Set Of Test Points Research Articles

Numerical Investigation of the Effect of Trailing Edge Thickness of Simulated Ceramic Matrix Composite Blades on Loss Profiles

Abstract Ceramic matrix composite (CMC) is an enabling material allowing higher turbine inlet temperatures and possibly resulting in better thermal efficiency of gas turbine engines. This is because CMC layers with environmental barrier coating have significantly higher thermal limits (∼1755 K) compared with the more conventional alloyed metallic blades. CMCs possess a complex fabrication process resulting in different geometrical characteristics than metallic blades (e.g., larger trailing edge thickness, large leading-edge curvature, etc.). It is therefore desirable to assess the aerodynamic performance of the CMC blades using experimental and numerical simulations. To investigate, three different CMC blades with varying trailing edge thicknesses were numerically simulated. Both large-eddy simulation with the dynamic subgrid closure as well a recently implemented Reynolds-averaged Navier–Stokes coupled with an intermittency function-based transition model were utilized. Two sets of experimental test points with different Reynolds numbers at a given high-freestream turbulence range (Tu = ∼10–13%) were considered. The predicted pressure loading profiles, total loss distributions, and integrated loss of the three different CMC blades were computed and comparisons against the experimental data are presented herein. It was found that the mixing loss forms a larger proportion of the overall loss as the trailing edge thickness increases.

Biot-Savart routines with minimal floating point error

A set of routines to compute the magnetic vector potential and magnetic field of two types of current carriers is presented. The (infinitely thin) current carrier types are a straight wire segment and a circular wire loop. The routines are highly accurate and exhibit the correct asymptotic behavior far away from and close to the current carrier. A suitable global set of test points is introduced and the methods presented in this work are tested against results obtained using arbitrary-precision arithmetic on all test points. The results are accurate to approximately 16 decimal digits of precision when computed using 64 bit floating point arithmetic. There are a few exceptions where accuracy drops to 13 digits. These primitive current carrier types can be used to assemble more complex arrangements, such as a current along a polygon (by means of defining straight wire segments from point to point along the polygon) and a multi-winding coil with circular cross-section. Reference data is provided along with the code for benchmarks with other implementations. Program summaryProgram Title: Biot-Savart Routines with Minimal Floating Point ErrorCPC Library link to program files:https://doi.org/10.17632/zcwk7zzt9y.1Developer's repository link:https://github.com/jonathanschilling/abscabLicensing provisions: Apache-2.0Programming language: C, Python, Java, FortranSupplementary material: Reference output data for all methods described in this article.Nature of problem: A common task in computational physics is to compute the magnetic field and magnetic vector potential originating from current-carrying wire arrangements. These current carriers are often approximated for computational simplicity as infinitely thin filaments following the center lines of the real current carrier. Computational methods are thus needed to compute the magnetic field and the magnetic vector potential of a single straight wire segment or a single circular wire loop as basis for modeling more complex current carrier arrangements by superposition of the current carrier primitives. A current carrier path specified by (x,y,z) coordinates can be modeled as a set of straight wire segments from point to point along the polygon describing the current carrier geometry. Closed circular wire loops are also commonly used as a proxy for a physical coil with helical windings.Solution method: Analytical expressions are derived in this work to accurately compute the magnetic field and the magnetic vector potential of a straight wire segment and a circular wire loop. The expressions consist of several special cases, which are automatically switched between depending on the location of the evaluation location in the coordinate system of the current carrier primitive. This approach guarantees that always the most accurate formulation available for a given evaluation location is used, including explicit use of simplifications in certain special cases for speed and accuracy.Additional comments including restrictions and unusual features: For most test cases, the full relative accuracy of the floating point arithmetic chosen for implementation is retained throughout the computations (16 digits of precision in the IEEE-754 binary64 implementation). There are a few exceptions where accuracy drops by up to 3 digits of precision (in the binary64 case). The provided implementations have not been optimized for speed.

A Test Point Selection Method Based on Fault Response Matrices Framework

This paper presents a novel method for test point selection based on fault response matrices framework. The main purpose of this method is to minimize the set of test points by constructing a fault response matrices framework, to resolve fault effects vanishing and fault detection degradation, and to achieve fewer test point set and higher fault coverage. According to the analysis of the fault response matrices and their derived matrices, the efficiency of the response of each node on the fault is ranked and a new propagation cone for undetectable faults is defined to narrow candidate test point set. We propose a custom testability measure to evaluate the impact of candidate test points on design, and obtain an optimal configuration of the most efficient test points accurately and rapidly. This methodology fully considers the correlation among test points, faults, and test vectors across internal signals resolving timing constraints to increase faults coverage. It can narrow the hard-to-test areas of random test patterns, and reduce the test vector set volume of deterministic test patterns. Our experimental results demonstrate the superiority of the proposed method: higher test coverage, minimum test time, fewer deterministic test patterns, and smaller hardware overhead.

Optimal Test Point Placement Based on Fault Diagnosability Quantitative Evaluation

The optimal test point placement problem in existing research results is mainly limited to the qualitative study of whether faults can be diagnosed without considering the difficulty of diagnosing faults. We proposed an optimal test point placement approach based on fault diagnosability quantitative evaluation to solve the above problem. First, the fault diagnosability is quantitatively evaluated based on the maximum mean discrepancy (MMD). Then, the problem of optimal test point placement is considered a multi objective optimization problem. The optimal test point set is solved using the multi objective sparrow search algorithm (MOSSA) based on the fault diagnosability quantitative evaluation results, considering limitations on the test point number, reliability, and cost. Finally, the proposed approach is used to optimize the placement of test points in the switching power supply system. The simulation results show that only three test points need to be placed to make the system meet the fault diagnosability requirements. Two test point placement schemes are obtained, which can be selected according to different practical requirements. The experimental results illustrate that the proposed approach can optimize the system test point placement while ensuring good fault diagnosability.

МЕТОД РОЗВ’ЯЗАННЯ ДВОВИМІРНОЇ ЗАДАЧІ РОЗТАШУВАННЯ ТОЧОК НА РЕГУЛЯРНИХ СІТКАХ

Point location problems often arise in areas such as object visibility analysis, sensor location, human flow simulation, urban planning, mobile control, and paint and film application. In many such problems, it is often necessary to define a set of points that cover given positions, such as viewpoints on the observed surface. This problem can be considered in terms of finding the set of sensor positions, which provides maximum coverage of the points of the set by some criterion of reachability, which is formulated in the paper on the basis of the Euclidean metric. Usually, the object location problem does not have an exact solution, so in practice, optimization methods are used to solve it, which do not guarantee finding a global optimum solution. This makes it necessary to find more effective methods for solving point location problems. The paper presents a method of solving the problem of location of a set of points, which provides a given coverage of the input point set by the criterion of reachability based on drawing circles on a regular (pixel) grid, and determining the grid points that belong to such circles. The presented method allows to solve such problems in a linear time from the number of points of the input set. The main iterations of the algorithm for finding a set of points covering a given set are presented, an experimental study on test point sets on a plane is performed, and examples of the obtained solutions are given. The presented approach consists of the following steps: formation of the criterion of mutual reach of points; determining the dimensions of the pixel grid and the battery array; drawing circles of a given radius on the grid; determination of points belonging to the applied circles; increment of values in the accumulator.Keywords: placement problem, point set, mutual reachability of points, pixel grid, coverage of point set.

Feasibility of Kd-Trees in Gaussian Process Regression to Partition Test Points in High Resolution Input Space

Bayesian inference using Gaussian processes on large datasets have been studied extensively over the past few years. However, little attention has been given on how to apply these on a high resolution input space. By approximating the set of test points (where we want to make predictions, not the set of training points in the dataset) by a kd-tree, a multi-resolution data structure arises that allows for considerable gains in performance and memory usage without a significant loss of accuracy. In this paper, we study the feasibility and efficiency of constructing and using such a kd-tree in Gaussian process regression. We propose a cut-off rule that is easy to interpret and to tune. We show our findings on generated toy data in a 3D point cloud and a simulated 2D vibrometry example. This survey is beneficial for researchers that are working on a high resolution input space. The kd-tree approximation outperforms the naïve Gaussian process implementation in all experiments.

Reduction of Test Data with Hybrid Test Points

ATPG vectors for a combinational circuit exhibit correlations among the bits of a test vector. We propose a BIST circuit design methodology using spectral methods which utilizes the correlation information. This circuit serves dual purposes. It generates BIST vectors that are similar to the ATPG vectors with higher test coverage as compared to random and weighted random vectors. The same circuit can also function as a test data de-compressor for compressed ATPG vectors applied from an external tester. Logic built-in self-test (LBIST) is now increasingly used with on-chip test compression as a complementary solution for in-system test, where high quality, low power, low silicon area, and most importantly short test application time are key factors affecting ICs targeted for safety-critical systems. Test points, common in LBIST-ready designs, can help to reduce test time and the overall silicon overhead so that one can get desired test coverage with the minimal number of patterns. Typically, LBIST test points are dysfunctional when enabled in an ATPG-based test compression mode. Similarly, test points used to reduce ATPG pattern counts cannot guarantee desired random testability. We present a hybrid test point technology designed to reduce deterministic pattern counts and to improve fault detection likelihood by means of the same minimal set of test points. The hybrid test points are subsequently deployed in a scan-based LBIST scheme addressing stringent test requirements of certain application domains such as the automotive electronics market. These requirements, largely driven by safety standards, are met by significantly reducing test application time while preserving the high fault coverage. The new scheme is a combination of pseudorandom test patterns delivered in a test-per-clock fashion through conventional scan chains and per cycle-driven hybrid observation test points that capture faulty effects every shift cycle into dedicated scan chains. We also exhibit test data compression capabilities of the proposed BIST architecture. This architecture provides a maximum test data compression exceeding and a proportional test time reduction for serial interface reseeding.

A test point selection approach for DC analog circuits with large number of predefined faults

Test point selection is an important stage in analog circuits fault diagnosis. In this paper, a test point selection method is proposed that is suitable for large numbers of predefined faults in DC analog circuits using the technique of constructing a fault dictionary for each component. Main idea is selecting a test point set with minimum amount of overlap between fault and no-fault regions for each component. A combined exclusive-inclusive algorithm is applied on each fault dictionary and using the achieved information, a grade is given to each test point. Then the test points with grades lower than a threshold value are eliminated and a decision guide is provided for tester to select final test point set. Applying this method on a benchmark circuit is eliminating 22% of test points and the accuracy metrics of proposed approach is comparable with using all of the test points. The results show that by eliminating 56% of the test points, the accuracy metrics just drops about 4%.

Logic BIST With Capture-Per-Clock Hybrid Test Points

Logic built-in self-test (LBIST) is now increasingly used with on-chip test compression as a complementary solution for in-system test, where high quality, low power, low silicon area, and most importantly short test application time are key factors affecting ICs targeted for safety-critical systems. Test points, common in LBIST-ready designs, can help to reduce test time and the overall silicon overhead so that one can get desired test coverage with the minimal number of patterns. Typically, LBIST test points are dysfunctional when enabled in an ATPG-based test compression mode. Similarly, test points used to reduce ATPG pattern counts (PCs) cannot guarantee desired random testability. In this paper, we present a hybrid test point technology designed to reduce deterministic PCs and to improve fault detection likelihood by means of the same minimal set of test points. The hybrid test points are subsequently deployed in a scan-based LBIST scheme addressing stringent test requirements of certain application domains such as the automotive electronics market. These requirements, largely driven by safety standards, are met by significantly reducing test application time while preserving the high fault coverage. The new scheme is a combination of pseudorandom test patterns delivered in a test-per-clock fashion through conventional scan chains and per-cycle-driven hybrid observation test points that capture faulty effects every shift cycle into dedicated scan chains. Their content is gradually shifted into a compactor shared with the remaining chains that deliver responses once a test pattern has been shifted-in. Experimental results obtained for industrial designs confirm feasibility of the new schemes, and they are reported herein.

Optimum test point selection method for analog fault dictionary techniques

Fault dictionary has two types of online and offline calculation. Online calculation is simple, but offline is excessive and time-consuming. In order to reduce the computation time and dimension of fault dictionary, optimal test points selection is very essential. So, the main purpose, fault isolation, is achieved in a short time. In this paper a new efficient method to select an optimum set of test points for fault diagnosis is proposed. At the first, a fault-isolated table is constructed to pick out the special test points from the candidates. Then, the isolation ability of special test points has been considered. If they can’t isolate all of faults, the fault dictionary is rearranged. Therefore, the special test points and isolated faults are eliminated from the table of fault dictionary. In this step, the test point with more single fault is added to special test points. This step is repeated to isolate all of faults. The proposed method is applied on two-stage operational amplifier. By this method, all of faults for this structure are isolated. The computation requirements are very simple than the other methods. In this circuit, the 0.045 s is needed to isolate all of circuit faults. According to the results, it’s clear that the method is a good solution to minimize the size of the test points set. Also, it can be a practical method for medium and large scale systems.

Test Point Selection Method for Analog Circuit Fault Diagnosis Based on Similarity Coefficient

The demand for testability analysis has increased with the integration densities and complexity of circuits. As an important part of testability analysis, the test point selection method needs to be researched in depth. A new similarity coefficient criterion is proposed to determine the fault isolation degree because output responses of a circuit with component tolerance are approximately subject to the normal distribution. Then, a new test point selection method is proposed based on the fault-pair similarity coefficient criterion information table. Simulation experiments are used to validate the accuracy of the proposed method in terms of the optimum test point set and fault isolation degree. The results show that the proposed method improves the performance of test point selection by comparing with the other reported methods.

Quasistatic error modeling and model testing for a 5-axis machine with a redundant axis

This paper presents an approach to modeling the quasistatic errors of a 5-axis machine tool with one redundant axis. By introducing errors to the ideal joints and shape transforms of the kinematics of the machine, an error model is obtained. First order error characteristics are used to parameterize the introduced errors. It is found that of the 52 introduced error parameters, only 32 have a linearly independent effect on the volumetric errors observed in the machine’s workspace. To identify these error parameters, the volumetric error components at 290 randomly chosen points are measured with a laser tracker. The unknown parameters are obtained by least-squares estimation, and the resulting model able to reduce average magnitude of the volumetric error vectors at these points by an average of 90% of their original values. Further, the identified model was used to predict the errors observed in two independent test point sets (each set consisting of 48 points). A 75% reduction in the average magnitude of the error vectors was observed. A large fraction of the residual errors was found to be attributable to the thermal drift of the machine during the experiments, which were not conducted in a thermally controlled environment and the positioning repeatability of the machine.

Concurrent surrogate model selection (COSMOS): optimizing model type, kernel function, and hyper-parameters

This paper presents an automated surrogate model selection framework called the Concurrent Surrogate Model Selection or COSMOS. Unlike most existing techniques, COSMOS coherently operates at three levels, namely: 1) selecting the model type (e.g., RBF or Kriging), 2) selecting the kernel function type (e.g., cubic or multiquadric kernel in RBF), and 3) determining the optimal values of the typically user-prescribed hyper-parameters (e.g., shape parameter in RBF). The quality of the models is determined and compared using measures of median and maximum error, given by the Predictive Estimation of Model Fidelity (PEMF) method. PEMF is a robust implementation of sequential k-fold cross-validation. The selection process undertakes either a cascaded approach over the three levels or a more computationally-efficient one-step approach that solves a mixed-integer nonlinear programming problem. Genetic algorithms are used to perform the optimal selection. Application of COSMOS to benchmark test functions resulted in optimal model choices that agree well with those given by analyzing the model errors on a large set of additional test points. For the four analytical benchmark problems and three practical engineering applications --- airfoil design, window heat transfer modeling, and building energy modeling --- diverse forms of models/kernels are observed to be selected as optimal choices. These observations further establish the need for automated multi-level model selection that is also guided by dependable measures of model fidelity.

Quasistatic Error Modeling and Model Testing for a 5-Axis Machine

This paper presents an approach to modeling the quasistatic errors of a 5-axis machine tool with one redundant axis. By introducing errors to the ideal joints and shape transforms of the kinematics of the machine, an error model is obtained. First order error characteristics are used to parameterize the introduced errors. It is found that of the 52 introduced error parameters, only 32 have a linearly independent effect on the volumetric errors observed in the machine's workspace. To identify these error parameters, the volumetric error components at 290 randomly chosen points are measured with a laser tracker. The unknown parameters are obtained by least-squares estimation, and the resulting model able to reduce average magnitude of the volumetric error vectors at these points by an average of 90% of their original values. Further, the identified model was used to predict the errors observed in two independent test point sets (each set consisting of 48 points). A 75% reduction in the average magnitude of the error vectors was observed. A large fraction of the residual errors was found to be attributable to the thermal drift of the machine during the experiments where were not conducted in a thermally controlled environment and the positioning repeatability of the machine.

Simplified Entropy and Multidimensional Search for Test Points Selection

In order to meet the demand of test points selection on higher speed, a new test points selection algorithm based on simplified entropy and multidimensional search is proposed. With the proposed algorithm, statistic results, instead of the complex entropy calculation, are utilized to estimate the order of entropy. In addition, multidimensional search method is adopted to find potential test points sets that can isolate the potential faults. The proposed method is also adaptive to the complexity of fault dictionary. In each iteration of the proposed algorithm, the dimension of multidimensional search could be changed according to the complexity of fault dictionary. Statistical experiments have shown that the proposed algorithm is more efficient in finding local optimum sets of test points compared with other test points selection algorithms.

The Use Of A Novel Stereological Sampling Approach To Estimate Islet, Duct And Acinar Total Cell Number And Compartment Volume In Nonhuman Primate Pancreas

Design‐based stereology is used to obtain quantitative estimates of biological structures from sections with known precision and unlike model‐based stereology, is free of any assumptions concerning tissue structure. Practical solutions for incorporation of stereological estimates of total number in regulatory toxicology studies should provide appropriate sections for both the estimation of cell or particle number and routine histopathology evaluation in standard paraffin format from the same animal. Development of novel sampling principles, stereological software and advancements in computer hardware technology make it possible to fulfill both these criteria. A novel form of sampling based on non‐uniform statistical sampling principles known as the Proportionator has been recently developed that provides precise estimates of non‐uniformly distributed structures efficiently. In this test system, the pancreas was used because of the inhomogeneous distribution of cell types with in the organ. Coupled with the Fractionator sampling principles, estimates of total acinar, duct and islet cell number and compartment volume can be efficiently quantified. Using a systematic uniform sampling strategy, entire pancreas from 6 nonhuman primates were sampled and processed to paraffin blocks. Disectors were generated and immunostained to delineate the islet and duct cells. Virtual images of disectors were captured and imported into the Visiopharm Integrator System (VIS) and sampled using the Autodisector/Proportionator software. Matched fields of view (FOV) were superimposed with either an unbiased counting frame (test probe for cell number) or a set of test points (test probe for compartment volume), and counts were performed. Mean total cell estimates were 656.78 × 106 (acinar), 267.7 × 106 (duct) and 49.47 × 106 (islet). Mean total compartment volumes were 1575.46 mm3 (acinar), 477.04 mm3 (duct), 35.91 mm3 (islet). Application of the Proportionator Fractionator sampling principles allowed for a more efficient, less labor intensive approach to quantitate organ sub‐structures and heterogeneously distributed cell populations. This study was conducted in accordance with the GSK Policy on the Care, Welfare and Treatment of Laboratory Animals and was reviewed by the ethical review process at the institution where the work was performed.

Application of permutation genetic algorithm for sequential model building–model validation design of experiments

The work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient design of experiments (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to enable an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of model building---model validation (MB---MV) sequences, by generating optimal infill sets of test points as OLH DoEs that preserve good space-filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB---MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back function, as well as the Gasoline Direct Injection engine steady-state engine mapping problem that motivated this research. The case studies show that the algorithm is effective in delivering quasi-orthogonal space-filling DoEs with good properties even after several MB---MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, is that significant reduction in the effort and cost of testing can be achieved.

A general method for analog test point selection using multi-frequency analysis

An important problem that arises in analog testing for fault dictionary approach is the test point selection. It consists of selecting a minimum set of test points to achieve the highest percentage of isolation. By using the concepts of ambiguity set and integer-coded dictionary, it can be formulated as a combinatorial optimization problem. On the basis of the formulation, this paper develops a general and accurate method for test point selection. This is achieved by presenting a new ambiguity set partition method based on hierarchical clustering and an improved entropy index method. Besides, multi-frequency analysis is also incorporated in the described method. The proposed method is tested and compared with exhaustive search through its application to two benchmark circuits. The results indicate that, compared with the extant methods, our method search for a test point set that not only includes a minimum number of test points but also has a good performance in terms of diagnostic accuracy and fault isolation rate. Meanwhile, the proposed method can be extended to deal with the problem of test point selection for some specific fault model.

Chaotic binary bat algorithm for analog test point selection

A set of novel methods are proposed to optimize analog test point selection in this paper. We have introduced chaos and elitist strategy into the binary bat algorithm so as to increase its global search mobility and effectiveness for robust global optimization. In the present study, nine well known chaotic maps are introduced and used to construct the chaotic binary bat algorithm (CBBA) respectively. As a result, we have developed nine different CBBAs and for the first time applied them to deal with the analog test point selection problem. The attractiveness of our proposed CBBA mainly lies in two aspects: the utilization of chaotic maps to tune the BBA parameter and the application of elitist strategy to store all the possible global best solutions. These improvements obviously enhance the performance of BBA and make our new algorithms (CBBAs) get all the best solutions (usually more than one) easily and effectively, which will give us more possible choices in practice. Analog circuits' examples and a group of statistical experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithms. The other reported algorithms are also used to do the comparison. The results indicate that the proposed algorithms have excellent performance in finding the optimum test point sets. Therefore, they are good solutions and applicable to actual circuits and engineering practice.

A New Test Point Selection Method for Analog Circuit

A new method to select an optimum test point set in analog fault diagnosis is proposed in this paper. As the probability density of the circuit output approximately satisfies the normal distribution, an accurate way for determining the fault ambiguity gap is used to calculate the isolation probability of the faults. The proposed fault-pair isolation table derived from the mean and standard deviation values of node voltage can exactly represent the fault-pair isolation capability of the test points. The special test points that can uniquely isolate some particular fault pairs are selected first. This step can help to save the total cost of the computation time and even find the final solution directly. After removing the isolated fault pairs (rows) and the selected test points (columns), the size of the fault-pair isolation table could reduce dramatically. If more optimum test points are needed, the normalized fault-pair isolation probability values in the table are used to select the right test point that has the largest fault-pair isolation capability among all the candidate test points. Analog circuits' examples and the statistical experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithm. The other reported algorithms are also used to do the comparison. The results indicate that the proposed algorithm has excellent performance in minimizing the size of the test point set. Therefore, it is a good solution and applicable to actual circuits and engineering practice.