Imaging sensors have been deployed for a variety of nonintrusive diagnostics in photogrammetry, videometry, and other pertinent gross-field visualization. Especially, three-dimensional (3-D) remote sensing based on stereoscopic vision has become increasingly important in many research and industrial applications. Typical applications can be particle tracking in flow visualization, motion/deformation detection in dynamics and stress analysis, and robot vision in automation and quality control, to name a few. The use of an appropriate calibration technique for image sensing is thus essential in both laboratory and field applications. To provide a robust and reliable calibration capability for stereoscopic 3-D detection, we develop a hybrid technique that is based on the use of artificial neural networks and a conventional physical-mathematical model. The hybrid technique is advantageous in procedural simplicity; that is, ease in hardware setup and speed in data processing. Our results show that the hybrid approach can improve the accuracy in predicting the object space coordinates by about 30% compared to those based on a purely physical-mathematical model. It appears that the hybrid technique can combine the merits of both physical-mathematical model and artificial neural networks to improve the overall performance.
Here, the physical and mathematical model is briefly described first, on which the photogrammetric calibration procedure of our Stereoscopic Tracking Velocimetry (STV) system is based. A new hybrid calibration approach is then introduced, which incorporates the use of artificial neural networks. The concept is to improve the performances of conventional calibration techniques of stereoscopic vision. In order to evaluate the quality of the hybrid calibration approach, calibration error is defined for the use of a camera. Our experimental investigation shows that the accuracy in predicting the object frame coordinates has been improved by 30 percents when the hybrid calibration is employed, as compared with the case when only the previous conventional physical and mathematical model is directly applied. It appears that the new idea of using artificial neural networks together with a physical and mathematical model of a system can improve the overall performance of the system. The hybrid method can also be applicable to other general areas in machine vision.
Convective motion in fluid dynamics and heat transfer is the most important phenomenon to be understood since it can greatly influence the performances of fluid and heat transfer systems in various manners. With the advances of modern technologies, new diagnostics for mapping 3D convective flow is veyr necessary for fundamentals of flow physics. Especially, modern computational modeling has been greatly advanced to demand 3D convective-flow diagnostics in order to verify and tune the methodologies and approaches. Conventional velocimetry is either pointwise or 2D. If available, 3D gross-field velocimetry can allow us unprecedented physical insight as well as the needed data for validation of numerical codes and understanding of funamental flow physics. In an effort to meet the need of 3D flow diagnostics, we have developed stereoscopic tracking velocimetry (STV). STV is based on the simultaneous stereoscopic monitoring of numerous particles dispersed in a carrier fluid. It can thus provide time-sequence velocity maps of an entire flow field. Here we briefly present the methodology of STV and its experimental measurement results of 3D flow fields including the traditional flow involving a free jet and the directional solidification for material processing.
Interferometric reconstruction of a flow field usually consists of three steps. The first is to record interferograms, the second is to extract phase information from interferograms and the final is for numerical inversion of the phase data. In interferometric flow recording, test section enclosures and opaque models are frequently present, blocking a portion of the probing rays or restricting the view angle of the field to produce a partial data set especially for interferometric tomography. It also involves very harsh environments with external vibrations and disturbances of the ambient air. The ill-posed problem is susceptible to experimental noise and can produce serious distortions in reconstruction. Interferometric reconstruction of flow fields thus needs accurate phase information extraction. The major problem encountered in interferometry is that it is extremely sensitive to external disturbances including the vibration of the optical setup. This is true especially for aerodynamic wind tunnel testing. For successful application of interferometry to experimental fluid mechancis and heat/mass transfer, efficient mechanisms for accurate flow-field recording and information extraction are thus very necessary. In interferometric recording, use of the phase stepping techniques is desirable whenever possible, since they provide the most accuracy. However, they are not applicable under disturbing conditions; that is, under harsh environments. In an effort to provide accurate interferometric data, we device interferogram recording and reduction techniques. They are based on a phase-stepping method: however, applicable to harsh environments including wind tunnel testing. Here we present the governing concepts, investigation results, and application demonstration of our approaches for practical flow measurements. The developed approaches are tested through phoase extraction and 3D reconstruction of an experimental flow field, which is designed for future wind tunnel testing. The test conditions are very harsh, involving building vibrations and ambient air disturbances especially during the interferometric data acquisition in the phase stepping process. The results of the thermocouple readings agree fairly well with those from the experiment when compared. The acceptable error in the entire interferometric reconstruction process is believed to be mainly due to the ill-posed nature of the tomographic reconstruction but not from the phase extraction of the employed phase-stepping technique.
Solid State array sensors are ubiquitous nowadays for obtaining gross field images in numerous scientific and engineering applications including optical diagnostics and instrumentation. Linear responses of these sensors are often required as in interferometry, light scattering and attenuation measurements, and photometry. In most applications, the linearity is usually taken to be granted without thorough quantitative assessment or correction through calibration. Upper-grade CCD cameras of high price may offer better linearity: however, they also require linearity checking and correction if necessary. Intermediate- or low-grade CCD cameras are likely to need calibration for achieving linearity. Here, we present two very simple approaches: one for quickly checking camera linearity without any additional setup and the other for precisely correcting nonlinear sensor responses. It is believed that after calibration, those sensors of intermediate or low grade can function as effectively as their expensive counterparts.
Stereoscopic tracking velocimetry (STV) can be a very efficient diagnostics tool for detecting three-dimensional three-component flows with great experimental freedom and computational processing speed but for a restricted region. To achieve the goal of near-real-time measurement with reasonable measurement accuracy, a particle tracking algorithm has been developed, which is an essential part of STV. The developed particle tracking is based on an optimization approach, hence it is a good candidate to be solved by applying computational neural networks. In this paper, we present the new tracking algorithm and its measurement applications to the material processing involving directional solidification as well as to a pulsating free-jet flow. Preliminary comparison of experimental and numerical results is also presented. We believe that by utilizing the massive parallel-processing power of neural networks for optimization, reliable solutions in the STV application can be obtained for near-real-time data extraction and display.
A refinement of a regional phase unwrapping technique that is driven by an integrated expert system is described. The traditional phase unwrapping algorithms used for phase shifting and Fourier transform is very noise-dependent and frequently unsatisfactory for heavily noise-ridden interferograms. THose methods that try to get rid of noises are too passive and limited. The developed method actively eliminates noises by using algorithmic as well as knowledge- based, intelligent approaches in constructing sound, not- distorted 2(pi) jump lines that divide an entire image into regions. Then regional phase unwrapping is performed region by region by adding or subtracting adequate 2(pi) multiples. The integrated expert system can correct noisy data on the iso-phase lines and the regional phase unwrapping algorithm isolates noises inside the regions without propagation.
Particle tracking is an essential step in data process of stereoscopic imaging velocimetry. It is known that in particle tracking velocimetry, part of the individual particle images or equivalently data points are likely to be lost when a flow field is seeded with a high-density particles. In order to maximize the data point-recovery and to enhance the measurement reliability, the neural networks are employed to attain a globally-optical solution in finding appropriate particle tracks. Our investigation indicates that the neural networks offer very good potential for performance enhancement and has proven to be very useful for stereoscopic imaging velocimetry.
In order to understand 3D transient flow, it is very essential to apply holographic interferometric tomography. The technique is also advantages in providing nonintrusive measurement capability with reasonable accuracy and spatial resolution. Here, a new computational tomographic algorithm which is termed as Curvilinear Nonlocal Basis Function Method is introduced for reconstruction of flow fields. It is appropriate for reconstruction under various ill-posed conditions involving a limited view angle and an opaque object inside a field with irregular boundaries. The reconstruction technique is tested through computer simulation of experiments as well as a real 3D flow field. The results presented here demonstrate reliable measurement accuracy when the technique is applied for reconstruction of transient 3D flow.
Measurement of 3D three-component velocity fields is of profound importance in microgravity fluid experiments including crystal growth, two-phase flows, and thermocapillary phenomena. Stereoscopic imaging velocimetry (SIV) is an optical nonintrusive technique for measuring gross-field flow, which is advantageous in system simplicity for building compact hardware and in software efficiency for continual near-real-time velocity monitoring. However, the challenge is how to increase spatial resolution, that is, marker particle density while maximizing data recovery rate. In this paper, the new SIV algorithms which utilize neural networks, are presented. Preliminary results from both simulating calculation and experiment show that the neural network algorithms offer very good potential for performance enhancement and has proven to be very useful for the SIV technique.
Phase unwrapping algorithms used in phase-shift or Fourier transform techniques can be classified typically into two groups: line-based unwrapping and region-based unwrapping. Theoretically, the latter can be less error susceptible than the former. However, the pixel categorization process required in the previous region-based algorithms, utilizing the local information obtained from 3 by 3 pixels, is still error prone in many interferograms contaminated by large- scale noises. In the new approach, the developed expert system intelligently find 2 (pi) jump iso-phase lines that categorize regions having no phase jump based on global/regional information rather than the local information, that is, interferogram-specific knowledge. Then it performs phase unwrapping region by region by adding or subtracting 2 (pi) phase-wrapped band wherever region changes. The regional phase unwrapping isolates noises inherently without propagation, since every pixel's phase is unwrapped independently each other. The new algorithm is also effective especially in handling large-scaled noise- affected phase distributions.
Despite the rapid proliferation and increased capabilities of computational fluid diagnostic algorithms, there still exists a need for experimental techniques which can provide global information in an accurate and expeditious manner for augmentation and/or verification of numerical methods. While many optical, non-intrusive techniques such as particle imaging velocimetry have been developed and refined in recent years, systems which can simultaneously extract all three-velocity components are few. We present a technique which utilizes a dual-reference-beam holographic recording and reconstruction system along with a two-step data acquisition and processing method for the determination of in- and out-of-plane velocity components form a single viewing direction. Although still under development, the method, termed holographic diffraction image velocimetry, shows promise to become a useful tool for accurate gross-field diagnostics of complex flows.
A new tomographic algorithm termed Curvilinear Nonlocal Basis Function Method (CNBFM) is formulated and tested for several different computer-generated fields. In addition, the performance of the hybrid method combining the CNBFM and the previously-developed complementary field method is also tested for the same fields. A holographic interferometric tomographic experiment is designed to apply the developed techniques for reconstruction of a 3D temperature field generated by a thermal plume with an aircraft forebody model inside the flow. Reconstruction results are compared with the corresponding thermocouple readings to check the accuracy of the technique. All the fields form the numerical simulation and the experiment are reconstructed under ill- posed conditions, i.e., limited view angle and incomplete projection. It appears that the developed method can substantially enhance the reconstruction accuracy and resolution in real applications of interferometric 3D flow measurements.
A computational tomographic technique, termed the variable grid method (VGM), has been developed for improving interferometric reconstruction of flow fields under ill-posed data conditions of restricted scanning and incomplete projection. The technique is based on natural pixel decomposition, that is, division of a field into variable grid elements. The performances of two algorithms, that is, original and revised versions, are compared to investigate the effects of the data redundancy criteria and seed element forming schemes. Tests of the VGMs are conducted through computer simulation of experiments and reconstruction of fields with a limited view angel of 90 degree(s). The temperature fields at two horizontal sections of a thermal plume of two interacting isothermal cubes, produced by a finite numerical code, are analyzed as test fields. The computer simulation demonstrates the superiority of the revised VGM to either the conventional fixed grid method or the original VGM. Both the maximum and average reconstruction errors are reduced appreciably. The reconstruction shows substantial improvement in the regions with dense scanning by probing rays. These regions are usually of interest in engineering applications.
A two-dimensional nonlinear regression method for accurate analysis of single-frame interferograms has been developed and tested. Similar to a simple algorithm for one-dimensional regression, analytical expressions of individual terms in a nonlinear intensity model are estimated through an iterative procedure. Computer simulation of experiments and real interferogram analysis show stable convergence and accurate phase extraction of the method. The method also works well under relatively high-level noise and broken fringe interferograms.
Measurement of an instantaneous flow field by interferometric tomography, that is, reconstruction of a 3D refractive-index field from multidirectional projection data, has ben conducted. In order to simulate the expected experimental arrangement at a wind tunnel, reconstructions are made from a restricted view angle less than 40 degrees and incomplete projections. In addition, appreciable ambient air and experimental setup disturbances are present. A new phase-stepping technique, based on a generalized phase-stepping approach of a four- bucket model, is applied for expeditious and accurate phase information extraction from projection interferograms under the harsh environments. Phase errors caused by the various disturbances, which can include ambient refractive-index change, optical component disturbance, hologram repositioning error, etc., are partially compensated with a linear corrective model. A new computational tomographic technique based on a series expansion approach was also utilized to efficiently deal with arbitrary boundary shapes and the continuous flow fields in reconstruction. The results of the preliminary investigation are encouraging; however, the technique needs to be further developed in the future through refinement of the approaches reported here and through hybridization with previously developed techniques.
The hybrid operation of digital image processing and a knowledge-based AI system has been recognized as a desirable approach of the automated evaluation of noise-ridden interferogram. Early noise/data reduction before phase is extracted is essential for the success of the knowledge- based processing. In this paper, new concepts of effective, interactive low-level processing operators: that is, a background-matched filter and a directional-smoothing filter, are developed and tested with transonic aerodynamic interferograms. The results indicate that these new operators have promising advantages in noise/data reduction over the conventional ones, leading success of the high-level, intelligent phase extraction.
Accurate static fringe-pattern analysis is very important for the successful application of a variety of interferometric techniques. In most cases of practical application, especially in aerodynamic flow testing, substantial noise can be introduced due to prevailing adverse environments. A means for efficiently reducing interferometric noise is thus desirable. Conventional noise reduction has mostly depended on ordinary averaging or median filtering in a squared mask to remove high-frequency components. These techniques, however, can induce some side effects of image blurring. If the structural integrity needs to be preserved, the method to be adopted should be able to eliminate noise efficiently without altering local intensity gradients, that is, local contrasts. In this paper, the concept of directional smoothing is introduced and its application to interferometric noise reduction is presented. Interferograms provide locally similar fringe directions, that is, isophase lines contaminated by noise, and thus contain directional information. In essence, the method exploits this valuable fringe directionality by setting up a slender mask of large aspect ratio along a fringe. A new value, that is, the average or median intensity of the mask, is then assigned to each pixel. The mask can be straight or curved. For a straight mask, the average direction of fringes within a processing region is employed. A curved mask is made to conform to a fringe curve. The proposed method is tested by computer simulation of experiments as well as with real interferograms. The results appear to be promising as compared with conventional techniques, especially for high-level noise.
In this paper, the concept of directional smoothing is introduced and its application to interferogram noise reduction is presented. Interferograms provide isophase lines, that is, fringes and they thus contain directional information. In essence, the method incorporates this valuable directional information of interferograms by setting up a slender mask of relatively large aspect ratio along a fringe. The new value, that is, the average or median intensity of the pixels within the slender mask is assigned to each pixel. The slender mask could be straight or curved. For a straight slender mask, one should find the average direction of fringes within a certain chosen region. For a curved slender mask, the mask is set up along the fringe direction and may vary for each pixel. Based on computer simulation of experiments, the results appear to be promising as compared with ordinary smoothing or median filtering.
Automation of interferogram analysis is very important for successful application of all interferometric measurement techniques. In high-speed aerodynamics or experimental mechanics, complex noise-ridden fringe patterns frequently arise due to prevailing adverse environments. In conventional practice, only local information has been heavily utilized to reduce background noise or to correct phase information. Under these circumstances, the currently available techniques, that is, fringe tracking, phase-shifting, Fourier transform, and regression methods, confront difficulties in phase unwrapping and thus need substantial interactive manual operations. The developed rule-based expert system utilizes both global/regional and local information, and makes use of expert knowledge. It can thus provide a potential for more comprehensive automation in noise reduction and phase unwrapping. The developed expert system adopts a hybrid mechanism in a single package, that is, the low-level and high-level processings to produce an optimal solution in fringe analysis. The system can be coupled with any current interferometric reduction techniques, being based on the analysis of isophase contour lines.
A newly conceived technique, termed holographic diffraction image velocimetry, has been investigated computationally and experimentally. The technique can capture 3D three- component velocity fields from a single observation direction. It is based on double-reference- beam double-exposure off axis holography. The independently reconstructed images are then analyzed by applying a cross-correlation technique with transplacing windows. The technique can offer experimental freedom and performance enhancement as compared with conventional techniques in addition to its ability for measuring 3D three-component velocity fields.
Interferometric reconstruction of three-dimensional flow fields, that is, interferometric tomography, can be a very useful diagnostic tool. It is noninvasive and can capture gross fields; however, it frequently confronts a challenging problem of reconstructing fields from insufficient data. In most cases, flow-field interferometric data are sparse, nonuniform, noisy, and incomplete in projection and scanning because of opaque objects present either inside or outside the field. Recently, a new method has been developed in an effort to improve reconstruction under these ill-posed conditions. The method is appropriate for reconstructing flow fields with the distinct data characteristics, being based on natural pixel decomposition of the field. It employs rectangular grid elements of different sizes and aspect ratios. It thus reflects intrinsic spatial resolution information contained in the data and allows better resolution and accuracy in the region with more probing rays. Computer simulation of
experiments has demonstrated superiority of the method to the conventional one. In simulation, the temperature field of a gravity-driven flow of two interacting heat sources, produced by a numerical code, are tested. Both the maximum and average reconstruction errors are reduced appreciably. Especially, the reconstruction demonstrates substantial improvement in the region with dense scanning by probing rays.
Interferometric reconstruction of three-dimensional flow fields, that is, interferometric tomography, can be a very useful flow diagnostic tool in many engineering applications. It is noninvasive and can capture gross fields; however, it frequently confronts a challenging problem of reconstructing flow fields from insufficient data. In most cases, flow-field interferometric data are sparse, nonuniform, noisy, and incomplete in projection and scanning due to opaque objects present either inside or outside the field. Recently, we have developed and tested a new approach in an effort to improve reconstruction under these ill-posed conditions. In essence, the method incorporates distinct features of flow field data, being based on natural pixel decomposition of the field to be reconstructed. It employs rectangular grid elements of different sizes and aspect ratios. It thus reflects intrinsic spatial resolution information contained in the measured data, and allows reconstruction with better resolution and accuracy in the region with more probing rays scanned. It also can efficiently utilize a priori information on the field. Computer simulation of experiments involving a flow field has demonstrated the superiority of the developed method to the conventional fixed grid method. In simulation, the temperature field of a three-dimensional gravity-driven flow of two interacting cubic heat sources, which is produced from a numerical code, is tested. Both of the maximum and average reconstruction errors are reduced appreciably. Especially, the reconstruction demonstrates substantial improvement in the region of interest. Currently, an experiment for verifying the developed interferometric tomographic technique as well as the three-dimensional numerical heat transfer code is under way.
In the past, various techniques have been developed for fringe reduction of conventional interferograms. Most typical ones utilize maximum/minimum or side tracking based on conventional image processing. These methods, however, pose a resolution limitation, not allowing acquisition of fractional fringe order numbers. The Fourier Transform method, requiring a relatively large number of fringes or injection of carrier fringes, also has limitations in some applications. The regression method, while simple, confronts a stability problem. That is, the ill-posed nonlinear intensity function cannot provide unique solutions. Here, we present a new approach and some of the test results for the regression method. It is based on iterative independent estimation of the individual terms that appear in the nonlinear model. The test results demonstrate stable convergence and accurate phase extraction by the new regression approach.
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