The development of deep learning has inspired some new methods to solve the 3D reconstruction problem for Tomographic Particle Image Velocimetry (Tomo-PIV). However, the supervised learning method requires a large num...The development of deep learning has inspired some new methods to solve the 3D reconstruction problem for Tomographic Particle Image Velocimetry (Tomo-PIV). However, the supervised learning method requires a large number of data with ground truth as training information, which is very difficult to gather from experiments. Although synthetic datasets can be used as alternatives, they are still not exactly the same with the real-world experimental data. In this paper, an Unsupervised Reconstruction Technique based on U-net (UnRTU) is proposed to reconstruct volume particle distribution explicitly. Instead of using ground truth data, a projection function is used as an unsupervised loss function for network training to reconstruct particle distribution. The UnRTU was compared with some traditional algebraic reconstruction algorithms and supervised learning method using synthetic data under different particle density and noise level. The results indicate that UnRTU outperforms these traditional approaches in both reconstruction quality and noise robustness, and is comparable to the supervised learning methods AI-PR. For experimental tests, particles dispersed in cured epoxy resin are moved by an electric rail with a certain speed to obtain the ground truth data of particle velocity. Compared with other algorithms, the reconstructed particle distribution by UnRTU has the best reconstruction fidelity. And the accuracy of the 3D velocity field estimated by UnRTU is 12.9% higher than that from the traditional MLOS-MART algorithm. It demonstrates significant potential and advantages for UnRTU in 3D reconstruction of particle distribution. Finally, UnRTU was successfully applied to the high-speed planar cascade airflow field, demonstrating its applicability for measuring complex fluid flow fields at higher particle density.展开更多
The core ecosystem functioning(e.g.trophic transfer efficiency)is at risk of being disrupted by the growing mismatch between nutrient content of primary producers and nutrient demand of grazing consumers.Ecological st...The core ecosystem functioning(e.g.trophic transfer efficiency)is at risk of being disrupted by the growing mismatch between nutrient content of primary producers and nutrient demand of grazing consumers.Ecological stoichiometry provides a conceptual framework that explains this trophic interaction using C,N and P elemental composition across trophic levels.In light of ongoing climate change and eutrophication,previous studies have raised concerns regarding the growing stoichiometric mismatch between phytoplankton and zooplankton,given the stoichiometric plasticity of phytoplankton.However,there is currently little conclusive evidence on the stoichiometric mismatch from a dual perspective of phytoplankton and zooplankton.To address this,we conducted a mesocosm experiment to investigate the separate and combined effects of climate warming(a constant increase of t3.5C plus heat waves)and eutrophication(nutrient addition)on stoichiometric mismatch between phytoplankton and zooplankton by examining stoichiometric changes in both communities.We observed a growing trend in stoichiometric mismatches when warming or nutrient addition acted individually,which was mediated by the increase in nutrient demand(N,P elements)of zooplankton growth.However,when these stressors acted jointly,the mismatches were reversed.This could be because climate warming and eutrophication combined would lead to changes in species composition,which accordingly reshaped the stoichiometric composition at the community level.These results illustrate the need of stoichiometric mismatches for understanding the implication of global change on trophic interactions and ecosystem functioning,requiring consideration not only of cross-trophic levels but also of compositional changes within communities.展开更多
基金the foundation of National Natural Science Foundation of China(No.52376163)National Key Laboratory of Science and Technology on Aerodynamic Design and Research(No.614220121050327).
文摘The development of deep learning has inspired some new methods to solve the 3D reconstruction problem for Tomographic Particle Image Velocimetry (Tomo-PIV). However, the supervised learning method requires a large number of data with ground truth as training information, which is very difficult to gather from experiments. Although synthetic datasets can be used as alternatives, they are still not exactly the same with the real-world experimental data. In this paper, an Unsupervised Reconstruction Technique based on U-net (UnRTU) is proposed to reconstruct volume particle distribution explicitly. Instead of using ground truth data, a projection function is used as an unsupervised loss function for network training to reconstruct particle distribution. The UnRTU was compared with some traditional algebraic reconstruction algorithms and supervised learning method using synthetic data under different particle density and noise level. The results indicate that UnRTU outperforms these traditional approaches in both reconstruction quality and noise robustness, and is comparable to the supervised learning methods AI-PR. For experimental tests, particles dispersed in cured epoxy resin are moved by an electric rail with a certain speed to obtain the ground truth data of particle velocity. Compared with other algorithms, the reconstructed particle distribution by UnRTU has the best reconstruction fidelity. And the accuracy of the 3D velocity field estimated by UnRTU is 12.9% higher than that from the traditional MLOS-MART algorithm. It demonstrates significant potential and advantages for UnRTU in 3D reconstruction of particle distribution. Finally, UnRTU was successfully applied to the high-speed planar cascade airflow field, demonstrating its applicability for measuring complex fluid flow fields at higher particle density.
基金supported by the National Natural Science Foundation of China[grant numbers 32171515,31800389]the Hundred-Talent Program of the Chinese Academy of Sciencesthe International Cooperation Project of the Chinese Academy of Sciences[grant numbers 152342KYSB20190025].
文摘The core ecosystem functioning(e.g.trophic transfer efficiency)is at risk of being disrupted by the growing mismatch between nutrient content of primary producers and nutrient demand of grazing consumers.Ecological stoichiometry provides a conceptual framework that explains this trophic interaction using C,N and P elemental composition across trophic levels.In light of ongoing climate change and eutrophication,previous studies have raised concerns regarding the growing stoichiometric mismatch between phytoplankton and zooplankton,given the stoichiometric plasticity of phytoplankton.However,there is currently little conclusive evidence on the stoichiometric mismatch from a dual perspective of phytoplankton and zooplankton.To address this,we conducted a mesocosm experiment to investigate the separate and combined effects of climate warming(a constant increase of t3.5C plus heat waves)and eutrophication(nutrient addition)on stoichiometric mismatch between phytoplankton and zooplankton by examining stoichiometric changes in both communities.We observed a growing trend in stoichiometric mismatches when warming or nutrient addition acted individually,which was mediated by the increase in nutrient demand(N,P elements)of zooplankton growth.However,when these stressors acted jointly,the mismatches were reversed.This could be because climate warming and eutrophication combined would lead to changes in species composition,which accordingly reshaped the stoichiometric composition at the community level.These results illustrate the need of stoichiometric mismatches for understanding the implication of global change on trophic interactions and ecosystem functioning,requiring consideration not only of cross-trophic levels but also of compositional changes within communities.
