In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle ...In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.展开更多
Al_(0.2) CrFeNiCo and Al_(0.2) CrFeNiCu high entropy alloys were deposited with high velocity oxygen fuel(HVOF)on 316 L substrate.Later,a laser re-melting(LR)process was applied to enhancing the coating microstructure...Al_(0.2) CrFeNiCo and Al_(0.2) CrFeNiCu high entropy alloys were deposited with high velocity oxygen fuel(HVOF)on 316 L substrate.Later,a laser re-melting(LR)process was applied to enhancing the coating microstructure.LR process effects on dry sliding wear and oxidation behaviors were investigated.The mixture of powders with free elements led to the formation of inner oxides in HVOF coatings.The oxide and porosity were eliminated using LR.After LR,FCC was the dominant phase in both alloys,while BCC,sigma and Cr2 O3 phases were observed in Al_(0.2) CrFeNiCo alloy.The hardnesses of the Al_(0.2) CrFeNiCo and Al_(0.2) CrFeNiCu coatings after HVOF were HV 591 and HV 361,respectively.After LR,the hardnesses decreased to HV 259 and HV 270,respectively.Although HVOF coatings were most affected by increased load,they showed the highest wear resistance compared to other samples.The lowest wear resistance could be seen in the substrate.After the oxidation tests,HVOF coating layer was completely oxidized and also,the coating layer was delaminated from the substrate after 50 h oxidation due to its porous structure.LR coatings exhibited better oxidation performance.Al_(0.2) CrFeNiCo was dominantly composed of Cr2 O3,exhibiting a slower-growing tendency at the end of the oxidation tests,while Al_(0.2) CrFeNiCu was composed of spinel phases.展开更多
Non-erodible elements such as stones and vegetation are key to controlling wind erosion and dust emission in drylands.Stony deserts are widely distributed in the Gobi Desert,but the effect of stones on wind erosion an...Non-erodible elements such as stones and vegetation are key to controlling wind erosion and dust emission in drylands.Stony deserts are widely distributed in the Gobi Desert,but the effect of stones on wind erosion and dust emission have not been well studied,except under artificial conditions.In this study,we evaluated the effect of stones on wind erosion and dust emission by measuring the sand saltation threshold in a stony desert in Tsogt-Ovoo in the Gobi Desert,Mongolia,under natural surface conditions during sand and dust storms.We quantified the amount of stones by measuring the roughness density,and determined the threshold friction velocity for sand saltation by measuring wind speed and sand saltation count.Our results showed that the threshold friction velocity increased with the roughness density of stones.In the northern part of the study area,where neither a surface crust nor vegetation was observed,the roughness density of stones was 0.000 in a topographic depression(TD),0.050 on a northern slope(N.SL),and 0.160 on the northern mountain(N.MT).The mean threshold friction velocity values were 0.23,0.41,and 0.57 m/s at the TD,N.SL,and N.MT sites,respectively.In the southern part of the study area,the roughness density values of stones were 0.000 and 0.070-0.320 at the TD and southern slope sites,respectively,and the mean threshold friction velocities were 0.23 and 0.45-0.71 m/s,respectively.We further compared the observed threshold friction velocities with simulated threshold friction velocities using Raupach's theoretical roughness correction and the measured roughness density values,and found that Raupach's roughness correction worked very well in the simulation of threshold friction velocity in the stony desert.This means that the results of our stone measurement can be applied to a numerical dust model.展开更多
To mitigate the damage caused by debris flows resulting from heavy precipitation and to aid in evacuation plan preparation, areas at risk should be mapped on a scale appropriate for affected individuals and communitie...To mitigate the damage caused by debris flows resulting from heavy precipitation and to aid in evacuation plan preparation, areas at risk should be mapped on a scale appropriate for affected individuals and communities. We tested the effectiveness of simply identifying debris-flow hazards through automated derivation of surface curvatures using LiDAR digital elevation models. We achieved useful correspondence between plan curvatures and areas of existing debris-flow damage in two localities in Japan using the analysis of digital elevation models(DEMs). We found that plan curvatures derived from 10 m DEMs may be useful to indicate areas that are susceptible to debris flow in mountainous areas. In residential areas located on gentle sloping debris flow fans, the greatest damage to houses was found to be located in the elongated depressions that are connected to mountain stream valleys. Plan curvaturederived from 5 m DEM was the most sensitive indicators for susceptibility to debris flows.展开更多
This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base st...This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base station(BS)adapts the number of antennas to the daily load profile(DLP).This paper takes into consideration user location distribution(ULD)variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system.ULD variation is modeled by dividing the cell into two coverage areas with different user densities:boundary focused(BF)and center focused(CF)ULD.All cells are assumed identical in terms of BS configurations,cell loading,and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load.Together with energy efficiency(EE)we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size,available bandwidth,output power level of the BS,and maximum output power of the power amplifier(PA)at different cell loading.We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business,residential,street,and highway areas.展开更多
Today there is a big interest in reducing the maintenance costs and in increasing the reliability of machines in continuous operation. Therefore, maintenance on condition is used. State-of-the-art is a trend analysis ...Today there is a big interest in reducing the maintenance costs and in increasing the reliability of machines in continuous operation. Therefore, maintenance on condition is used. State-of-the-art is a trend analysis and a fault prediction made only based on sensor signals and stochastic methods. The identification possibilities of this technique are limited. A new concept for model-based monitoring has been developed for more detailed fault identification. The developed concept determines the condition of a machine after the occurrence of a fault. The concept is based on a simulation including various faults and an optimization tool. The development of a cost function and the optimization is one of the challenges of such a concept. Using an AMB rotor system with an auxiliary bearing, the new concept of model-based monitoring is investigated using experiments and the optimization is discussed in this paper.展开更多
文摘In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.
基金financially supported by Scientific Research Funds of Bart?n University(No.2019-FEN-A-012,2019-FEN-A-013)。
文摘Al_(0.2) CrFeNiCo and Al_(0.2) CrFeNiCu high entropy alloys were deposited with high velocity oxygen fuel(HVOF)on 316 L substrate.Later,a laser re-melting(LR)process was applied to enhancing the coating microstructure.LR process effects on dry sliding wear and oxidation behaviors were investigated.The mixture of powders with free elements led to the formation of inner oxides in HVOF coatings.The oxide and porosity were eliminated using LR.After LR,FCC was the dominant phase in both alloys,while BCC,sigma and Cr2 O3 phases were observed in Al_(0.2) CrFeNiCo alloy.The hardnesses of the Al_(0.2) CrFeNiCo and Al_(0.2) CrFeNiCu coatings after HVOF were HV 591 and HV 361,respectively.After LR,the hardnesses decreased to HV 259 and HV 270,respectively.Although HVOF coatings were most affected by increased load,they showed the highest wear resistance compared to other samples.The lowest wear resistance could be seen in the substrate.After the oxidation tests,HVOF coating layer was completely oxidized and also,the coating layer was delaminated from the substrate after 50 h oxidation due to its porous structure.LR coatings exhibited better oxidation performance.Al_(0.2) CrFeNiCo was dominantly composed of Cr2 O3,exhibiting a slower-growing tendency at the end of the oxidation tests,while Al_(0.2) CrFeNiCu was composed of spinel phases.
基金This study was supported by the Arid Land Research Center's Project(Impacts of Climate Change on Drylands:Assessment and Adaptation,funded by the Japan's Ministry of Education,Culture,Sports,Science,and Technology)the Grants-in-Aid for Scientific Research(JSPS KAKENHI)(15H05115,17H01616,16H02712,and 25220201)+1 种基金the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency(JPMEERF20205001)This study was funded by the Joint Research Program of Arid Land Research Center,Tottori University(31C2003 and 31C2012).
文摘Non-erodible elements such as stones and vegetation are key to controlling wind erosion and dust emission in drylands.Stony deserts are widely distributed in the Gobi Desert,but the effect of stones on wind erosion and dust emission have not been well studied,except under artificial conditions.In this study,we evaluated the effect of stones on wind erosion and dust emission by measuring the sand saltation threshold in a stony desert in Tsogt-Ovoo in the Gobi Desert,Mongolia,under natural surface conditions during sand and dust storms.We quantified the amount of stones by measuring the roughness density,and determined the threshold friction velocity for sand saltation by measuring wind speed and sand saltation count.Our results showed that the threshold friction velocity increased with the roughness density of stones.In the northern part of the study area,where neither a surface crust nor vegetation was observed,the roughness density of stones was 0.000 in a topographic depression(TD),0.050 on a northern slope(N.SL),and 0.160 on the northern mountain(N.MT).The mean threshold friction velocity values were 0.23,0.41,and 0.57 m/s at the TD,N.SL,and N.MT sites,respectively.In the southern part of the study area,the roughness density values of stones were 0.000 and 0.070-0.320 at the TD and southern slope sites,respectively,and the mean threshold friction velocities were 0.23 and 0.45-0.71 m/s,respectively.We further compared the observed threshold friction velocities with simulated threshold friction velocities using Raupach's theoretical roughness correction and the measured roughness density values,and found that Raupach's roughness correction worked very well in the simulation of threshold friction velocity in the stony desert.This means that the results of our stone measurement can be applied to a numerical dust model.
基金supported by the Crisis Management division of Toho village, and JSPS KAKENHI Grant Number (18K04660)
文摘To mitigate the damage caused by debris flows resulting from heavy precipitation and to aid in evacuation plan preparation, areas at risk should be mapped on a scale appropriate for affected individuals and communities. We tested the effectiveness of simply identifying debris-flow hazards through automated derivation of surface curvatures using LiDAR digital elevation models. We achieved useful correspondence between plan curvatures and areas of existing debris-flow damage in two localities in Japan using the analysis of digital elevation models(DEMs). We found that plan curvatures derived from 10 m DEMs may be useful to indicate areas that are susceptible to debris flow in mountainous areas. In residential areas located on gentle sloping debris flow fans, the greatest damage to houses was found to be located in the elongated depressions that are connected to mountain stream valleys. Plan curvaturederived from 5 m DEM was the most sensitive indicators for susceptibility to debris flows.
文摘This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base station(BS)adapts the number of antennas to the daily load profile(DLP).This paper takes into consideration user location distribution(ULD)variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system.ULD variation is modeled by dividing the cell into two coverage areas with different user densities:boundary focused(BF)and center focused(CF)ULD.All cells are assumed identical in terms of BS configurations,cell loading,and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load.Together with energy efficiency(EE)we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size,available bandwidth,output power level of the BS,and maximum output power of the power amplifier(PA)at different cell loading.We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business,residential,street,and highway areas.
基金supported by a fellowship within the Postdoc-Programme of the German Academic Exchange Service (DAAD)
文摘Today there is a big interest in reducing the maintenance costs and in increasing the reliability of machines in continuous operation. Therefore, maintenance on condition is used. State-of-the-art is a trend analysis and a fault prediction made only based on sensor signals and stochastic methods. The identification possibilities of this technique are limited. A new concept for model-based monitoring has been developed for more detailed fault identification. The developed concept determines the condition of a machine after the occurrence of a fault. The concept is based on a simulation including various faults and an optimization tool. The development of a cost function and the optimization is one of the challenges of such a concept. Using an AMB rotor system with an auxiliary bearing, the new concept of model-based monitoring is investigated using experiments and the optimization is discussed in this paper.
