Simulation of an NSGA-III Based Fireball Inner-Temperature-Field Reconstructive Method

Xue, Bing; Hao, Xinchang; Liu, Xuanda; Ziqi, Han; Zhou, Hanchang

doi:10.1109/access.2020.2977853

Cited by 13 publications

(7 citation statements)

References 24 publications

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“…To validate the accuracy of the temperature distribution, the images of ImageIR with 320 × 256 resolution were resized and pooled into the size of 16 × 16, 32 × 32, and 64 × 64. The super-resolution reconstruction performances of the traditional super-resolution method interpolation, sparse representation based on the GA reconstruction method, and machine learning method SRCNN(FCN) − were compared with our SRResUNet.…”

Section: Results and Discussionmentioning

confidence: 99%

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

et al. 2022

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Resolution is an important index for evaluating the reconstruction performance of temperature distributions in a combustion environment, and a higher resolution is necessary to obtain more precise combustion diagnoses. Tunable diode laser absorption tomography (TDLAT) has proven to be a powerful combustion diagnosis method for efficient detection. However, restricted by the line-of-sight (LOS) measurement, the reconstruction resolution of TDLAT was dependent on the size of the detection data, which made it difficult to obtain sufficient data for extreme environmental measurements. This severely limits the development of TDLAT in combustion diagnosis. To overcome this limitation, we proposed a super-resolution reconstruction method based on the super-resolution residual U-Net (SRResUNet) to improve the reconstruction resolution using a software method that could take full advantage of residual networks and U-Net to extract the deep features from the limited data of TDLAT to reconstruct the temperature distribution efficiently. A simulation study was conducted to investigate how the parameters would affect the performance of the super-resolution model and to optimize the reconstruction. The results show that our SRResUNet model can effectively improve the accuracy of reconstruction with super-resolution, with good antinoise performance, with the errors of 2-, 4-, and 8-times super-resolution reconstructions of approximately 5.3, 7.4, and 9.7%, respectively. The successful demonstration of SRResUNet in this work indicates the possible applications of other deep learning methods, such as enhanced super-resolution generative adversarial networks (ESRGANs) for limited-data TDLAT.

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Section: Results and Discussionmentioning

confidence: 99%

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

et al. 2022

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“…By detecting the edge of the flame clearly, we can get many parameters of the flame, such as the area of the flame calculated by the pixel points surrounded by the edge, the perimeter of flame by edge point can be calculated, and the damage radius of the thermobaric projectile can be analyzed effectively. The background can be removed to significantly reduce the amount of data required for the flame temperature calculation 33 and 3D reconstruction. The furnace flame can be diagnosed by our flame edge detection, and some corresponding measures can be taken to improve production efficiency.…”

Section: Resultsmentioning

confidence: 99%

Flame Edge Detection Method Based on a Convolutional Neural Network

et al. 2022

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In this study, an improved flame edge detector based on convolutional neural network (CNN) was proposed. The proposed method can generate edge graphs and extract edge graphs relatively effectively. Our network architecture was based on VGG16 primarily, the last two max-pooling operators and all full connection layers of the VGG16 network were deleted, and the rest was taken as the basic network. The images output by the five convolution layers were upsampled to the size of the input images and finally fused to the edge image. Error calculation and back propagation of the fusion image and label image are carried out to form a weakly supervised model. Using the open datasets BSDS500 to train the network, the ODS F-measure can reach 0.810. Various experiments were carried out on different flame and fire images, including butane–air flame, oxygen–ethanol flame, energetic material flame, and oxygen–acetylene premixed jet flame, and the infrared thermogram was also verified by our method. The results demonstrate the effectiveness and robustness of the proposed algorithm.

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“…The violent high temperature and asphyxia effect is significantly higher than that of conventional explosives, which can kill and destroy a certain range of living forces. In addition, sustained high temperatures can also destroy diffuse biological and chemical weapons and avoid secondary injury caused by the leakage of biological and chemical weapons. − …”

Section: Introductionmentioning

confidence: 99%

“…In the early period, some scholars used static models to calculate the hot dose of fireballs. They ignored the change process of the fireball and simply assumed that the diameter, location, and thermal radiation of the fireball are constant over the full duration of the event, and the results were generally large. − The parameters of fireball temperature, fireball size, and rising height are all dynamically changed, and the dynamic model of fireball thermal radiation is proposed according to the real change process of the fireball. − …”

Section: Introductionmentioning

confidence: 99%

Research on the Dynamic Model of Fireball Thermal Dose Based on the Effective Band Integral Method

Pei

Hao

2023

ACS Omega

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In the brief combustion process of an explosive fireball, the fireball can release considerable radiant energy. Aiming at the problem that the Stephen−Boltzmann formula calculates the fireball surface radiant energy (full band), it does not match the working bands of most infrared thermal imagers. So, in this paper, we obtain dynamic parameters such as the temperature, diameter, and height of the fireball from the infrared thermal image of the thermobaric explosive fireball, achieve on-site atmospheric transmittance by the temperature calibration target, and integrate within the effective wavelength band of the infrared thermal imager, and a precise dynamic model of the fireball's thermal radiation dose was finally established. According to the fireball test data of the infrared thermal imaging camera in the 2−5 μm band, the heat dose of the fireball at different distances is calculated, which is about 1/2.5 of the calculation result of the Stephen−Boltzmann full-band integral formula. The calculations in this paper are more accurate than measurements from existing static models and provide a better assessment of the thermal damage performance of various types of munitions.

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Simulation of an NSGA-III Based Fireball Inner-Temperature-Field Reconstructive Method

Cited by 13 publications

References 24 publications

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

Flame Edge Detection Method Based on a Convolutional Neural Network

Research on the Dynamic Model of Fireball Thermal Dose Based on the Effective Band Integral Method

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