Silica aerogel composites have promising applications in high-temperature heat storage insulation.However,the impact of high temperatures and moisture on their insulation performance remains unclear.To reveal the infl...Silica aerogel composites have promising applications in high-temperature heat storage insulation.However,the impact of high temperatures and moisture on their insulation performance remains unclear.To reveal the influences of high temperature and moisture absorption property on the heat transfer of silica aerogel composites,an experimental and numerical study was conducted to explore the micromorphology,thermophysical parameters,moisture absorption characteristics,and temperature response.The service temperature limit of the silica aerogel composite has been clarified.Measurements have conducted for the thermal conductivity,specific heat capacity,thermal diffusivity,specific surface area,density,porosity,and pore size distribution of the heated silica aerogel composite(at temperatures of 600,800,1,000,1,100 and 1,200℃).The moisture absorption characteristic curve at 20℃ has been obtained.Thermal testing of silica aerogel composites under varying heating temperatures and moisture content has been completed.Additionally,a numerical method has been developed to calculate the temperature curve of moist silica aerogel composites.The insulation performance of silica aerogel composite with varying moisture contents depends on the game between thermal conductivity and latent heat.Compared with the negative effect of the moisture content on insulation performance,the positive influence of moisture evaporation and heat absorption is dominant in situations involving temperatures higher than the phase transition temperature.展开更多
Fault detection and diagnosis(FDD)of heating,ventilation,and air conditioning(HVAC)systems can help to improve the energy saving in building energy systems.However,most data-driven trained FDD models have limited gene...Fault detection and diagnosis(FDD)of heating,ventilation,and air conditioning(HVAC)systems can help to improve the energy saving in building energy systems.However,most data-driven trained FDD models have limited generalizability and can only be applied to specific systems.The diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of FDD.Transfer learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited data.In this study,a novel transfer learning approach for HVAC FDD is proposed.First,the transformer model is modified to incorporate one encoder and two decoders connected,enabling two outputs.This modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer learning.It has effective performance in complex systems and achieves an accuracy of 91.38%for a system with 16 faults and multiple fault severity levels.Second,the adapter-based parameter-efficient transfer learning method,facilitating the transfer of trained models simply by inserting small adapter modules,is investigated as the transfer learning strategy.Results demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable parameters.It works well with limited data amount in target domain.Furthermore,the findings highlight the significance of adapters positioned near the bottom and top layers,emphasizing their critical role in facilitating successful transfer learning.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.:52006168 and 12102056).
文摘Silica aerogel composites have promising applications in high-temperature heat storage insulation.However,the impact of high temperatures and moisture on their insulation performance remains unclear.To reveal the influences of high temperature and moisture absorption property on the heat transfer of silica aerogel composites,an experimental and numerical study was conducted to explore the micromorphology,thermophysical parameters,moisture absorption characteristics,and temperature response.The service temperature limit of the silica aerogel composite has been clarified.Measurements have conducted for the thermal conductivity,specific heat capacity,thermal diffusivity,specific surface area,density,porosity,and pore size distribution of the heated silica aerogel composite(at temperatures of 600,800,1,000,1,100 and 1,200℃).The moisture absorption characteristic curve at 20℃ has been obtained.Thermal testing of silica aerogel composites under varying heating temperatures and moisture content has been completed.Additionally,a numerical method has been developed to calculate the temperature curve of moist silica aerogel composites.The insulation performance of silica aerogel composite with varying moisture contents depends on the game between thermal conductivity and latent heat.Compared with the negative effect of the moisture content on insulation performance,the positive influence of moisture evaporation and heat absorption is dominant in situations involving temperatures higher than the phase transition temperature.
基金supported by the National Natural Science Foundation of China(Grant Nos.:52293413 and 52076161).
文摘Fault detection and diagnosis(FDD)of heating,ventilation,and air conditioning(HVAC)systems can help to improve the energy saving in building energy systems.However,most data-driven trained FDD models have limited generalizability and can only be applied to specific systems.The diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of FDD.Transfer learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited data.In this study,a novel transfer learning approach for HVAC FDD is proposed.First,the transformer model is modified to incorporate one encoder and two decoders connected,enabling two outputs.This modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer learning.It has effective performance in complex systems and achieves an accuracy of 91.38%for a system with 16 faults and multiple fault severity levels.Second,the adapter-based parameter-efficient transfer learning method,facilitating the transfer of trained models simply by inserting small adapter modules,is investigated as the transfer learning strategy.Results demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable parameters.It works well with limited data amount in target domain.Furthermore,the findings highlight the significance of adapters positioned near the bottom and top layers,emphasizing their critical role in facilitating successful transfer learning.
