An experimental system based on the background-oriented schlieren（BOS） technique is built to reconstruct the density and temperature distribution of a flame-induced distorted flow field which has a density gradient....An experimental system based on the background-oriented schlieren（BOS） technique is built to reconstruct the density and temperature distribution of a flame-induced distorted flow field which has a density gradient. The cross-correlation algorithm with sub-pixel accuracy is introduced and used to calculate the background-element displacement of a disturbed image and a fourth-order difference scheme is also developed to solve the Poisson equation. An experiment for a disturbed flow field caused by a burning candle is performed to validate the built BOS system and the results indicate that density and temperature distribution of the disturbed flow field can be reconstructed accurately. A notable conclusion is that in order to make the reconstructed results have a satisfactory accuracy, the inquiry step length should be less than the size of the interrogation window.展开更多
Volumetric imaging represents one of the major development trends of flow diagnostics due to both the advancement in hardware and the requirement for more information to further understand complicated turbulent and/or...Volumetric imaging represents one of the major development trends of flow diagnostics due to both the advancement in hardware and the requirement for more information to further understand complicated turbulent and/or reactive flows. Backgroundoriented Schlieren tomography(BOST) has become increasingly popular due to its experimental simplicity. It has been demonstrated to be capable of simultaneously recovering the distributions of refractive index, density, and temperature of flows.However, its capability in thermometry has only been demonstrated under the axisymmetric assumption, which greatly limits its applicability. In this work, we dedicated to developing a cost-effective BOST system for the simultaneous retrieval of refractive index, density, and temperature distributions for the asymmetric flame. A few representative tomographic inversion algorithms were assessed as well. Both numerical and experimental demonstrations were conducted and the results show that our implemented BOST can successfully reconstruct the three-dimensional temperature distribution with a satisfactory accuracy.展开更多
Speech is a highly coordinated process that requires precise control over vocal tract morphology/motion to produce intelligible sounds while simultaneously generating unique exhaled flow patterns.The schlieren imaging...Speech is a highly coordinated process that requires precise control over vocal tract morphology/motion to produce intelligible sounds while simultaneously generating unique exhaled flow patterns.The schlieren imaging technique visualizes airflows with subtle density variations.It is hypothesized that speech flows captured by schlieren,when analyzed using a hybrid of convolutional neural network(CNN)and long short-term memory(LSTM)network,can recognize alphabet pronunciations,thus facilitating automatic speech recognition and speech disorder therapy.This study evaluates the feasibility of using a CNN-based video classification network to differentiate speech flows corresponding to the first four alphabets:/A/,/B/,/C/,and/D/.A schlieren optical system was developed,and the speech flows of alphabet pronunciations were recorded for two participants at an acquisition rate of 60 frames per second.A total of 640 video clips,each lasting 1 s,were utilized to train and test a hybrid CNN-LSTM network.Acoustic analyses of the recorded sounds were conducted to understand the phonetic differences among the four alphabets.The hybrid CNN-LSTM network was trained separately on four datasets of varying sizes(i.e.,20,30,40,50 videos per alphabet),all achieving over 95%accuracy in classifying videos of the same participant.However,the network’s performance declined when tested on speech flows from a different participant,with accuracy dropping to around 44%,indicating significant inter-participant variability in alphabet pronunciation.Retraining the network with videos from both participants improved accuracy to 93%on the second participant.Analysis of misclassified videos indicated that factors such as low video quality and disproportional head size affected accuracy.These results highlight the potential of CNN-assisted speech recognition and speech therapy using articulation flows,although challenges remain in expanding the alphabet set and participant cohort.展开更多
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.NSFC 91441205)
文摘An experimental system based on the background-oriented schlieren（BOS） technique is built to reconstruct the density and temperature distribution of a flame-induced distorted flow field which has a density gradient. The cross-correlation algorithm with sub-pixel accuracy is introduced and used to calculate the background-element displacement of a disturbed image and a fourth-order difference scheme is also developed to solve the Poisson equation. An experiment for a disturbed flow field caused by a burning candle is performed to validate the built BOS system and the results indicate that density and temperature distribution of the disturbed flow field can be reconstructed accurately. A notable conclusion is that in order to make the reconstructed results have a satisfactory accuracy, the inquiry step length should be less than the size of the interrogation window.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51706141&51976122)。
文摘Volumetric imaging represents one of the major development trends of flow diagnostics due to both the advancement in hardware and the requirement for more information to further understand complicated turbulent and/or reactive flows. Backgroundoriented Schlieren tomography(BOST) has become increasingly popular due to its experimental simplicity. It has been demonstrated to be capable of simultaneously recovering the distributions of refractive index, density, and temperature of flows.However, its capability in thermometry has only been demonstrated under the axisymmetric assumption, which greatly limits its applicability. In this work, we dedicated to developing a cost-effective BOST system for the simultaneous retrieval of refractive index, density, and temperature distributions for the asymmetric flame. A few representative tomographic inversion algorithms were assessed as well. Both numerical and experimental demonstrations were conducted and the results show that our implemented BOST can successfully reconstruct the three-dimensional temperature distribution with a satisfactory accuracy.
文摘Speech is a highly coordinated process that requires precise control over vocal tract morphology/motion to produce intelligible sounds while simultaneously generating unique exhaled flow patterns.The schlieren imaging technique visualizes airflows with subtle density variations.It is hypothesized that speech flows captured by schlieren,when analyzed using a hybrid of convolutional neural network(CNN)and long short-term memory(LSTM)network,can recognize alphabet pronunciations,thus facilitating automatic speech recognition and speech disorder therapy.This study evaluates the feasibility of using a CNN-based video classification network to differentiate speech flows corresponding to the first four alphabets:/A/,/B/,/C/,and/D/.A schlieren optical system was developed,and the speech flows of alphabet pronunciations were recorded for two participants at an acquisition rate of 60 frames per second.A total of 640 video clips,each lasting 1 s,were utilized to train and test a hybrid CNN-LSTM network.Acoustic analyses of the recorded sounds were conducted to understand the phonetic differences among the four alphabets.The hybrid CNN-LSTM network was trained separately on four datasets of varying sizes(i.e.,20,30,40,50 videos per alphabet),all achieving over 95%accuracy in classifying videos of the same participant.However,the network’s performance declined when tested on speech flows from a different participant,with accuracy dropping to around 44%,indicating significant inter-participant variability in alphabet pronunciation.Retraining the network with videos from both participants improved accuracy to 93%on the second participant.Analysis of misclassified videos indicated that factors such as low video quality and disproportional head size affected accuracy.These results highlight the potential of CNN-assisted speech recognition and speech therapy using articulation flows,although challenges remain in expanding the alphabet set and participant cohort.
