Scattering of Elastic Waves by Small Surface-Breaking or Subsurface Cracks in Three Dimensions

The long-wavelength limit of elastic wave scattering by surface cracks in 3d is considered. It is shown that, if the crack is normal to the surface, the scattering can be described by two real parameters, one of which may be taken to be the crack size. The other therefore depends on shape, orientation, and burial depth. Many computed illustrations are given. It is concluded that the amount of information about cracks obtainable by low frequency elastic wave scattering is very limited

Comparison of Matrix Methods for Elastic Wave Scattering Problems

In the last ten years several numerical methods have been developed for the solution of elastic wave scattering problems that have found application in quantitative flaw definition. Before the development of these methods, due to the complexity of Navier’s equation which governs wave motion in an elastic continuum, numerical results were available only for circular cylinders and spheres. The elastic wave equation is separable only in polar and spherical coordinates. For other geometries, three types of numerical methods have been developed. They were all originally developed for acoustic and electromagnetic problems governed by the scalar and vector wave equations respectively

The Inverse Born Approximation: Exact Determination of Shape of Convex Voids

The Inverse Born Approximation (IBA) to the elastic wave inverse scattering problem is known to give highly accurate results for the shape of complex voids. In this paper we present an argument demonstrating that the IBA is, in fact, exact for determining the size, shape and orientation of a wide class of these scatterers given infinite bandwidth and unlimited aperture information. Essentially, our argument demonstrates how the IBA algorithm picks out the singular contribution to the impulse response function and correctly relates it to the shape of the scatterer. Some specific examples will be used to illustrate the more intuitive aspects of the discussion

Elastic Wave Scattering Methods: Assessments and Suggestions

I was asked by the meeting organizers to review and assess the developments over the past ten or so years in elastic wave scattering methods and to suggest areas of future research opportunities. I will highlight the developments, focusing on what I feel were distinct steps forward in our theoretical understanding of how elastic waves interact with flaws. For references and illustrative figures, I decided to use as my principal source the proceedings of the various annual Reviews of Progress in Quantitative Nondestructive Evaluation (NDE). These meetings have been the main forum not only for presenting results of theoretical research but also for demonstrating the relevance of the theoretical research for the design and interpretation of experiment. In my opinion a quantitative NDE is possible only if this relevance exists, and my major objective is to discuss and illustrate the degree to which relevance has developed. I apologize if any one feels slighted by my not mentioning a particular work To keep the size of “review” manageable, I had to be brief and to the point

Ultrasonic Imaging and the Long Wavelength Phase

Elastodynamic and acoustic wave scattering play an essential role in various inspection methods such as sonar and ultrasonic tomography. Recently there has been considerable interest in the implications of long wavelength elastodynamic scattering for the characterization of flaws in elastic solids [1-6]. If the scattering amplitude is expanded as a power series in the frequency, the leading term is real and varies as the frequency squared. The next term varies as the frequency cubed and is purely imaginary. The evaluation of the phase variation in the long wavelength limit requires the ratio of these terms. Most effort to date has been invested in understanding the dependence of the coefficient of the frequency squared term on the size, shape, orientation and material properties of the scatterer. Richardson [3] and Kohn and Rice [4] have shown that, for an anisotropic elastic inclusion in an otherwise isotropic and homogeneous elastic space, the coefficient depends on at most 22 parameters. In addition, efficient numerical programs have been constructed to evaluate this coefficient for ellipsoidal inclusions. Other work has related it to the stress intensity factor for flaws which are crack-like [5]

Identification of Mendelian inconsistencies between SNP and pedigree information of sibs

Background Using SNP genotypes to apply genomic selection in breeding programs is becoming common practice. Tools to edit and check the quality of genotype data are required. Checking for Mendelian inconsistencies makes it possible to identify animals for which pedigree information and genotype information are not in agreement. Methods Straightforward tests to detect Mendelian inconsistencies exist that count the number of opposing homozygous marker (e.g. SNP) genotypes between parent and offspring (PAR-OFF). Here, we develop two tests to identify Mendelian inconsistencies between sibs. The first test counts SNP with opposing homozygous genotypes between sib pairs (SIBCOUNT). The second test compares pedigree and SNP-based relationships (SIBREL). All tests iteratively remove animals based on decreasing numbers of inconsistent parents and offspring or sibs. The PAR-OFF test, followed by either SIB test, was applied to a dataset comprising 2,078 genotyped cows and 211 genotyped sires. Theoretical expectations for distributions of test statistics of all three tests were calculated and compared to empirically derived values. Type I and II error rates were calculated after applying the tests to the edited data, while Mendelian inconsistencies were introduced by permuting pedigree against genotype data for various proportions of animals. Results Both SIB tests identified animal pairs for which pedigree and genomic relationships could be considered as inconsistent by visual inspection of a scatter plot of pairwise pedigree and SNP-based relationships. After removal of 235 animals with the PAR-OFF test, SIBCOUNT (SIBREL) identified 18 (22) additional inconsistent animals. Seventeen animals were identified by both methods. The numbers of incorrectly deleted animals (Type I error), were equally low for both methods, while the numbers of incorrectly non-deleted animals (Type II error), were considerably higher for SIBREL compared to SIBCOUNT. Conclusions Tests to remove Mendelian inconsistencies between sibs should be preceded by a test for parent-offspring inconsistencies. This parent-offspring test should not only consider parent-offspring pairs based on pedigree data, but also those based on SNP information. Both SIB tests could identify pairs of sibs with Mendelian inconsistencies. Based on type I and II error rates, counting opposing homozygotes between sibs (SIBCOUNT) appears slightly more precise than comparing genomic and pedigree relationships (SIBREL) to detect Mendelian inconsistencies between sib

Practical and Theoretical Considerations in Study Design for Detecting Gene-Gene Interactions Using MDR and GMDR Approaches

Detection of interacting risk factors for complex traits is challenging. The choice of an appropriate method, sample size, and allocation of cases and controls are serious concerns. To provide empirical guidelines for planning such studies and data analyses, we investigated the performance of the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) methods under various experimental scenarios. We developed the mathematical expectation of accuracy and used it as an indicator parameter to perform a gene-gene interaction study. We then examined the statistical power of GMDR and MDR within the plausible range of accuracy (0.50∼0.65) reported in the literature. The GMDR with covariate adjustment had a power of>80% in a case-control design with a sample size of≥2000, with theoretical accuracy ranging from 0.56 to 0.62. However, when the accuracy was<0.56, a sample size of≥4000 was required to have sufficient power. In our simulations, the GMDR outperformed the MDR under all models with accuracy ranging from 0.56∼0.62 for a sample size of 1000–2000. However, the two methods performed similarly when the accuracy was outside this range or the sample was significantly larger. We conclude that with adjustment of a covariate, GMDR performs better than MDR and a sample size of 1000∼2000 is reasonably large for detecting gene-gene interactions in the range of effect size reported by the current literature; whereas larger sample size is required for more subtle interactions with accuracy<0.56

Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest