A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variable...A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.展开更多
Excessive exposure to heat can lead to injuries,illness,and death among mineworkers.The actual cost of heat-related injuries and illnesses is unknown because of underreporting and lack of symptom recognition.Multi-fac...Excessive exposure to heat can lead to injuries,illness,and death among mineworkers.The actual cost of heat-related injuries and illnesses is unknown because of underreporting and lack of symptom recognition.Multi-factorial,evidence-based,and field-ready guidelines for identifying–and predicting–physiolo gical markers of heat strain are currently unavailable.The predicted heat strain(PHS)model,is the latest attempt by mining companies to aid in the evaluation and management of occupational heat exposures.The adopted algorithm relies on worksite environmental measurements and an estimate of individual metabolic rate for mine workers to provide an estimate of the workers’core temperature during a work shift.There are several known limitations of the PHS model,including the assumption that the subject worker is hydrated and fit.A modified PHS model was presented based on eight physical parameters that are measured at different intervals during a work shift;these parameters are air temperature,relative humidity,air velocity,radiation,metabolic rate,acclimatization,clothing insulation and posture.To validate the results,the predictions from the modified PHS model were compared with direct physiological measurements obtained from ingestible pills and heat stress monitors under different environmental and working conditions.展开更多
The accurate simulation of boundary layer transition process plays a very important role in the prediction of turbine blade temperature field. Based on the Abu-Ghannam and Shaw （AGS） and c-Re h transition models, a ...The accurate simulation of boundary layer transition process plays a very important role in the prediction of turbine blade temperature field. Based on the Abu-Ghannam and Shaw （AGS） and c-Re h transition models, a 3D conjugate heat transfer solver is developed, where the fluid domain is discretized by multi-block structured grids, and the solid domain is discretized by unstructured grids. At the unmatched fluid/solid interface, the shape function interpolation method is adopted to ensure the conservation of the interfacial heat flux. Then the shear stress transport （SST） model, SST ＆ AGS model and SST ＆ c-Re h model are used to investigate the flow and heat transfer characteristics of Mark II turbine vane. The results indicate that compared with the full turbulence model （SST model）, the transition models could improve the prediction accuracy of temperature and heat transfer coefficient at the laminar zone near the blade leading edge. Compared with the AGS transition model, the c-Re h model could predict the transition onset location induced by shock/boundary layer interaction more accurately, and the prediction accuracy of temperature field could be greatly improved.展开更多
The prediction of heat pump system has more complicated characteristics, and the prediction accuracy of the existing single model is not ideal. From the perspective of energy efficiency and energy consumption, it is n...The prediction of heat pump system has more complicated characteristics, and the prediction accuracy of the existing single model is not ideal. From the perspective of energy efficiency and energy consumption, it is necessary to improve the accuracy of prediction. A sewage source heat pump system in Shenyang, China, was used as the research object in this paper. The ARIMA model, the BP neural network model, and the ARIMA-BP integrated model, were built. The accuracy of the predicted values of heat supply obtained by the models was verified. The prediction accuracy of the model was verified in extreme weather. The completeness of the model validation was improved. Three prediction models had been applied to the water source heat pump system and the soil source heat pump system. The adaptability and generalization of the model were verified. The number of training sets for heat supply prediction was divided. The number of training sets at the beginning of the heating season was analyzed. The results showed that the mean absolute percentage errors of the ARIMA model, BP neural network model and ARIMA-BP integrated model were 5.37 %, 5.97 % and 3.21 %, respectively. The root mean square errors were 177.31, 186.98, 139.44, respectively. The ARIMA-BP integrated model had a prediction accuracy that improved by 2.16 % compared to the ARIMA model. The ARIMA-BP integrated model had a prediction accuracy that improved by 2.76 % compared to the BP model. In extreme weather, the mean absolute percentage error was 7.83 %, the root mean square error was 296.42. The overall error was also within a reasonable range. The ARIMA-BP integrated model had high prediction accuracy and good applicability and generalization. At the beginning of the heating season, the heat supply can be better predicted when the number of training sets is 4 days.展开更多
Heat exchangers are the core components of energy transfer and conversion and are widely used in the energy,chemical,and other fields.In an actual operational process,load changes lead to variations in the operating c...Heat exchangers are the core components of energy transfer and conversion and are widely used in the energy,chemical,and other fields.In an actual operational process,load changes lead to variations in the operating conditions of the heat exchanger.Evaluating the heat-transfer performance is crucial for the safe and efficient operation of the system.To realize high-precision heat transfer prediction through simulations,instead of using traditional solid equipment,this study proposed a heat transfer prediction modeling method that combines three-dimensional high-precision and one-dimensional real-time dynamic simulations.This method combines the high-precision advantage of three-dimensional simulation with the real-time advantage of one-dimensional simulation.To verify the feasibility of the modeling method,a heat transfer prediction model was constructed based on the heat transfer channel structure of a CO_(2)mixture heat transfer characteristic experimental test system.The steady-state and dynamic heat transfer characteristics of CO_(2)/R32 mixtures were simulated and experimentally tested.Finally,the real-time operational capability of the heat transfer prediction model was verified using a real-time simulator.The results showed that the heat transfer prediction model modeling method proposed in this study could improve the accuracy by 1.75-4.64 times compared with the conventional one-dimensional dynamic model.The established heat transfer prediction model exhibited good accuracy for both dynamic and steady-state processes.The average relative errors with the experimental results were in the range of 0.91%-2.83%under six sets of experimental tests.Thus,the proposed heat transfer prediction model can predict the heat transfer process in real-time under all experimental heat source conditions.展开更多
Accurate prediction of the heat load is the basic premise of intelligent regulation of the heating system,which helps to realize effective management of heating,ventilation,air conditioning system.For the problem that...Accurate prediction of the heat load is the basic premise of intelligent regulation of the heating system,which helps to realize effective management of heating,ventilation,air conditioning system.For the problem that the weight of each influencing factor is not taken into account in the current heat load prediction and is not highly targeted,this article deeply explores the influence of different factors on the room heat load,and we propose a method to calculate room heat load prediction based on the combination of analytic hierarchy process(AHP)and back-propagation(BP)neural network.Firstly,eight environmental factors affecting the heat load are selected as prediction inputs through parametric analysis,and then the weights of each input are determined by AHP and normalize the prediction data by combining expert opinions,and finally do one-to-one training the quantified score and the room heat load to predict the future heat load by BP neural network.The simulation tests show that the mean absolute relative error(MARE)of the proposed prediction method is 5.40%.This article also verifies the influence of different expert opinions on the stability of the model.The results show that the proposed method can guarantee higher prediction accuracy and stability.展开更多
Accurate prediction of supercritical CO_(2)(scCO_(2))heat transfer is important for heat exchanger design and safe operation of scCO_(2)power cycles.The main prediction method is empirical correlation.This paper demon...Accurate prediction of supercritical CO_(2)(scCO_(2))heat transfer is important for heat exchanger design and safe operation of scCO_(2)power cycles.The main prediction method is empirical correlation.This paper demonstrates an alternative way by artificial neural networks(ANN)model with two hidden layers.To assess widely cited correlations and newly developed ANN model,scCO_(2)heat transfer experiment in vertical tube with pressure up to 20.8 MPa was performed to extend experiment database,which includes 2674 runs.Compared with empirical correlations,the ANN model is promising for following advantages:(1)ANN model has much better prediction accuracy.The mean relative error,mean absolute relative error and the root-mean-square relative error between predicted and measured wall temperatures are eA=0.38%,eR=4.88%and eS=7.29%,respectively.(2)ANN model performs faster computation speed.(3)ANN model can accurately and speedily predict scCO_(2)heat transfer performance for both normal heat transfer and heat transfer deterioration modes.The trained ANN program is provided with this paper,which is a useful tool and can be directly applied in engineering of scCO_(2)heat transfer.展开更多
Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-qui...Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.展开更多
基金supported by the National Special Fund for Major Research Instrument Development(2011YQ140145)111 Project (B07009)+1 种基金the National Natural Science Foundation of China(11002013)Defense Industrial Technology Development Program(A2120110001 and B2120110011)
文摘A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.
文摘Excessive exposure to heat can lead to injuries,illness,and death among mineworkers.The actual cost of heat-related injuries and illnesses is unknown because of underreporting and lack of symptom recognition.Multi-factorial,evidence-based,and field-ready guidelines for identifying–and predicting–physiolo gical markers of heat strain are currently unavailable.The predicted heat strain(PHS)model,is the latest attempt by mining companies to aid in the evaluation and management of occupational heat exposures.The adopted algorithm relies on worksite environmental measurements and an estimate of individual metabolic rate for mine workers to provide an estimate of the workers’core temperature during a work shift.There are several known limitations of the PHS model,including the assumption that the subject worker is hydrated and fit.A modified PHS model was presented based on eight physical parameters that are measured at different intervals during a work shift;these parameters are air temperature,relative humidity,air velocity,radiation,metabolic rate,acclimatization,clothing insulation and posture.To validate the results,the predictions from the modified PHS model were compared with direct physiological measurements obtained from ingestible pills and heat stress monitors under different environmental and working conditions.
基金National Natural Science Foundation of China(Grant No.91130013)Innovation Foundation of BUAA for PhD Graduates(YWF-12-RBYJ-010)Specialized Research Fund for the Doctoral Program of Higher Education(20101102110011)for funding this work
文摘The accurate simulation of boundary layer transition process plays a very important role in the prediction of turbine blade temperature field. Based on the Abu-Ghannam and Shaw （AGS） and c-Re h transition models, a 3D conjugate heat transfer solver is developed, where the fluid domain is discretized by multi-block structured grids, and the solid domain is discretized by unstructured grids. At the unmatched fluid/solid interface, the shape function interpolation method is adopted to ensure the conservation of the interfacial heat flux. Then the shear stress transport （SST） model, SST ＆ AGS model and SST ＆ c-Re h model are used to investigate the flow and heat transfer characteristics of Mark II turbine vane. The results indicate that compared with the full turbulence model （SST model）, the transition models could improve the prediction accuracy of temperature and heat transfer coefficient at the laminar zone near the blade leading edge. Compared with the AGS transition model, the c-Re h model could predict the transition onset location induced by shock/boundary layer interaction more accurately, and the prediction accuracy of temperature field could be greatly improved.
基金the National Natural Science Foundation of China(project number 52108081)the Foundation of Liaoning Province Education Administration(Project number LJKZ0577)for providing financial support and thank you to the reviewers for their advice and comments.
文摘The prediction of heat pump system has more complicated characteristics, and the prediction accuracy of the existing single model is not ideal. From the perspective of energy efficiency and energy consumption, it is necessary to improve the accuracy of prediction. A sewage source heat pump system in Shenyang, China, was used as the research object in this paper. The ARIMA model, the BP neural network model, and the ARIMA-BP integrated model, were built. The accuracy of the predicted values of heat supply obtained by the models was verified. The prediction accuracy of the model was verified in extreme weather. The completeness of the model validation was improved. Three prediction models had been applied to the water source heat pump system and the soil source heat pump system. The adaptability and generalization of the model were verified. The number of training sets for heat supply prediction was divided. The number of training sets at the beginning of the heating season was analyzed. The results showed that the mean absolute percentage errors of the ARIMA model, BP neural network model and ARIMA-BP integrated model were 5.37 %, 5.97 % and 3.21 %, respectively. The root mean square errors were 177.31, 186.98, 139.44, respectively. The ARIMA-BP integrated model had a prediction accuracy that improved by 2.16 % compared to the ARIMA model. The ARIMA-BP integrated model had a prediction accuracy that improved by 2.76 % compared to the BP model. In extreme weather, the mean absolute percentage error was 7.83 %, the root mean square error was 296.42. The overall error was also within a reasonable range. The ARIMA-BP integrated model had high prediction accuracy and good applicability and generalization. At the beginning of the heating season, the heat supply can be better predicted when the number of training sets is 4 days.
基金supported by the National Key R&D Program of China(Grant No.2022YFE0100100)。
文摘Heat exchangers are the core components of energy transfer and conversion and are widely used in the energy,chemical,and other fields.In an actual operational process,load changes lead to variations in the operating conditions of the heat exchanger.Evaluating the heat-transfer performance is crucial for the safe and efficient operation of the system.To realize high-precision heat transfer prediction through simulations,instead of using traditional solid equipment,this study proposed a heat transfer prediction modeling method that combines three-dimensional high-precision and one-dimensional real-time dynamic simulations.This method combines the high-precision advantage of three-dimensional simulation with the real-time advantage of one-dimensional simulation.To verify the feasibility of the modeling method,a heat transfer prediction model was constructed based on the heat transfer channel structure of a CO_(2)mixture heat transfer characteristic experimental test system.The steady-state and dynamic heat transfer characteristics of CO_(2)/R32 mixtures were simulated and experimentally tested.Finally,the real-time operational capability of the heat transfer prediction model was verified using a real-time simulator.The results showed that the heat transfer prediction model modeling method proposed in this study could improve the accuracy by 1.75-4.64 times compared with the conventional one-dimensional dynamic model.The established heat transfer prediction model exhibited good accuracy for both dynamic and steady-state processes.The average relative errors with the experimental results were in the range of 0.91%-2.83%under six sets of experimental tests.Thus,the proposed heat transfer prediction model can predict the heat transfer process in real-time under all experimental heat source conditions.
基金supported by the Natural Science Foundation of China(No.61765012)the Natural Science Foundation of Inner Mongolia Autonomous Region(No.2021LHBS05005)+4 种基金the Science and Technology Research Project of Inner Mongolia Autonomous Region Higher Education(No.2021SHZR0620)the Inner Mongolia Autonomous Region 2017 Science and Technology Innovation Guidance Award Funding Projects(No.2017CXYD-2)the Natural Science Foundation of Inner Mongolia Autonomous Region(No.2019MS05008).The funders had no role in the design of the studyin the collection,analyses,or interpretation of datain the writing of the manuscript,or in the decision to publish the results.
文摘Accurate prediction of the heat load is the basic premise of intelligent regulation of the heating system,which helps to realize effective management of heating,ventilation,air conditioning system.For the problem that the weight of each influencing factor is not taken into account in the current heat load prediction and is not highly targeted,this article deeply explores the influence of different factors on the room heat load,and we propose a method to calculate room heat load prediction based on the combination of analytic hierarchy process(AHP)and back-propagation(BP)neural network.Firstly,eight environmental factors affecting the heat load are selected as prediction inputs through parametric analysis,and then the weights of each input are determined by AHP and normalize the prediction data by combining expert opinions,and finally do one-to-one training the quantified score and the room heat load to predict the future heat load by BP neural network.The simulation tests show that the mean absolute relative error(MARE)of the proposed prediction method is 5.40%.This article also verifies the influence of different expert opinions on the stability of the model.The results show that the proposed method can guarantee higher prediction accuracy and stability.
基金The study was supported by the National Key R&D Program of China(2017YFB0601801)the National Natural Science Foundation of China(51806065)Fundamental Research Funds for Central Universities(2020DF002).
文摘Accurate prediction of supercritical CO_(2)(scCO_(2))heat transfer is important for heat exchanger design and safe operation of scCO_(2)power cycles.The main prediction method is empirical correlation.This paper demonstrates an alternative way by artificial neural networks(ANN)model with two hidden layers.To assess widely cited correlations and newly developed ANN model,scCO_(2)heat transfer experiment in vertical tube with pressure up to 20.8 MPa was performed to extend experiment database,which includes 2674 runs.Compared with empirical correlations,the ANN model is promising for following advantages:(1)ANN model has much better prediction accuracy.The mean relative error,mean absolute relative error and the root-mean-square relative error between predicted and measured wall temperatures are eA=0.38%,eR=4.88%and eS=7.29%,respectively.(2)ANN model performs faster computation speed.(3)ANN model can accurately and speedily predict scCO_(2)heat transfer performance for both normal heat transfer and heat transfer deterioration modes.The trained ANN program is provided with this paper,which is a useful tool and can be directly applied in engineering of scCO_(2)heat transfer.
文摘Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.
