Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century.Vacuum freeze-drying(VFD),though invented over a hundred years ago,remains one of the most ad...Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century.Vacuum freeze-drying(VFD),though invented over a hundred years ago,remains one of the most advanced drying techniques,known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state.The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods.However,the qualitative aspects of the dried product are not predictable.In this context,the present study aims to create a deep neural framework(DNF)that predicts the performance of a Vacuum Freeze Drying(VFD)system for kiwifruit,based on its morphology and nutritional value under varying conditions.This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network(GAN)to create diverse,unlabeled datasets.The framework is optimized using Gaussian Process(GP)for hyper-parameter tuning,focusing on minimizing errors like mean square error(MSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).The maximum MSE of 1.243 is found in the prediction of rehydration rate,followed by color(0.725),energy consumption(0.426),moisture content(0.379),texture(0.320),sensory(0.250),and Brix(0.215),respectively.The maximum MAE and MAPE values are recorded 0.833 and 32.99%while the minimum is observed 0.368 and 7.019%in the case of rehydration rate and Brix,respectively.Overall,the R2 value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.展开更多
The tidal power has the potential to play a vital role in a sustainable energy future.The main objective of this paper is to investigate the performance and fatigue life of tidal current turbine(TCT)using fluid struct...The tidal power has the potential to play a vital role in a sustainable energy future.The main objective of this paper is to investigate the performance and fatigue life of tidal current turbine(TCT)using fluid structure interaction(FSI)modeling.The performance of TCT was predicted using Ansys CFX.The performance curve,pressure distribution on the blade,and velocity streamline were visualized for eight repetitive analyses at different tip speed ratio.The hydrodynamic load calculated from CFD analysis was transferred to FEA model for investigation of the structural response of TCT.Modal analysis was performed to examine the mode shapes and natural frequencies of TCT.The fatigue analysis were performed and number of cycles and safety factor at different equivalent alternating stresses were investigated.The results of the simulation confirm that the turbine has a maximum value of the coefficient of performance atλ=5,the turbine operating frequency is not close to its natural frequency,and it is safe under the applied fatigue loads with a high factor of safety.展开更多
基金support from the National Science and Technology Council Taiwan under the Contract No.NSTC 112-2221-E-027-054-MY2.
文摘Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century.Vacuum freeze-drying(VFD),though invented over a hundred years ago,remains one of the most advanced drying techniques,known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state.The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods.However,the qualitative aspects of the dried product are not predictable.In this context,the present study aims to create a deep neural framework(DNF)that predicts the performance of a Vacuum Freeze Drying(VFD)system for kiwifruit,based on its morphology and nutritional value under varying conditions.This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network(GAN)to create diverse,unlabeled datasets.The framework is optimized using Gaussian Process(GP)for hyper-parameter tuning,focusing on minimizing errors like mean square error(MSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).The maximum MSE of 1.243 is found in the prediction of rehydration rate,followed by color(0.725),energy consumption(0.426),moisture content(0.379),texture(0.320),sensory(0.250),and Brix(0.215),respectively.The maximum MAE and MAPE values are recorded 0.833 and 32.99%while the minimum is observed 0.368 and 7.019%in the case of rehydration rate and Brix,respectively.Overall,the R2 value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.
文摘The tidal power has the potential to play a vital role in a sustainable energy future.The main objective of this paper is to investigate the performance and fatigue life of tidal current turbine(TCT)using fluid structure interaction(FSI)modeling.The performance of TCT was predicted using Ansys CFX.The performance curve,pressure distribution on the blade,and velocity streamline were visualized for eight repetitive analyses at different tip speed ratio.The hydrodynamic load calculated from CFD analysis was transferred to FEA model for investigation of the structural response of TCT.Modal analysis was performed to examine the mode shapes and natural frequencies of TCT.The fatigue analysis were performed and number of cycles and safety factor at different equivalent alternating stresses were investigated.The results of the simulation confirm that the turbine has a maximum value of the coefficient of performance atλ=5,the turbine operating frequency is not close to its natural frequency,and it is safe under the applied fatigue loads with a high factor of safety.
