actual, predicted, and simulated technical performance of a residential 2.835 kW<sub>p</sub> retrofitted grid-connected photovoltaic (GCPV) system under the feed-in-tariff (FiT) scheme in Klang, Malaysia located in the equatorial region. The technical performance indices of the GCPV system were assessed based on the three-year energy production in 2018, 2019 and 2020. The actual and predicted technical performance were calculated using SEDA mathematical model, which the solar irradiation data was acquired from PVsyst software. Meanwhile, the simulated technical performance was obtained using PVsyst software. The results showed that the prediction using mathematical model has higher percentage difference within the range of 12.54-13.29%, compared to PVsyst simulation that was within 7.93-11.93%. This study has highlighted the factors that contributed to the technical performance underprediction of both mathematical model and PVsyst simulation, which are the estimation of losses and annual irradiation data accuracy. Lastly, the FiT gross income calculated for the three consecutive years were within the range of 3310.80 MYR and 3357.30 MYR. This FiT gross income result conveys an example of Malaysian case study, to enlighten the public, on the economic aspect of installing GCPV system under FiT scheme.

Comparative mathematical and data-driven models for simulating the performance of forward osmosis membrane under different draw solutions

Mathematical models have frequently been used to simulate system performance, including forward osmosis (FO)-related processes. More recently, Industry 4.0 has increased in importance, and artificial intelligence (AI) technology has proven to be effective at assisting decision-making across a range of industries, including membrane processes. This study, for the first time, proposes a comparison of the accuracy of predictions for water permeability under various types of draw solutions using conventional models and AI-based models. The results showed that the AI-based models, including artificial neural networks (NN) (2 layers; 10 neurons) and adaptive neuro-fuzzy inference system (NFIS) (with grid partition; membership functions = 5) performed significantly better than the conventional mathematical intermediate blocking (CIB) model, with the coefficient values (R) of 0.974, 0.994, and 0.963, respectively. The corresponding errors of these simulations were 0.04, 0.70, and 0.97 LMH, indicating that the AI technique was better suited than the mathematical CIB model for simulating FO-related system performance. Additionally, using AI to forecast system performance is timely (i.e., considerably quicker than developing a sophisticated mathematical model) and cost-effective (i.e., reducing the expense for analysis of environmental factors for CIB model construction). The authors also successfully constructed an application for end-users for better and simpler experiences with modeling and simulation, even for those with varying knowledge in the field.

Research on Multi-objective Optimisation Algorithm for Light and Heat Environment in Underground Atrium Buildings

This paper firstly defines the concepts of underground atrium body shape, climate adaptability and parametric design, analyses the climatic characteristics of cold regions to extract their climatic factors, and responds to the climate with the light and heat environment, extracts the influence factors of the light and heat environment of the underground atrium and the body shape factors of the underground atrium, and constructs the underground atrium body shape design parameter system according to the classification of the body shape factors. Secondly using Ladybug+Honeybee software, a parametric underground atrium performance simulation system is con-structed to simulate the light and heat performance of the underground atrium under the non-air-conditioned state by controlling the design parameters of the underground atrium's body shape, and to explore the role of each body shape parameter in relation to the light and heat environment of the underground atrium respectively. Then, using Ladybug+Honeybee+Wallacei software, we construct a multi-objective optimisation system for under-ground atrium shapes under non-air-conditioned condition with differentiation of light and heat performance, and explore underground atrium shapes suitable for cold regions.

Object level frustum culling based frame rate acceleration method

Frame rate is an important index to evaluate the performance of simulation systems. The higher it is, the more intuitive and realistic the simulation application will be. The terrain database rendering process in the simulation field takes a lot of time and hardware resources. Therefore, optimizing it leads to a significant speedup of the simulation system. The frustum culling method is very helpful in avoiding computations of things that are not visible. In this method, instead of sending all information to the graphics processing unit, visible and invisible elements will be sorted, and only visible elements will be rendered. This paper presents an effective frustum culling technique at object level. In the proposed method, the rendered region was frustum culled by objects, so the number of objects to be rendered in each frame was significantly reduced. The purpose of the study is to improve the rendering performance of terrain data in the Unity 3D framework. Experimental results have shown that the given method is very competitive.