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| 1 | A New Approach to Nonlinear Partial Differential Equations显示文摘In this paper,a novel method called variational iteration method is proposed to solve nonlinear partial differential equations without linearization or small perturbations.In this method,a correction functional is constructed by a general Lagrange multiplier,which can be identified via variational theory.An analytical solution can be obtained from its trial- function with possible unknown constants,which can be identified by imposing the boundary conditions,by successively iteration. | Jihuan HE (Shanghai University,Shanghai Institute of Applied Mathematics and Mechanics,Shanghai 2 0 0 0 72 ,China) | 1997 | Communications in Nonlinear Science and Numerical Simulation1997，2，4： | 16 |
| 2 | Global asymptotic stability of a two species competitive system with stage structure and harvesting显示文摘The dynamics of a two species competitive system, where the species one has two stages, a immature stage and a mature stage with harvesting, and the growth of the species two is of Lotka-Volterra nature, is modelled by a system of retarded functional differential equations. We obtain the conditions for global asymptotic stability of three nonnegative equilibria and the threshold of harvesting mature population. The effect of the delay on the population at equilibrium and the optimal harvesting of mature population are also considered. | XinyuSONG LansunCHEN 等 | 2001 | Communications in Nonlinear Science and Numerical Simulation2001，6，2： | 11 |
| 3 | Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches显示文摘Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector. | Cheng Fan Da Yan Fu Xiao Ao Li Jingjing An Xuyuan Kang | 2021 | Building Simulation2021，14，1： | 10 |
| 4 | The Superconvergence of Mixed FiniteElement Methods for Nonlinear HyperbolicEquations显示文摘Improved L2-error estimates are computed for mixed finite element methods for second order nonlinear hyperbolic equations. Superconvergence results, L∞intime and discrete L2 in space, are derived for both the solution and gradients on therectangular domain. Results are given for the continuous-time case. | YanpingCHEN YunqingHUANG | 1998 | Communications in Nonlinear Science and Numerical Simulation1998，3，3： | 8 |
| 5 | The Effects of Limiters on High Resolution Computations of Hypersonic Flows over Bodies with Complex Shapes显示文摘IntroductionSincetheideaofTVDwasproposedbyHarten[1]sometwodecadesago,varioustypesofhighresolutionschemesforsimulatingthecompr... | BoZHENG Chun-HianLEE | 1998 | Communications in Nonlinear Science and Numerical Simulation1998，3，2： | 8 |
| 6 | An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces显示文摘Reliable energy and performance prediction for building design and planning is important for newly-designed or retrofitted buildings.Window operating behavior has an important influence on the ventilation and energy consumption of these buildings under different realistic scenarios.Therefore,quantitatively describing this behavior and constructing a prediction model are important.In this work,an action-based Markov chain modeling approach for predicting window operating behavior in office spaces was proposed.Two summer measurement data(2016 and 2018)were used to verify the accuracy and validity of the modeling approach.The opening rate,outdoor temperature,time distribution,and on-off curve were proposed as four inspection standards.This study also compared the prediction performance between the action-based Markov chain modeling approach with the state-based Markov chain modeling approach,which is the most popular modeling approach to model occupant window operating behavior.This study proved that the yearly variation of occupants’behavior performed a form of action that remained unchanged during a certain period.Meanwhile,the results also proved that the action-based Markov chain modeling approach can reflect the actual window operating behavior accurately within an open-plan office,which is a beneficial supplement for energy-consumption simulation software in a window-state prediction module.The state-based Markov chain modeling approach showed better stability and accuracy in terms of the opening rate,whereas the action-based Markov chain modeling approach showed good consistency with the measurement data in the on-off curves and in situations with little data.For the on-off curves,the accuracy of action-based modeling approach in the prediction of window open-state is 20%higher. | Xin Zhou Tiance Liu Da Yan Xing Shi Xing Jin | 2021 | Building Simulation2021，14，2： | 7 |
| 7 | Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use显示文摘Occupant behavior is an important factor affecting building energy consumption.Many studies have been conducted recently to model occupant behavior and analyze its impact on building energy use.However,to achieve a reduction of energy consumption in buildings,the coordination between occupant behavior and energy-efficient technologies are essential to be considered simultaneously rather than separately considering the development of technologies and the analysis of occupant behavior.It is important to utilize energy-efficient technologies to guide the occupants to avoid unnecessary energy uses.This study,therefore,proposes a new concept,“technology-guided occupant behavior”to coordinate occupant behavior with energy-efficient technologies for building energy controls.The occupants are involved into the control loop of central air-conditioning systems by actively responding to their cooling needs.On-site tests are conducted in a Hong Kong campus building to analyze the performance of“technology-guided occupant behavior”on building energy use.According to the measured data,the occupant behavior guided by the technology could achieve“cooling on demand”principle and hence reduce the energy consumption of central air-conditioning system in the test building about 23.5%,which accounts for about 7.8%of total building electricity use. | Rui Tang Shengwei Wang Shaobo Sun | 2021 | Building Simulation2021，14，1： | 6 |
| 8 | Variational Iteration Method for Delay Differential Equations显示文摘The variational iteration method is shown to be applicable to delay differential equations for analytical approximate | Jihuan HE (Shanghai University,Shanghai Institute of Applied Mathematics and Mechanics,Shanghai 2 0 0 0 72 ,China) | 1997 | Communications in Nonlinear Science and Numerical Simulation1997，2，4： | 6 |
| 9 | A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development显示文摘The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can be effectively reduced using the demand-response control.The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared(PIR)sensor.However,the detection inaccuracy of the PIR sensor usually results in false-offs.To diminish the false-error frequency,the existing lighting system control simply deploys a delayed reaction period(e.g.,5 to 20 min),which is not sufficiently accurate for the demand-response operation.Therefore,in this research,a novel data-driven model predictive control(MPC)method that is based on the temporal sequential-based artificial neural network(TS-ANN)is proposed to overcome this challenge using an updated historical occupancy status.Using an office as case study,the proposed model is also compared with the traditional lighting system control method.In the proposed model,the occupancy data was trained to predict the occupancy pattern to improve the control.It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature.The simulation results indicated that the proposed method achieved higher accuracy(97.4%)and fewer false-offs(from 79.5 with traditional time delay method to 0.6 times per day)are achieved by the MPC model.The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation. | Yuan Jin Da Yan Xingxing Zhang Jingjing An Mengjie Han | 2021 | Building Simulation2021，14，1： | 6 |
| 10 | A real-time forecast of tunnel fire based on numerical database and artificial intelligence显示文摘The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters.To alleviate the potential casualties,fast while reasonable decisions should be made for rescuing,based on the timely prediction of fire development in tunnels.This paper targets to achieve a real-time prediction(within 1 s)of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence(Al)methods.A CFD database of 100 simulated tunnel fire scenarios under various fire location,fire size,and ventilation condition is established.The proposed Al model combines a Long Short-term Memory(LSTM)model and a Transpose Convolution Neural Network(TCNN).The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output.Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%.Also,the Al model can quickly identify the critical temperature field for safe evacuation(i.e.,a critical event)and guide emergency responses and firefighting activities.This study demonstrates the promising prospects of Al-based fire forecasts and smart firefighting in tunnel spaces. | Xiqiang Wu Xiaoning Zhang Xinyan Huang Fu Xiao Asif Usmani | 2022 | Building Simulation2022，15，4： | 6 |
| 11 | An Approximate Solution Technique Depending on an Artificial Parameter: A Special Example显示文摘IntroductionInthelasttwodecadeswiththerapiddevelopmentofnonlinearscience,therehasappearedeverincreasinginterestofscientists... | Jihuan HE (Shanghai University, Shanghai Institute of Applied Mathematics and Mechanics, Shanghai 200072, China) | 1998 | Communications in Nonlinear Science and Numerical Simulation1998，3，2： | 5 |
| 12 | Physiological and subjective thermal responses to heat exposure in northern and southern Chinese people显示文摘When studying the thermal adaptation of building occupants,understanding the effects of different thermal experiences on adaptation is necessary,particularly for moderate and severe heat exposure.However,this area has seen limited research.Further,skin temperature,a common parameter for quantifying thermal sensation,may insufficiently reflect the automatic thermoregulation of the human body.This study investigates the effects of long-term heat exposure on the human body using multiple physiological and subjective indexes.Two heat exposure experiments were conducted on healthy male participants from northern and southern China.Participant responses,including skin temperature,heart rate,heart rate variability,blood volume pulse(BVP),subjective thermal comfort thermal sensation,thermal acceptability,and normalized high and low frequency values were collected and compared The results indicated that the subjective responses of northern and southern participants were not significantly different;however,the subjective physiological symptoms and self-reported discomfort of the latter were less than those of the former,indicating that the southern participants had superior heat tolerance.Additionally,the physiological responses of all the participants were largely similar.However,southern participants showed slightly higher normalized high frequency and BVP values,indicating that they have more active vagus nerves and better vasodilation.They also showed a wider acceptable temperature range and better acclimation to heat exposure.Notably,the mean skin temperature could not effectively predict thermal sensation during heat exposure;this was more accurately achieved using the rate of change of skin temperature.These findings suggest that long-term thermal experiences can affect building occupants’thermal adaptability. | Yufan Lin Liu Yang Maohui Luo | 2021 | Building Simulation2021，14，6： | 5 |
| 13 | Evaluating the improvement effect of low-energy strategies on the summer indoor thermal environment and cooling energy consumption in a library building:A case study in a hot-humid and less-windy city of China显示文摘Public buildings such as libraries consume a vast amount of cooling energy for maintaining a comfortable and stable indoor environment in summer,especially in the hot-humid climate.This study used a case study approach to discuss the effect of low-energy strategies that can be applied to improve indoor thermal environment and cooling energy consumption of library buildings in hot and humid cities like Nanning City(a southern city,China).The use of cooling window shutters(a shutter with the effects of shading and evaporative cooling)and ceiling fans for generating airflow was considered as applicable energy-saving measures in this study,and a university library was selected as the study building in which the two energy-saving measures were employed.The SET*and annual cooling load before and after the adoption of the proposed measures were quantitatively investigated with a building energy consumption simulation software(DesignBuilder).Simulation results showed that the daytime SET*values can be reduced by 3.0℃and 4.5℃respectively on a typical summer day after the use of the cooling shutters and ceiling fans.Moreover,the cooling loads can also be decreased by 8.4%and 16.6%respectively.Particularly,the combination of these two measures enabled the daytime SET*value and annual cooling load lower by 7.0℃and 60.8%respectively. | Yigang Li Jiang He | 2021 | Building Simulation2021，14，5： | 5 |
| 14 | A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling显示文摘As the critical component of manufacturing systems,production scheduling aims to optimize objectives in terms of profit,efficiency,and energy consumption by reasonably determining the main factors including processing path,machine assignment,execute time and so on.Due to the large scale and strongly coupled constraints nature,as well as the real-time solving requirement in certain scenarios,it faces great challenges in solving the manufacturing scheduling problems.With the development of machine learning,Reinforcement Learning(RL)has made breakthroughs in a variety of decision-making problems.For manufacturing scheduling problems,in this paper we summarize the designs of state and action,tease out RL-based algorithm for scheduling,review the applications of RL for different types of scheduling problems,and then discuss the fusion modes of reinforcement learning and meta-heuristics.Finally,we analyze the existing problems in current research,and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization. | Ling Wang Zixiao Pan Jingjing Wang | 2021 | Complex System Modeling and Simulation2021，1，4： | 5 |
| 15 | Blow up problem for Landau-Lifshitz equations in two dimensions显示文摘The solutions of two dimensional Landau-Lifshitz equations, which blow up in finite time, are obtained. | BolingGUO YongqianHAN GanshanYANG | 2000 | Communications in Nonlinear Science and Numerical Simulation2000，5，1： | 5 |
| 16 | DeST 3.0:A new-generation building performance simulation platform显示文摘Buildings contribute to almost 30%of total energy consumption worldwide.Developing building energy modeling programs is of great significance for lifecycle building performance assessment and optimization.Advances in novel building technologies,the requirements of high-performance computation,and the demands for multi-objective models have brought new challenges for building energy modeling software and platforms.To meet the increasing simulation demands,DeST 3.0,a new-generation building performance simulation platform,was developed and released.The structure of DeST 3.0 incorporates four simulation engines,including building analysis and simulation(BAS)engine,HVAC system engine,combined plant simulation(CPS)engine,and energy system(ES)engine,connected by air loop and water loop balancing iterations.DeST 3.0 offers numerous new simulation features,such as advanced simulation modules for building envelopes,occupant behavior and energy systems,cross-platform and compatible simulation kernel,FMI/FMU-based co-simulation functionalities,and high-performance parallel simulation architecture.DeST 3.0 has been thoroughly evaluated and validated using code verification,inter-program comparison,and case-study calibration.DeST 3.0 has been applied in various aspects throughout the building lifecycle,supporting building design,operation,retrofit analysis,code appliance,technology adaptability evaluation as well as research and education.The new generation building simulation platform DeST 3.0 provides an efficient tool and comprehensive simulation platform for lifecycle building performance analysis and optimization. | Da Yan Xin Zhou Jingjing An Xuyuan Kang Fan Bu Youming Chen Yiqun Pan Yan Gao Qunli Zhang Hui Zhou Kuining Qiu Jing Liu Yan Liu Honglian Li Lei Zhang Hong Dong Lixin Sun Song Pan Xiang Zhou Zhe Tian Wenjie Zhang Ruhong Wu Hongsan Sun Yu Huang Xiaohong Su Yongwei Zhang Rui Shen Diankun Chen Guangyuan Wei Yixing Chen Jinqing Peng | 2022 | Building Simulation2022，15，11： | 5 |
| 17 | Analysis of a SIS epidemic model with stage structure and a delay显示文摘A disease transmission model of SIS type with Stage structure and a delay corresponding to the infectious period is formulated. By constructing the difference operator and using global convergence theorem, we show that the disease-free equilibrium is globally asymp- totically stable. The Stability of an endemic equilibrium are investigated for the differential- difference equations. | Yanni XIAO and Lansun CHEN (Academy of Mathematics & System Sciences, Academic Sinica, Beijing 100080, China | 2001 | Communications in Nonlinear Science and Numerical Simulation2001，6，1： | 5 |
| 18 | Study on the application of reinforcement learning in the operation optimization of HVAC system显示文摘Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this paper,we apply RL algorithm to the operation optimization of air-conditioning(AC)system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process.We use a variable air volume(VAV)air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller.We select control strategies with the rule-based control controller(RBC)and proportional-integral-derivative(PID)controller respectively as the reference cases.The results show that,for the air supply of single zone,the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning.The total energy consumption of AC system reduced by 7.7%and 4.7%,respectively compared with RBC and PID strategies.For the air supply of multi-zone,the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage.From the seventh year on,RL controller performs much better in terms of both non-comfortable time and operating costs of AC system,while the operating cost of AC system is reduced by 2.7%to 4.6%compared with the reference strategies.In addition,RL controller is more suitable for small-scale operation optimization problems. | Xiaolei Yuan Yiqun Pan Jianrong Yang Weitong Wang Zhizhong Huang | 2021 | Building Simulation2021，14，1： | 5 |
| 19 | Review of Dynamic Task Allocation Methods for UAV Swarms Oriented to Ground Targets显示文摘Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle(UAV)swarms task planning and the key technology to improve autonomy.The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms,the target and environment state,and the high real-time allocation requirements.Hence,dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning.In this work,a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects:the establishment of an allocation model and the solution of the allocation model.First,the basic concept and trigger scenario are introduced.Second,the research status and the advantages and disadvantages of the two allocation models are analyzed.Third,the research status and the advantages and disadvantages of several common dynamic task allocation algorithms,such as the algorithm based on market mechanisms,intelligent optimization algorithm,and clustering algorithm,are evaluated.Finally,the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted,and future research directions are established.This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology. | Qiang Peng Husheng Wu Ruisong Xue | 2021 | Complex System Modeling and Simulation2021，1，3： | 5 |
| 20 | Multi-objective optimization of thermochromic glazing based on daylight and energy performance evaluation显示文摘Many efforts have been detected to investigate thermochromic(TC)glazing for improving building energy saving,while only a few approaches for daylight performance analysis.In this study,the performance of TC glazing is investigated based on multi-objective optimization for minimizing energy demand while maximizing daylight availability.The effects of five parameters including transition temperature,solar transmittance in clear state,solar transmittance modulation ability,luminous transmittance in clear state,and luminous modulation ability on the building energy consumption and useful daylighting illuminance(UDI_(300-3000))are examined.Linear Programming Technique for Multi-dimensional Analysis of Preference(LINMAP)is used for the decision-making of Pareto frontier.This research aims to explore the ideal thermochromic glazing by considering the daylight and energy performance of a typical office room,taking the weather condition of Xiamen,China as an example.Although it is impossible to achieve both optimal values of energy consumption and UDI_(300-3000)simultaneously,the proposed multi-objective optimization method could still provide low energy consumption with sufficient daylight.The non-dominated sorting of Pareto optimal solution(POS)demonstrated that the optimum building energy consumption and UDI_(300-3000)for single glazed windows are 46.64 kWh/m^(2)and 70.92%,respectively,while the values for double glazed windows are 44.40 kWh/m^(2)and 71.88%,respectively.The selected hypothetical TC windows can improve the building energy and daylighting performance simultaneously when compared with traditional clear single and double glazed windows.The presented framework provides a multi-objective optimization method to determine the most suitable TC glazing technologies for designers and architects during the design and retrofit procedure. | Xiaoqiang Hong Feng Shi Shaosen Wang Xuan Yang Yue Yang | 2021 | Building Simulation2021，14，6： | 4 |
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