Modelling最新文献_第3页

An Efficient Explicit Moving Particle Simulation Solver for Simulating Free Surface Flow on Multicore CPU/GPUs 在多核 CPU/GPU 上模拟自由表面流的高效显式移动粒子模拟求解器

Modelling Pub Date : 2024-02-19 DOI: 10.3390/modelling5010015

Yu Zhao, Fei Jiang, Shinsuke Mochizuki

引用次数: 0

Model for Hydrogen Production Scheduling Optimisation 氢气生产调度优化模型

Modelling Pub Date : 2024-02-19 DOI: 10.3390/modelling5010014

V. Komašilovs, A. Zacepins, A. Kviesis, Vladislavs Bezrukovs

{"title":"Model for Hydrogen Production Scheduling Optimisation","authors":"V. Komašilovs, A. Zacepins, A. Kviesis, Vladislavs Bezrukovs","doi":"10.3390/modelling5010014","DOIUrl":"https://doi.org/10.3390/modelling5010014","url":null,"abstract":"This scientific article presents a developed model for optimising the scheduling of hydrogen production processes, addressing the growing demand for efficient and sustainable energy sources. The study focuses on the integration of advanced scheduling techniques to improve the overall performance of the hydrogen electrolyser. The proposed model leverages constraint programming and satisfiability (CP-SAT) techniques to systematically analyse complex production schedules, considering factors such as production unit capacities, resource availability and energy costs. By incorporating real-world constraints, such as fluctuating energy prices and the availability of renewable energy, the optimisation model aims to improve overall operational efficiency and reduce production costs. The CP-SAT was applied to achieve more efficient control of the electrolysis process. The optimisation of the scheduling task was set for a 24 h time period with time resolutions of 1 h and 15 min. The performance of the proposed CP-SAT model in this study was then compared with the Monte Carlo Tree Search (MCTS)-based model (developed in our previous work). The CP-SAT was proven to perform better but has several limitations. The model response to the input parameter change has been analysed.","PeriodicalId":504049,"journal":{"name":"Modelling","volume":"19 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methodology for International Transport Corridor Macro-Modeling Using Petri Nets at the Early Stages of Corridor Development with Limited Input Data 在输入数据有限的情况下，利用 Petri 网在国际运输走廊开发早期阶段进行宏观建模的方法

Modelling Pub Date : 2024-02-17 DOI: 10.3390/modelling5010013

I. Kabashkin, Zura Sansyzbayeva

{"title":"Methodology for International Transport Corridor Macro-Modeling Using Petri Nets at the Early Stages of Corridor Development with Limited Input Data","authors":"I. Kabashkin, Zura Sansyzbayeva","doi":"10.3390/modelling5010013","DOIUrl":"https://doi.org/10.3390/modelling5010013","url":null,"abstract":"International transport corridors (ITCs) are intricate logistical networks essential for global trade flows. The effective modeling of these corridors provides invaluable insights into optimizing the transport system. However, existing approaches have significant limitations in dynamically representing the complexities and uncertainties inherent in ITC operations and at the early stages of ITC development when data are limited. This gap is addressed through the application of Evaluation Petri Nets (E-Nets), which facilitate the detailed, flexible, and responsive macro-modeling of international transport corridors. This paper proposes a novel methodology for developing E-Net-based macro-models of corridors by incorporating key parameters like transportation time, costs, and logistics performance. The model is scalable, enabling analysis from an international perspective down to specific country segments. E-Nets overcome limitations of conventional transport models by capturing the interactive, stochastic nature of ITCs. The proposed modeling approach and scalability provide strategic insights into optimizing corridor efficiency. This research delivers a streamlined yet comprehensive methodology for ITC modeling using E-Nets. The presented framework has substantial potential for enhancing logistics system analysis and planning.","PeriodicalId":504049,"journal":{"name":"Modelling","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stress–Strength Reliability of the Type P(X Birnbaum-Saunders 组件的 P(X<Y) 型应力强度可靠性：一般结果、模拟和真实数据集应用

Modelling Pub Date : 2024-02-15 DOI: 10.3390/modelling5010012

F. Quintino, L. Ozelim, Tiago A. da Fonseca, P. N. Rathie

引用次数: 0

Stress–Strength Reliability of the Type P(X Birnbaum-Saunders 组件的 P(X<Y) 型应力强度可靠性：一般结果、模拟和真实数据集应用

Modelling Pub Date : 2024-02-15 DOI: 10.3390/modelling5010012

F. Quintino, L. Ozelim, Tiago A. da Fonseca, P. N. Rathie

引用次数: 0

From Data to Draught: Modelling and Predicting Mixed-Culture Beer Fermentation Dynamics Using Autoregressive Recurrent Neural Networks 从数据到生啤酒：利用自回归递归神经网络模拟和预测混合培养啤酒发酵动态

Modelling Pub Date : 2024-02-07 DOI: 10.3390/modelling5010011

Alexander O’Brien, Hongwei Zhang, Daniel M. Allwood, Andy Rawsthorne

{"title":"From Data to Draught: Modelling and Predicting Mixed-Culture Beer Fermentation Dynamics Using Autoregressive Recurrent Neural Networks","authors":"Alexander O’Brien, Hongwei Zhang, Daniel M. Allwood, Andy Rawsthorne","doi":"10.3390/modelling5010011","DOIUrl":"https://doi.org/10.3390/modelling5010011","url":null,"abstract":"The ascendency of the craft beer movement within the brewing industry may be attributed to its commitment to unique flavours and innovative styles. Mixed-culture fermentation, celebrated for its novel organoleptic profiles, presents a modelling challenge due to its complex microbial dynamics. This study addresses the inherent complexity of modelling mixed-culture beer fermentation while acknowledging the condition monitoring limitations of craft breweries, namely sporadic offline sampling rates and limited available measurement parameters. A data-driven solution is proposed, utilising an Autoregressive Recurrent Neural Network (AR-RNN) to facilitate the production of novel, replicable, mixed-culture fermented beers. This research identifies time from pitch, specific gravity, pH, and fluid temperature as pivotal model parameters that are cost-effective for craft breweries to monitor offline. Notably, the autoregressive RNN fermentation model is generated using high-frequency multivariate data, a departure from intermittent offline measurements. Employing the trained autoregressive RNN framework, we demonstrate its robust forecasting prowess using limited offline input data, emphasising its ability to capture intricate fermentation dynamics. This data-driven approach offers significant advantages, showcasing the model’s accuracy across various fermentation configurations. Moreover, tailoring the design to the craft beer market’s unique demands significantly enhances the model’s practicable predictive capabilities. It empowers nuanced decision-making in real-world mixed-culture beer production. Furthermore, this model lays the groundwork for future studies, highlighting transformative possibilities for cost-effective model-based control systems in the craft beer sector.","PeriodicalId":504049,"journal":{"name":"Modelling","volume":"160 3‐4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139796759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Data to Draught: Modelling and Predicting Mixed-Culture Beer Fermentation Dynamics Using Autoregressive Recurrent Neural Networks 从数据到生啤酒：利用自回归递归神经网络模拟和预测混合培养啤酒发酵动态

Modelling Pub Date : 2024-02-07 DOI: 10.3390/modelling5010011

Alexander O’Brien, Hongwei Zhang, Daniel M. Allwood, Andy Rawsthorne

{"title":"From Data to Draught: Modelling and Predicting Mixed-Culture Beer Fermentation Dynamics Using Autoregressive Recurrent Neural Networks","authors":"Alexander O’Brien, Hongwei Zhang, Daniel M. Allwood, Andy Rawsthorne","doi":"10.3390/modelling5010011","DOIUrl":"https://doi.org/10.3390/modelling5010011","url":null,"abstract":"The ascendency of the craft beer movement within the brewing industry may be attributed to its commitment to unique flavours and innovative styles. Mixed-culture fermentation, celebrated for its novel organoleptic profiles, presents a modelling challenge due to its complex microbial dynamics. This study addresses the inherent complexity of modelling mixed-culture beer fermentation while acknowledging the condition monitoring limitations of craft breweries, namely sporadic offline sampling rates and limited available measurement parameters. A data-driven solution is proposed, utilising an Autoregressive Recurrent Neural Network (AR-RNN) to facilitate the production of novel, replicable, mixed-culture fermented beers. This research identifies time from pitch, specific gravity, pH, and fluid temperature as pivotal model parameters that are cost-effective for craft breweries to monitor offline. Notably, the autoregressive RNN fermentation model is generated using high-frequency multivariate data, a departure from intermittent offline measurements. Employing the trained autoregressive RNN framework, we demonstrate its robust forecasting prowess using limited offline input data, emphasising its ability to capture intricate fermentation dynamics. This data-driven approach offers significant advantages, showcasing the model’s accuracy across various fermentation configurations. Moreover, tailoring the design to the craft beer market’s unique demands significantly enhances the model’s practicable predictive capabilities. It empowers nuanced decision-making in real-world mixed-culture beer production. Furthermore, this model lays the groundwork for future studies, highlighting transformative possibilities for cost-effective model-based control systems in the craft beer sector.","PeriodicalId":504049,"journal":{"name":"Modelling","volume":"12 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139856464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the Impact of Copula Selection on Reliability Measures of Type P(X < Y) with Generalized Extreme Value Marginals 评估 Copula 选择对具有广义极值边际的 P(X < Y) 型可靠性度量的影响

Modelling Pub Date : 2024-01-28 DOI: 10.3390/modelling5010010

Rebeca Klamerick Lima, F. Quintino, Tiago A. da Fonseca, Luan Carlos de Sena Monteiro Ozelim, P. N. Rathie, H. Saulo

{"title":"Assessing the Impact of Copula Selection on Reliability Measures of Type P(X < Y) with Generalized Extreme Value Marginals","authors":"Rebeca Klamerick Lima, F. Quintino, Tiago A. da Fonseca, Luan Carlos de Sena Monteiro Ozelim, P. N. Rathie, H. Saulo","doi":"10.3390/modelling5010010","DOIUrl":"https://doi.org/10.3390/modelling5010010","url":null,"abstract":"In reliability studies, we are interested in the behaviour of a system when it interacts with its surrounding environment. To assess the system’s behaviour in a reliability sense, we can take the system’s intrinsic quality as strength and the outcome of interactions as stress. Failure is observed whenever stress exceeds strength. Taking Y as a random variable representing the stress the system experiences and random variable X as its strength, the probability of not failing can be taken as a proxy for the reliability of the component and given as P(Y<X)=1−P(X<Y). This way, in the present paper, it is considered that X and Y follow generalized extreme value distributions, which represent a family of continuous probability distributions that have been extensively applied in engineering and economic contexts. Our contribution deals with a more general scenario where stress and strength are not independent and copulas are used to model the dependence between the involved random variables. In such modelling framework, we explored the proper selection of copula models characterizing the dependence structure. The Gumbel–Hougaard, Frank, and Clayton copulas were used for modelling bivariate data sets. In each case, information criteria were considered to compare the modelling capabilities of each copula. Two economic applications, as well as an engineering one, on real data sets are discussed. Overall, an easy-to-use methodological framework is described, allowing practitioners to apply it to their own research projects.","PeriodicalId":504049,"journal":{"name":"Modelling","volume":"403 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stepwise Regression for Increasing the Predictive Accuracy of Artificial Neural Networks: Applications in Benchmark and Advanced Problems 提高人工神经网络预测准确性的逐步回归：基准和高级问题中的应用

Modelling Pub Date : 2024-01-12 DOI: 10.3390/modelling5010009

G. Papazafeiropoulos

{"title":"Stepwise Regression for Increasing the Predictive Accuracy of Artificial Neural Networks: Applications in Benchmark and Advanced Problems","authors":"G. Papazafeiropoulos","doi":"10.3390/modelling5010009","DOIUrl":"https://doi.org/10.3390/modelling5010009","url":null,"abstract":"A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression between the output and the input data. In this study, the SR procedure adds quadratic terms and products of the input variables on pairs. Afterwards, the ANN is trained based on the enhanced input data obtained by SR. After testing the proposed SR-ANN algorithm in four benchmark function approximation problems found in the literature, six examples of multivariate training data are considered, of two different sizes (big and small) often encountered in engineering applications and of three different distributions in which the diversity and correlation of the data are varied, and the testing performance of the ANN for varying sizes of its hidden layer is investigated. It is shown that the proposed SR-ANN algorithm can reduce the prediction error by a factor of up to 26 and increase the regression coefficient between predicted and actual data in all cases compared to ANNs trained with ordinary algorithms.","PeriodicalId":504049,"journal":{"name":"Modelling","volume":"55 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control 利用模型预测控制为具有三个控制输入的汽车空调设计控制器

Modelling Pub Date : 2024-01-03 DOI: 10.3390/modelling5010008

Trevor Parent, J. J. Defoe, Afshin Rahimi

{"title":"Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control","authors":"Trevor Parent, J. J. Defoe, Afshin Rahimi","doi":"10.3390/modelling5010008","DOIUrl":"https://doi.org/10.3390/modelling5010008","url":null,"abstract":"Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model.","PeriodicalId":504049,"journal":{"name":"Modelling","volume":"7 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0