Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring...Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.展开更多
The Balanced Truncation Method (BTM) is applied to an even distributed RC interconnect case by using Wang's closed-forms of even distributed RC interconnect models. The results show that extremely high order RC in...The Balanced Truncation Method (BTM) is applied to an even distributed RC interconnect case by using Wang's closed-forms of even distributed RC interconnect models. The results show that extremely high order RC interconnect can be high-accurately approximated by only third order balanced model. Related simulations are executed in both time domain and frequency domain. The results may be applied to VLSI interconnect model reduction and design.展开更多
BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To...BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.展开更多
The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimizati...The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimization problem.Currently,a comprehensive framework for the structural design and optimization of compound balanced BPUs is lacking.Therefore,this study proposes a novel structural design scheme for BPUs,aiming to meet the practical needs of designers and operators by sequentially optimizing both the dynamic characteristics and balance properties of the BPUs.A dynamic model of compound balanced BPU was established based on D'Alembert's principle.The constraints for structural dimensions were formulated based on the actual operational requirements and design experience with BPUs.To optimize the structure,three algorithms were employed:the particle swarm optimization(PSO)algorithm,the genetic algorithm(GA),and the gray wolf optimization(GWO)algorithm.Each newly generated individuals are regulated by constraints to ensure the rationality of the outcomes.Furthermore,the integration of three algorithms ensures the increased likelihood of attaining the global optimal solution.The polished rod acceleration of the optimized structure is significantly reduced,and the dynamic characteristics of the up and down strokes are essentially symmetrical.Additionally,these three algorithms are also applied to the balance optimization of BPUs based on the measured dynamometer card.The calculation results demonstrate that the GWO-based optimization method exhibits excellent robustness in terms of structural optimization by enhancing the operational smoothness of the BPU,as well as in balance optimization by achieving energy conservation.By applying the optimization scheme proposed in this paper,the CYJW7-3-23HF type of BPU was designed,achieving a maximum polished rod acceleration of±0.675 m/s^(2) when operating at a stroke of 6 min^(−1).When deployed in two wells,the root-mean-square(RMS)torque was minimized,reaching values of 7.539 kN·m and 12.921 kN·m,respectively.The proposed design method not only contributes to the personalized customization but also improves the design efficiency of compound balanced BPUs.展开更多
The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for p...The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply.While complex machine learning models can achieve high accuracy,their“blackbox”nature severely limits the model interpretability.In this paper,we explore the trade-off between model accuracy and interpretability for the energy balancing market.Particularly,we take the example of forecasting manual frequency restoration reserve(mFRR)activation price in the balancing market using real market data from different energy price zones.We explore the interpretability of mFRR forecasting using two models:extreme gradient boosting(XGBoost)machine and explainable boosting machine(EBM).We also integrate the two models,and we benchmark all the models against a baseline naive model.Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability.Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price.Importantly,EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics.Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.展开更多
Glacier mass balance is a key indicator of glacier health and climate change sensitivity.Influencing factors include both climatic and nonclimatic elements,forming a complex set of drivers.There is a lack of quantitat...Glacier mass balance is a key indicator of glacier health and climate change sensitivity.Influencing factors include both climatic and nonclimatic elements,forming a complex set of drivers.There is a lack of quantitative analysis of these composite factors,particularly in climate-typical regions like the Tanggula Mountains on the central Tibetan Plateau.We collected data on various factors affecting glacier mass balance from 2000 to 2020,including climate variables,topographic variables,geometric parameters,and glacier dynamics.We utilized linear regression models,ensemble learning models,and Open Global Glacier Model(OGGM)to analyze glacier mass balance changes in the Tanggula Mountains.Results indicate that linear models explain 58%of the variance in glacier mass balance,with seasonal temperature and precipitation having significant impacts.Our findings show that ensemble learning models made the explanations 5.2%more accurate by including the impact of topographic and geometric factors such as the average glacier height,the slope of the glacier tongue,the speed of the ice flow,and the area of the glacier.Interpretable machine learning identified the spatial distribution of positive and negative impacts of these characteristics and the interaction between glacier topography and ice dynamics.Finally,we predicted the responses of glaciers of different sizes to future climate change based on the results of interpretable machine learning.It was found that relatively large glaciers(>1 km~2)are likely to persist until the end of this century under low emission scenarios,whereas small glaciers(<1 km~2)are expected to nearly disappear by 2080 under any emission scenario.Our research provides technical support for improving glacier change modeling and protection on the Tibetan Plateau.展开更多
In order to predict long-term flooding under extreme weather conditions in central Asia, an energy balance-based distributed snowmelt runoff model was developed and coupled with the Soil and Water Assessment Tool(SWAT...In order to predict long-term flooding under extreme weather conditions in central Asia, an energy balance-based distributed snowmelt runoff model was developed and coupled with the Soil and Water Assessment Tool(SWAT) model. The model was tested at the Juntanghu watershed on the northern slope of the Tian Shan Mountains, Xinjiang,China. We compared the performances of temperature-index method and energy balanced method in SWAT model by taking Juntanghu river basin as an application example(as the simulation experiment was conducted in Juntanghu River, we call the energy balanced method as SWAT-JTH). The results suggest that the SWAT snowmelt model had overall Nash-Sutcliffe efficiency(NSE) coefficients ranging from 0.61 to 0.85 while the physical based approach had NSE coefficients ranging from 0.58 to0.69. Overall, on monthly scale, the SWAT model provides better results than that from the SWAT-JTH model. However, results generated from both methods seem to be fairly close at a daily scale. Thestructure of the temperature-index method is simple and produces reasonable simulation results if the parameters are well within empirical ranges. Although the data requirement for the energy balance method in current observation is difficult to meet and the existence of uncertainty is associated with the experimental approaches of physical processes, the SWAT-JTH model still produced a reasonably high NSE. We conclude that using temperature-index methods to simulate the snowmelt process is sufficient, but the energy balance-based model is still a good choice to simulate extreme weather conditions especially when the required data input for the model is acquired.展开更多
In order to verify the feasibility and stability of a degree-day model on simulating the long time series of glacier mass balance, we apply a degree-day model to simulate the mass balance of Urumqi Glacier No. 1 for t...In order to verify the feasibility and stability of a degree-day model on simulating the long time series of glacier mass balance, we apply a degree-day model to simulate the mass balance of Urumqi Glacier No. 1 for the period 1987/1988-2007/2008 based on temperature and precipitation data from a nearby climate station. The model is calibrated by simulating point measurements of mass bal- ance, mass balance profiles, and mean specific mass balance during 1987/1988-1996/1997. The opti- mized parameters are obtained by using a least square method to make the model fit the measured mass balance through the model calibration. The model validation （1997/1998-2007/2008） indicates that the modeled results are in good agreement with the observations. The static mass balance sensitiv- ity of Urumqi Glacier No. 1 is analyzed by computing the mass balance of the glacier for a temperature increase of 1℃, with and without a 5% precipitation increase, and the values for the east branch are -0.80 and -0.87 m w.e. a-1℃-1, respectively, and for the west branch, the values are -0.68 and -0.74 m w.e. a-1℃-1, respectively. Moreover, the analysis of the parameter stability indicates that the parame- ters in the model determined from the current climate condition can be applied in the prediction of the future mass balance changes for the glacier and provide a reference for extending the model to other small glaciers in western China.展开更多
In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then...In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace （LF）. This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high-temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 95.93%. These efficiencies are calculated for all input elements as 8. 45% for C, 30.31% for Si, 46.36% for Mn, 30.64% for P, 41.96% for S, and 69.79% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.展开更多
The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire ...The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.展开更多
The organic matter degradation process during anaerobic co-digestion of municipal biomass waste （MBW） and waste-activated sludge （WAS） under different organic loading rates （OLRs） was investigated in bench-scale...The organic matter degradation process during anaerobic co-digestion of municipal biomass waste （MBW） and waste-activated sludge （WAS） under different organic loading rates （OLRs） was investigated in bench-scale and pilot-scale semi-continuous stirred tank reactors. To better understand the degradation process of MBW and WAS co-digestion and provide theoretical guidance for engineering application, anaerobic digestion model No. 1 was revised for the co-digestion of MBW and WAS. The results showed that the degradation of organic matter could be characterized into three different fractions, including readily hydrolyzable organics, easily degradable particulate organics, and recalcitrant particle organics. Hydrolysis was the rate-limiting step under lower OLRs, and methanogenesisis was the rate-limiting step for an OLR of 8.0 kg volatile solid （VS）/（m^3·day）. The hydrolytic parameters of carbohydrate, protein, and lipids were 0.104, 0.083, and 0.084 kg chemical oxygen demand （COD）/（kg COD·hr）, respectively, and the reaction rate parameters of lipid fermentation were 1 and 1.25 kg COD/（kg COD.hr） for OLRs of 4.0 and 6.0 kg VS/（m^3·day）. A revised model was used to simulate methane yield, and the results fit well with the experimental data. Material balance data were acquired based on the revised model, which showed that 58.50% of total COD was converted to methane.展开更多
The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based ...The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.展开更多
A nonlinear transformation of the Whitham-Broer-Kaup (WBK) model equations in the shallow water small-amplitude regime is derived by using a simplified homogeneous balance method. The WBK model equations are linearize...A nonlinear transformation of the Whitham-Broer-Kaup (WBK) model equations in the shallow water small-amplitude regime is derived by using a simplified homogeneous balance method. The WBK model equations are linearized under the nonlinear transformation. Various exact solutions of the WBK model equations are obtained via the nonlinear transformation with the aid of solutions for the linear equation.展开更多
The Miyun Reservoir is the most important water source for Beijing Municipality, the capital of China with a population of more than 12 million. In recent decades, the inflow to the reservoir has shown a decreasing tr...The Miyun Reservoir is the most important water source for Beijing Municipality, the capital of China with a population of more than 12 million. In recent decades, the inflow to the reservoir has shown a decreasing trend, which has seriously threatened water use in Beijing. In order to analyze the influents of land use and cover change (LUCC) upon inflow to Miyun Reservoir, terrain and land use information from remote sensing were utilized with a revised evapotranspiration estimation formula; a water loss model under conditions of human impacts was introduced; and a distributed monthly water balance model was established and applied to the Chaobai River Basin controlled by the Miyun Reservoir. The model simulation suggested that not only the impact of land cover change on evapotranspiration, but also the extra water loss caused by human activities, such as the water and soil conservation development projects should be considered. Although these development projects were of great benefit to human and ecological protection, they could reallocate water resources in time and space, and in a sense thereby influence the stream flow.展开更多
In this study,based on the Luo bubble coalescence model,a model correction factor C_e for pressures according to the literature experimental results was introduced in the bubble coalescence efficiency term.Then,a coup...In this study,based on the Luo bubble coalescence model,a model correction factor C_e for pressures according to the literature experimental results was introduced in the bubble coalescence efficiency term.Then,a coupled modified population balance model(PBM) with computational fluid dynamics(CFD) was used to simulate a high-pressure bubble column.The simulation results with and without C_e were compared with the experimental data.The modified CFD-PBM coupled model was used to investigate its applicability to broader experimental conditions.These results showed that the modified CFD-PBM coupled model can predict the hydrodynamic behaviors under various operating conditions.展开更多
The population balance modeling is regarded as a universally accepted mathematical framework for dynamic simulation of various particulate processes, such as crystallization, granulation and polymerization. This artic...The population balance modeling is regarded as a universally accepted mathematical framework for dynamic simulation of various particulate processes, such as crystallization, granulation and polymerization. This article is concerned with the application of the method of characteristics (MOC) for solving population balance models describing batch crystallization process. The growth and nucleation are considered as dominant phenomena, while the breakage and aggregation are neglected. The numerical solutions of such PBEs require high order accuracy due to the occurrence of steep moving fronts and narrow peaks in the solutions. The MOC has been found to be a very effective technique for resolving sharp discontinuities. Different case studies are carried out to analyze the accuracy of proposed algorithm. For validation, the results of MOC are compared with the available analytical solutions and the results of finite volume schemes. The results of MOC were found to be in good agreement with analytical solutions and superior than those obtained by finite volume schemes.展开更多
In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial n...In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.展开更多
Snow depth and sea ice thickness were observed applying an ice mass balance buoy(IMB)in the drifting ice station Tara during the International Polar Year in 2007.Detailed in situ observations on meteorological variabl...Snow depth and sea ice thickness were observed applying an ice mass balance buoy(IMB)in the drifting ice station Tara during the International Polar Year in 2007.Detailed in situ observations on meteorological variables and surface fluxes were taken during May to August.For this study,the operational analyses and short-term forecasts from two numerical weather prediction(NWP)models(ECMWF and HIRLAM)were extracted for the Tara drift trajectory.We compared the IMB,meteorological and surface flux observations against the NWP products,also applying a one-dimensional thermodynamic sea ice model(HIGHTSI)to calculate the snow and ice mass balance and its sensitivity to atmospheric forcing.The modelled snow depth time series,controlled by NWP-based precipitation,was in line with the observed one.HIGHTSI reproduced well the snowmelt onset,the progress of the melt,and the first date of snow-free conditions.HIGHTSI performed well also in the late August freezing season.Challenges remain to model the“false bottom”observed during the melting season.The evolution of the vertical temperature profiles in snow and ice was better simulated when the model was forced by in situ observations instead of NWP results.During the melting period,the nonlinear ice temperature profile was successfully modelled with both forcing options.During spring and the melting season,total sea ice mass balance was most sensitive to uncertainties in NWP results for the downward longwave radiation,followed by the downward shortwave radiation,air temperature,and wind speed.展开更多
Land-cover changes cause a loss of natural vegetation in many parts of the world. In the Xishuangbanna （西双版纳） district （Yunnan （云南） Province）, rubber plantations replace tropical rainforests covering alrea...Land-cover changes cause a loss of natural vegetation in many parts of the world. In the Xishuangbanna （西双版纳） district （Yunnan （云南） Province）, rubber plantations replace tropical rainforests covering already an area of about 10% of the study area （2007）. There, land-use allocation is mostly driven by economic considerations. Thus, local planning authorities need decision support for land-use planning issues, which integrate socio-economic and ecological aspects. Within the NabanFrame, an agro-economic, ecological and social model was applied, which, altogether, interacted with a land allocation model via defined interfaces. Effects on the water cycle, ecological conditions as well as socio-economic should be considered by integrating the spatially distributed rainfali-runoff and water balance model AKWA-M in the model setup.展开更多
Wave transmission and overtopping around nearshore breakwaters can have significant influence on the transmitted wave parameters,which affects wave conditions and sediment transportation and becomes the focus of desig...Wave transmission and overtopping around nearshore breakwaters can have significant influence on the transmitted wave parameters,which affects wave conditions and sediment transportation and becomes the focus of design in engineering.The objective of this paper is to present a simplified model to estimate these important wave parameters.This paper describes the incorporation of wave transmission and overtopping module into a wave model for multi-directional random wave transformation based on energy balance equation with the consideration of wave shoaling,refraction,diffraction,reflection and breaking.Wen's frequency spectrum and non-linear dispersion relation are also included in this model.The influence of wave parameters of transmitted waves through a smooth submerged breakwater has been considered in this model with an improved description of the transmitted wave spectrum of van der Meer et al.(2000) by Carevic et al.(2013).This improved wave model has been validated through available laboratory experiments.Then the verified model is applied to investigate the effect of wave transmission and overtopping on wave heights behind low-crested breakwaters in a project for nearshore area.Numerical calculations are carried out with and without consideration of the wave transmission and overtopping,and comparison of them indicates that there is a considerable difference in wave height and thus it is important to include wave transmission and overtopping in modelling nearshore wave field with the presence of low-crested breakwaters.Therefore,this model can provide a general estimate of the desired wave field parameters,which is adequate for engineers at the preliminary design stage of low-crested breakwaters.展开更多
基金supporteded by Natural Science Foundation of Shanghai(Grant No.22ZR1463900)State Key Laboratory of Mechanical System and Vibration(Grant No.MSV202318)the Fundamental Research Funds for the Central Universities(Grant No.22120220649).
文摘Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.
基金Supported in part by the National Science Foundation (US) under Grant CCR 0098275
文摘The Balanced Truncation Method (BTM) is applied to an even distributed RC interconnect case by using Wang's closed-forms of even distributed RC interconnect models. The results show that extremely high order RC interconnect can be high-accurately approximated by only third order balanced model. Related simulations are executed in both time domain and frequency domain. The results may be applied to VLSI interconnect model reduction and design.
文摘BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.
基金supported by the Key Laboratory of Petroleum and Natural Gas Equipment,Ministry of Education(No.OGE202303-08)Engineering Technology Research Center for Industrial Internet of Things and Intelligent Sensing,Hubei Province(No.KXZ 202203).
文摘The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimization problem.Currently,a comprehensive framework for the structural design and optimization of compound balanced BPUs is lacking.Therefore,this study proposes a novel structural design scheme for BPUs,aiming to meet the practical needs of designers and operators by sequentially optimizing both the dynamic characteristics and balance properties of the BPUs.A dynamic model of compound balanced BPU was established based on D'Alembert's principle.The constraints for structural dimensions were formulated based on the actual operational requirements and design experience with BPUs.To optimize the structure,three algorithms were employed:the particle swarm optimization(PSO)algorithm,the genetic algorithm(GA),and the gray wolf optimization(GWO)algorithm.Each newly generated individuals are regulated by constraints to ensure the rationality of the outcomes.Furthermore,the integration of three algorithms ensures the increased likelihood of attaining the global optimal solution.The polished rod acceleration of the optimized structure is significantly reduced,and the dynamic characteristics of the up and down strokes are essentially symmetrical.Additionally,these three algorithms are also applied to the balance optimization of BPUs based on the measured dynamometer card.The calculation results demonstrate that the GWO-based optimization method exhibits excellent robustness in terms of structural optimization by enhancing the operational smoothness of the BPU,as well as in balance optimization by achieving energy conservation.By applying the optimization scheme proposed in this paper,the CYJW7-3-23HF type of BPU was designed,achieving a maximum polished rod acceleration of±0.675 m/s^(2) when operating at a stroke of 6 min^(−1).When deployed in two wells,the root-mean-square(RMS)torque was minimized,reaching values of 7.539 kN·m and 12.921 kN·m,respectively.The proposed design method not only contributes to the personalized customization but also improves the design efficiency of compound balanced BPUs.
基金PriTEM project funded by UiO:Energy Convergence Environments
文摘The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply.While complex machine learning models can achieve high accuracy,their“blackbox”nature severely limits the model interpretability.In this paper,we explore the trade-off between model accuracy and interpretability for the energy balancing market.Particularly,we take the example of forecasting manual frequency restoration reserve(mFRR)activation price in the balancing market using real market data from different energy price zones.We explore the interpretability of mFRR forecasting using two models:extreme gradient boosting(XGBoost)machine and explainable boosting machine(EBM).We also integrate the two models,and we benchmark all the models against a baseline naive model.Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability.Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price.Importantly,EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics.Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.
基金funding from the National Key Research and Development Program of China(2023YFC3206300)the Gansu Provincial Science and Technology Program(22ZD6FA005)+2 种基金the Gansu Youth Science and Technology Fund(E4310103)the Gansu Postdoctoral Science Foundation(E339880112)the Tibet Science and Technology Program(XZ202301ZY0001G and XZ202401JD0007)。
文摘Glacier mass balance is a key indicator of glacier health and climate change sensitivity.Influencing factors include both climatic and nonclimatic elements,forming a complex set of drivers.There is a lack of quantitative analysis of these composite factors,particularly in climate-typical regions like the Tanggula Mountains on the central Tibetan Plateau.We collected data on various factors affecting glacier mass balance from 2000 to 2020,including climate variables,topographic variables,geometric parameters,and glacier dynamics.We utilized linear regression models,ensemble learning models,and Open Global Glacier Model(OGGM)to analyze glacier mass balance changes in the Tanggula Mountains.Results indicate that linear models explain 58%of the variance in glacier mass balance,with seasonal temperature and precipitation having significant impacts.Our findings show that ensemble learning models made the explanations 5.2%more accurate by including the impact of topographic and geometric factors such as the average glacier height,the slope of the glacier tongue,the speed of the ice flow,and the area of the glacier.Interpretable machine learning identified the spatial distribution of positive and negative impacts of these characteristics and the interaction between glacier topography and ice dynamics.Finally,we predicted the responses of glaciers of different sizes to future climate change based on the results of interpretable machine learning.It was found that relatively large glaciers(>1 km~2)are likely to persist until the end of this century under low emission scenarios,whereas small glaciers(<1 km~2)are expected to nearly disappear by 2080 under any emission scenario.Our research provides technical support for improving glacier change modeling and protection on the Tibetan Plateau.
基金financially supported by the Ministry of Water Resources (MWR) public sector research and special funds-the most stringent in arid zone water resources management key technologies (201301103)National Nature Science Foundation of China (NSFC) under Grant No. 41130641, 41201025+1 种基金Ministry of Education Key Laboratory of Eco-Oasis Open Topic-Moisture change in Central Asia and its influence on precipitation in Xinjang Province (XJDX0201-2013-07)the Tianshan Scholar Start-up Fund provided by Xinjiang University
文摘In order to predict long-term flooding under extreme weather conditions in central Asia, an energy balance-based distributed snowmelt runoff model was developed and coupled with the Soil and Water Assessment Tool(SWAT) model. The model was tested at the Juntanghu watershed on the northern slope of the Tian Shan Mountains, Xinjiang,China. We compared the performances of temperature-index method and energy balanced method in SWAT model by taking Juntanghu river basin as an application example(as the simulation experiment was conducted in Juntanghu River, we call the energy balanced method as SWAT-JTH). The results suggest that the SWAT snowmelt model had overall Nash-Sutcliffe efficiency(NSE) coefficients ranging from 0.61 to 0.85 while the physical based approach had NSE coefficients ranging from 0.58 to0.69. Overall, on monthly scale, the SWAT model provides better results than that from the SWAT-JTH model. However, results generated from both methods seem to be fairly close at a daily scale. Thestructure of the temperature-index method is simple and produces reasonable simulation results if the parameters are well within empirical ranges. Although the data requirement for the energy balance method in current observation is difficult to meet and the existence of uncertainty is associated with the experimental approaches of physical processes, the SWAT-JTH model still produced a reasonably high NSE. We conclude that using temperature-index methods to simulate the snowmelt process is sufficient, but the energy balance-based model is still a good choice to simulate extreme weather conditions especially when the required data input for the model is acquired.
基金supported by the Knowledge Innovation Project of the Chinese Academy of Sciences (No. KZCX2-EW-311)the National Basic Research Program of China (No. 2007CB411501)the National Natural Science Foundation of China (Nos. 1141001040, J0930003/J0109)
文摘In order to verify the feasibility and stability of a degree-day model on simulating the long time series of glacier mass balance, we apply a degree-day model to simulate the mass balance of Urumqi Glacier No. 1 for the period 1987/1988-2007/2008 based on temperature and precipitation data from a nearby climate station. The model is calibrated by simulating point measurements of mass bal- ance, mass balance profiles, and mean specific mass balance during 1987/1988-1996/1997. The opti- mized parameters are obtained by using a least square method to make the model fit the measured mass balance through the model calibration. The model validation （1997/1998-2007/2008） indicates that the modeled results are in good agreement with the observations. The static mass balance sensitiv- ity of Urumqi Glacier No. 1 is analyzed by computing the mass balance of the glacier for a temperature increase of 1℃, with and without a 5% precipitation increase, and the values for the east branch are -0.80 and -0.87 m w.e. a-1℃-1, respectively, and for the west branch, the values are -0.68 and -0.74 m w.e. a-1℃-1, respectively. Moreover, the analysis of the parameter stability indicates that the parame- ters in the model determined from the current climate condition can be applied in the prediction of the future mass balance changes for the glacier and provide a reference for extending the model to other small glaciers in western China.
文摘In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace （LF）. This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high-temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 95.93%. These efficiencies are calculated for all input elements as 8. 45% for C, 30.31% for Si, 46.36% for Mn, 30.64% for P, 41.96% for S, and 69.79% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.
基金supported by the National Key Research and Development Program of China(No.2018YFC0214102)。
文摘The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.
基金supported by the Ministry of Science and Technology of China (No.2010DFA22770)the National Science and Technology Support Program of China (No.2010BAC66B04)the Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province (No.AE201003)
文摘The organic matter degradation process during anaerobic co-digestion of municipal biomass waste （MBW） and waste-activated sludge （WAS） under different organic loading rates （OLRs） was investigated in bench-scale and pilot-scale semi-continuous stirred tank reactors. To better understand the degradation process of MBW and WAS co-digestion and provide theoretical guidance for engineering application, anaerobic digestion model No. 1 was revised for the co-digestion of MBW and WAS. The results showed that the degradation of organic matter could be characterized into three different fractions, including readily hydrolyzable organics, easily degradable particulate organics, and recalcitrant particle organics. Hydrolysis was the rate-limiting step under lower OLRs, and methanogenesisis was the rate-limiting step for an OLR of 8.0 kg volatile solid （VS）/（m^3·day）. The hydrolytic parameters of carbohydrate, protein, and lipids were 0.104, 0.083, and 0.084 kg chemical oxygen demand （COD）/（kg COD·hr）, respectively, and the reaction rate parameters of lipid fermentation were 1 and 1.25 kg COD/（kg COD.hr） for OLRs of 4.0 and 6.0 kg VS/（m^3·day）. A revised model was used to simulate methane yield, and the results fit well with the experimental data. Material balance data were acquired based on the revised model, which showed that 58.50% of total COD was converted to methane.
基金financially supported by the National Natural Science Foundation of China (Nos.51974023 and52374321)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,China (No.41620007)。
文摘The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.
文摘A nonlinear transformation of the Whitham-Broer-Kaup (WBK) model equations in the shallow water small-amplitude regime is derived by using a simplified homogeneous balance method. The WBK model equations are linearized under the nonlinear transformation. Various exact solutions of the WBK model equations are obtained via the nonlinear transformation with the aid of solutions for the linear equation.
基金supported by the Knowledge Innovation Key Project of Chinese Academy of Sciences (Nos. CX10G-E01-08 andKZCX2-SW-317) and the National Natural Science Foundation of China (No. 50279049)
文摘The Miyun Reservoir is the most important water source for Beijing Municipality, the capital of China with a population of more than 12 million. In recent decades, the inflow to the reservoir has shown a decreasing trend, which has seriously threatened water use in Beijing. In order to analyze the influents of land use and cover change (LUCC) upon inflow to Miyun Reservoir, terrain and land use information from remote sensing were utilized with a revised evapotranspiration estimation formula; a water loss model under conditions of human impacts was introduced; and a distributed monthly water balance model was established and applied to the Chaobai River Basin controlled by the Miyun Reservoir. The model simulation suggested that not only the impact of land cover change on evapotranspiration, but also the extra water loss caused by human activities, such as the water and soil conservation development projects should be considered. Although these development projects were of great benefit to human and ecological protection, they could reallocate water resources in time and space, and in a sense thereby influence the stream flow.
基金Supported by the National Natural Science Foundation of China(91634101)The Project of Construction of Innovative TeamsTeacher Career Development for Universities and Colleges under Beijing Municipality(IDHT20180508)
文摘In this study,based on the Luo bubble coalescence model,a model correction factor C_e for pressures according to the literature experimental results was introduced in the bubble coalescence efficiency term.Then,a coupled modified population balance model(PBM) with computational fluid dynamics(CFD) was used to simulate a high-pressure bubble column.The simulation results with and without C_e were compared with the experimental data.The modified CFD-PBM coupled model was used to investigate its applicability to broader experimental conditions.These results showed that the modified CFD-PBM coupled model can predict the hydrodynamic behaviors under various operating conditions.
文摘The population balance modeling is regarded as a universally accepted mathematical framework for dynamic simulation of various particulate processes, such as crystallization, granulation and polymerization. This article is concerned with the application of the method of characteristics (MOC) for solving population balance models describing batch crystallization process. The growth and nucleation are considered as dominant phenomena, while the breakage and aggregation are neglected. The numerical solutions of such PBEs require high order accuracy due to the occurrence of steep moving fronts and narrow peaks in the solutions. The MOC has been found to be a very effective technique for resolving sharp discontinuities. Different case studies are carried out to analyze the accuracy of proposed algorithm. For validation, the results of MOC are compared with the available analytical solutions and the results of finite volume schemes. The results of MOC were found to be in good agreement with analytical solutions and superior than those obtained by finite volume schemes.
基金Project(2013CB035402) supported by the National Basic Research Program of ChinaProjects(51105048,51209028) supported by the National Natural Science Foundation of China
文摘In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.
基金This study was initialized during DAMOCLES project(Grant no.18509)which was funded by the 6th Framework Programme of the European Commission+2 种基金The initial data analysis was funded by the Research Council of Norway’s AMORA project(Grant no.#193592)The modelling work has been supported by the Academy of Finland(Contract 317999)The finalization of this work was supported by the European Union’s Horizon 2020 research and innovation programme(Grant no.727890–INTAROS).
文摘Snow depth and sea ice thickness were observed applying an ice mass balance buoy(IMB)in the drifting ice station Tara during the International Polar Year in 2007.Detailed in situ observations on meteorological variables and surface fluxes were taken during May to August.For this study,the operational analyses and short-term forecasts from two numerical weather prediction(NWP)models(ECMWF and HIRLAM)were extracted for the Tara drift trajectory.We compared the IMB,meteorological and surface flux observations against the NWP products,also applying a one-dimensional thermodynamic sea ice model(HIGHTSI)to calculate the snow and ice mass balance and its sensitivity to atmospheric forcing.The modelled snow depth time series,controlled by NWP-based precipitation,was in line with the observed one.HIGHTSI reproduced well the snowmelt onset,the progress of the melt,and the first date of snow-free conditions.HIGHTSI performed well also in the late August freezing season.Challenges remain to model the“false bottom”observed during the melting season.The evolution of the vertical temperature profiles in snow and ice was better simulated when the model was forced by in situ observations instead of NWP results.During the melting period,the nonlinear ice temperature profile was successfully modelled with both forcing options.During spring and the melting season,total sea ice mass balance was most sensitive to uncertainties in NWP results for the downward longwave radiation,followed by the downward shortwave radiation,air temperature,and wind speed.
基金supported by the German Federal Ministry of Education and Science (BMBF) (No. 0330797A)
文摘Land-cover changes cause a loss of natural vegetation in many parts of the world. In the Xishuangbanna （西双版纳） district （Yunnan （云南） Province）, rubber plantations replace tropical rainforests covering already an area of about 10% of the study area （2007）. There, land-use allocation is mostly driven by economic considerations. Thus, local planning authorities need decision support for land-use planning issues, which integrate socio-economic and ecological aspects. Within the NabanFrame, an agro-economic, ecological and social model was applied, which, altogether, interacted with a land allocation model via defined interfaces. Effects on the water cycle, ecological conditions as well as socio-economic should be considered by integrating the spatially distributed rainfali-runoff and water balance model AKWA-M in the model setup.
基金supported by the NSFC-Shandong Joint Fund Project(No.U1706226)Research Award Fund for Outstanding Young and Middle-aged Scientists of Shandong Province(No.ZR2016EEB06)the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents
文摘Wave transmission and overtopping around nearshore breakwaters can have significant influence on the transmitted wave parameters,which affects wave conditions and sediment transportation and becomes the focus of design in engineering.The objective of this paper is to present a simplified model to estimate these important wave parameters.This paper describes the incorporation of wave transmission and overtopping module into a wave model for multi-directional random wave transformation based on energy balance equation with the consideration of wave shoaling,refraction,diffraction,reflection and breaking.Wen's frequency spectrum and non-linear dispersion relation are also included in this model.The influence of wave parameters of transmitted waves through a smooth submerged breakwater has been considered in this model with an improved description of the transmitted wave spectrum of van der Meer et al.(2000) by Carevic et al.(2013).This improved wave model has been validated through available laboratory experiments.Then the verified model is applied to investigate the effect of wave transmission and overtopping on wave heights behind low-crested breakwaters in a project for nearshore area.Numerical calculations are carried out with and without consideration of the wave transmission and overtopping,and comparison of them indicates that there is a considerable difference in wave height and thus it is important to include wave transmission and overtopping in modelling nearshore wave field with the presence of low-crested breakwaters.Therefore,this model can provide a general estimate of the desired wave field parameters,which is adequate for engineers at the preliminary design stage of low-crested breakwaters.
