首页 > 论文 > 光学学报 > 39卷 > 10期(pp:1012002--1)

基于正则先验的全变差快速代数迭代算法及其在火焰辐射测量中的重建性能分析

Regularization Priori Based Fast ARTTV Algorithm and Its Reconstruction Performance Analysis During Flame Radiation Measurement

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

燃烧诊断技术的发展是影响动力装置热工性能和大型炉窑加热性能的关键因素。现有的检测技术主要有相干反斯托克斯拉曼光谱法[1](CARS)、激光诱导炽光法[2](LII)和激光荧光光谱法[3](LIF),这些检测技术虽然能够实现对火焰的测量,但由于这些技术所依托的设备成本高昂、空间布置困难,无法在工业实际测量中得到广泛推广。

Abstract

Aiming at the problems of low precision and slow speed of the traditional reconstruction algorithms, we propose a regularization priori based fast all variation algebraic iteration (ARTTV) algorithm to improve the reconstruction precision of the symmetric and asymmetric flames. Further, to improve the reconstruction speed, we establish an extreme learning machine neural network based on the “ARTTV-particle swarm algorithm kernel”, which exhibits approximately the same reconstruction ability as that of the iterative algorithm. The construction speed of the proposed algorithm is approximately 300 times that of the iterative algorithm.

Newport宣传-MKS新实验室计划
补充资料

DOI:10.3788/AOS201939.1012002

所属栏目:仪器,测量与计量

基金项目:国家自然科学基金青年项目;

收稿日期:2019-03-06

修改稿日期:2019-06-21

网络出版日期:2019-10-01

作者单位    点击查看

李明杰:武汉科技大学耐火材料与冶金国家重点实验室, 湖北 武汉 430081武汉科技大学材料与冶金学院, 湖北 武汉 430081
贺铸:武汉科技大学耐火材料与冶金国家重点实验室, 湖北 武汉 430081武汉科技大学材料与冶金学院, 湖北 武汉 430081

联系人作者:贺铸(hezhu@wust.edu.cn)

备注:国家自然科学基金青年项目;

【1】Tolles W M and Nibler J W. McDonald J R, et al. A review of the theory and application of coherent anti-Stokes Raman spectroscopy (CARS). Applied Spectroscopy. 31(4), 253-271(1977).

【2】Lou C, Chen C, Sun Y P et al. Review of soot measurement in hydrocarbon-air flames. Scientia Sinica(Technologica). 40(8), 946-958(2010).
娄春, 陈辰, 孙亦鹏 等. 碳氢火焰中碳黑检测方法评述. 中国科学:技术科学. 40(8), 946-958(2010).

【3】Rensberger K J, Jeffries J B, Copeland R A et al. Laser-induced fluorescence determination of temperatures in low pressure flames. Applied Optics. 28(17), 3556-3566(1989).

【4】Liu J, Kang Y Q, Gu Y B et al. Sparse tensor constrained for low dose CT reconstruction. Acta Optica Sinica. 39(8), (2019).
刘进, 亢艳芹, 顾云波 等. 稀疏张量约束的低剂量CT图像重建. 光学学报. 39(8), (2019).

【5】Cheng Q, Zhang X Y, Wang Z C et al. Simultaneous measurement of three-dimensional temperature distributions and radiative properties based on radiation image processing technology in a gas-fired pilot tubular furnace. Heat Transfer Engineering. 35(6/7/8), 770-779(2014).

【6】Cai W W and Kaminski C F. Tomographic absorption spectroscopy for the study of gas dynamics and reactive flows. Progress in Energy and Combustion Science. 59, 1-31(2017).

【7】Yang W Q and Peng L H. Image reconstruction algorithms for electrical capacitance tomography. Measurement Science and Technology. 14(1), R1-R13(2003).

【8】Song X Z, Xu Y B and Dong F. A spatially adaptive total variation regularization method for electrical resistance tomography. Measurement Science and Technology. 26(12), (2015).

【9】Zhang X Y, Cheng Q, Lou C et al. An improved colorimetric method for visualization of 2-D, inhomogeneous temperature distribution in a gas fired industrial furnace by radiation image processing. Proceedings of the Combustion Institute. 33(2), 2755-2762(2011).

【10】Liu D, Yan J H, Wang F et al. Experimental reconstructions of flame temperature distributions in laboratory-scale and large-scale pulverized-coal fired furnaces by inverse radiation analysis. Fuel. 93, 397-403(2012).

【11】Liu L H and Man G L. Reconstruction of time-averaged temperature of non-axisymmetric turbulent unconfined sooting flame by inverse radiation analysis. Journal of Quantitative Spectroscopy and Radiative Transfer. 78(2), 139-149(2003).

【12】Sidky E Y and Pan X C. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine & Biology. 53(17), 4777-4807(2008).

【13】Cai W W, Li X S and Ma L. Practical aspects of implementing three-dimensional tomography inversion for volumetric flame imaging. Applied Optics. 52(33), 8106-8116(2013).

【14】Zhang B, Zhao M M, Liu Z G et al. Flame four-dimensional deflection tomography with compressed-sensing-revision reconstruction. Optics and Lasers in Engineering. 83, 23-31(2016).

【15】Huang G B, Zhu Q Y and Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks. [C]//2004 IEEE International Joint Conference on Neural Networks, July 25-29, 2004, Budapest, Hungary. New York: IEEE. 985-990(2004).

【16】Liu X, Wang X X, Hu H L et al. An extreme learning machine combined with Landweber iteration algorithm for the inverse problem of electrical capacitance tomography. Flow Measurement and Instrumentation. 45, 348-356(2015).

【17】Yu T, Cai W W and Liu Y Z. Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics. Review of Scientific Instruments. 89(4), (2018).

【18】Liu D, Yan J H and Cen K F. On the treatment of scattering for three-dimensional temperature distribution reconstruction accuracy in participating medium. International Journal of Heat and Mass Transfer. 54(7/8), 1684-1687(2011).

【19】Li T J, Li S N, Yuan Y et al. Light field imaging analysis of flame radiative properties based on Monte Carlo method. International Journal of Heat and Mass Transfer. 119, 303-311(2018).

【20】Gordon R, Bender R and Herman G T. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. Journal of Theoretical Biology. 29(3), 471-481(1970).

【21】Mo L D and Xu B Q. An improved TV image reconstruction algorithm. Electronic Science and Technology. 29(10), 47-50(2016).
莫礼东, 徐伯庆. 一种改进的TV图像重建算法. 电子科技. 29(10), 47-50(2016).

【22】40):. Guo X C. Unconstrained optimization problems: study on the most rapid descent method, Newton algorithm. Course Education Research. 157, (2018).
郭勋诚. 40):. . 无约束优化问题: 最速下降法和牛顿算法研究. 课程教育研究. 157, (2018).

【23】Paige C C and Saunders M A. LSQR: an algorithm for sparse linear equations and sparse least squares. ACM Transactions on Mathematical Software. 8(1), 43-71(1982).

【24】Shi Y and Eberhart R C. Empirical study of particle swarm optimization. [C]//1999 Congress on Evolutionary Computation-CEC99, July 6-9, 1999, Washington, DC, USA. New York: IEEE. 1945-1950(1999).

【25】Liu F S, Guo H S, Smallwood G J et al. Numerical modelling of soot formation and oxidation in laminar coflow non-smoking and smoking ethylene diffusion flames. Combustion Theory and Modelling. 7(2), 301-315(2003).

【26】van Heeswijk M, Miche Y, Oja E et al. . GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing. 74(16), 2430-2437(2011).

引用该论文

Mingjie Li,Zhu He. Regularization Priori Based Fast ARTTV Algorithm and Its Reconstruction Performance Analysis During Flame Radiation Measurement[J]. Acta Optica Sinica, 2019, 39(10): 1012002

李明杰,贺铸. 基于正则先验的全变差快速代数迭代算法及其在火焰辐射测量中的重建性能分析[J]. 光学学报, 2019, 39(10): 1012002

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF