Single Test Case Research Articles

Experimental and numerical characterization of a ceramic matrix composite shroud segment under impact loading

This work focuses on the experimental and numerical characterization of stress levels within a shroud segment made of ceramic matrix composite (CMC) material undergoing repeated blade contacts. The dedicated experimental set-up consists in a rotating disk with three notched mock blades that impact the shroud segment as they rotate. The underplatform on which the shroud is fixed progressively gets closer to the blades, so that the abradable layer deposited onto the shroud is progressively worn out from a blade revolution to another. Four sensors, located under the shroud segment, are used to record forces during the experiments. Different blade/shroud relative positions are tested, in such a way that impacts may occur at distinct locations. In addition to the experimental tests, a numerical model is built based on both reduced order models of the blade and the shroud segment, in order to numerically predict the forces at the four sensors. This numerical model is first calibrated based on a single reference test case to retrieve the same force magnitude at each sensor. Then, the other tests are simulated using the calibrated model. Predicted contact forces are in good agreement with experimental data, which validates the numerical model. Finally, simulations are carried out considering engine-like conditions, which could not be reproduced using the experimental test bench. The influence of several parameters (radial velocity, impact location, number of blades in contact and angular speed) is analyzed in detail.

Bayesian joint muographic and gravimetric inversion applied to volcanoes

SUMMARY Gravimetry is a technique widely used to image the structure of the Earth. However, inversions are ill-posed and the imaging power of the technique rapidly decreases with depth. To overcome this limitation, muography, a new imaging technique relying on high energy atmospheric muons, has recently been developed. Because muography only provides integrated densities above the detector from a limited number of observation points, inversions are also ill-posed. Previous studies have shown that joint muographic and gravimetric inversions better reconstruct the 3-D density structure of volcanic edifices than independent density inversions. These studies address the ill-posedness of the joint problem by regularizing the solution with respect to a prior density model. However, the obtained solutions depend on some hyperparameters, which are either determined relative to a single test case or rely on ad-hoc parameters. This can lead to inaccurate retrieved models, sometimes associated with artefacts linked to the muon data acquisition. In this study, we use a synthetic example based on the Puy de Dôme volcano to determine a robust method to obtain the resulting model closest to the synthetic model and devoid of acquisition artefacts. We choose a Bayesian approach to include an a priori density model and a smoothing by a Gaussian spatial correlation function relying on two hyperparameters: an a priori density standard deviation and an isotropic spatial correlation length. This approach has the advantage to provide a posteriori standard deviations on the resulting densities. Using our synthetic volcano, we investigate the most reliable criterion to determine the hyperparameters. Our results suggest that k-fold Cross-Validation Sum of Squares and the Leave One Out methods are more robust criteria than the classically used L-curves. The determined hyperparameters allow to overcome the artefacts linked to the data acquisition geometry, even when only a limited number of muon telescopes is available. We also illustrate the behaviour of the inversion in case of offsets in the a priori density or in the data and show that they lead to recognizable structures that help identify them.

Flat bed desiccant dehumidification: A predictive model for desiccant transient characterisation using a species transport model within CFD

As opposed to steady state characteristics which have been, in the past, estensively investigated, the transient dehumidification characteristics of silica gel under different flat bed configurations, has not been studied in detail with respect to varying mass and air flow rates. Such data is essential in the design of systems employing this method of dehumidification. Moreover, numerical models of the performance of flat bed desiccant configurations generally take the form of fundamental studies with explicit modelling of the desiccant particle geometries. The primary objective of this work aims is to generate dehumidification characteristics in high humidity environments. Secondly, a more simplified and practical approach is proposed here on the basis of experimental calibration. The methodology consists of two main approaches: (i) the development of a test rig for the experimental determination of the transient dehumidification characteristics and, (ii) the development of a Computational Fluid Dynamics (CFD) model using experimental data as input to provide easy extrapolation of experimental data. This paper presents detailed dehumidification results for varying air volume flow rate and desiccant mass. The numerical model, on the other hand, successfully predicts the dehumidification performance of varying silica gel masses by using only a single experimental test case. This proves the validity of using such a model to extrapolate on experimental data.

Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches

In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest.

EXCAVATION OF AN OBSIDIAN CRAFT WORKSHOP AT TEOTIHUACAN, MEXICO

AbstractThe original research by the Teotihuacan Mapping Project (TMP) identified a large number of obsidian workshops within Teotihuacan based on surface concentrations of production debris. Clark (1986b) questioned the validity of these identifications and called for subsurface excavation to confirm the presence of in situ workshop locales. This article summarizes the results from the excavation of one of the obsidian workshops identified in the Tlajinga district of Teotihuacan at Compound 17:S3E1 (Compound 17). We describe the excavations, discuss the lithic technology, and examine the subsurface contexts in terms of what they tell us about in situ obsidian craft activity. Excavations confirm that Compound 17 was a locus of large-scale obsidian craft production during the Classic period. While only a single test case, these results suggest that surface remains at Teotihuacan can be a useful guide in identifying craft production areas when they are confirmed through subsurface testing.

Physically Constrained Stochastic Shallow Convection in Realistic Kilometer‐Scale Simulations

AbstractA new configuration of a parameterization for shallow convection in the Icosahedral Nonhydrostatic Model (ICON) is described and tested on a single realistic test case. As a test case, a shallow convective day over Germany is simulated using four different configurations of ICON. These configurations differ by the choice of the shallow convection parameterization, which can be deterministic, stochastic, or completely switched off. As the fourth configuration, the ICON large eddy simulation setup is used as a reference against which the three other ICON model configurations are tested and compared at resolutions from 1 to 10 km. It is demonstrated that a deterministic mass flux closure combined with the stochastic sampling of the cloud base mass fluxes corrects the spatial and temporal distribution of cloudiness. The mean vertical structure of the cloud layer and vertical profiles of the thermodynamic variables in the boundary layer are also improved. The stochastic parameterization adapts to the model resolution by its formulation, while a limited scale‐aware behavior is present in the outcome of the simulations. This limitation stems from the resolution dependence of the resolved dynamics, which produces incorrect distributions of cloudiness, and scale‐dependence opposite of what is expected based on the reference large eddy simulation results. The deterministic version of the convection scheme cannot correct the behavior of the resolved dynamics, while the stochastic version corrects the resolved dynamics to some extent and improves the overall behavior across resolutions.

Mixed-mode cohesive zone parameters from integrated digital image correlation on micrographs only

Mixed-mode loading conditions strongly affect the failure mechanisms of interfaces between different material layers as typically encountered in microelectronic systems, exhibiting complex material stacking and 3D microstructures. The integrated digital image correlation (IDIC) method is here extended to enable identification of mixed-mode cohesive zone model parameters under arbitrary levels of mode-mixity. Micrographs of a mechanical experiment with a restricted field of view and without any visual data of the applied far-field boundary conditions are correlated to extract the cohesive zone model parameters used in a corresponding finite element simulation. Reliable or accurate force measurement data is thereby not available, which constitutes a complicating factor. For proof-of-concept, a model system comprising a bilayer double cantilever beam specimen loaded under mixed-mode bending conditions is explored. Virtual experiments are conducted to assess the sensitivities of the technique with respect to mixed-mode loading conditions at the interface. The virtual experiments reveal the necessity of (1) optimizing the applied local boundary conditions in the finite element model and (2) optimizing the region of interest by analyzing the model’s kinematic sensitivity relative to the cohesive zone parameters. From a single test-case, exhibiting a range of mode-mixity values, the mixed-mode cohesive zone model parameters are accurately identified with errors below 1%. The IDIC-procedure is shown to be robust against large variations in the initial guess values for the parameters.Real mixed-mode bending experiments are conducted on bilayer specimens comprising two spring steel beams and an epoxy adhesive interface, under different levels of mode-mixity. The mixed-mode cohesive zone model parameters are identified, demonstrating that IDIC is a powerful technique for characterizing interface properties of interfaces, imaged with a limited field of view, as is typically the case in microelectronic applications.

High-performance, robustly verified Monte Carlo simulation with FullMonte.

We introduce the FullMonte tetrahedral 3-D Monte Carlo (MC) software package for simulation, visualization, and analysis of light propagation in heterogeneous turbid media including tissue. It provides the highest computational performance and richest set of input, output, and analysis facilities of any open-source tetrahedral-mesh MC light simulator. It also provides a robust framework for statistical verification. A scripting interface makes set-up of simulation runs simple, including parameter sweeps, while simultaneously providing customization options. Data formats shared with class-leading visualization tools, VTK and Paraview, facilitate interactive generation of publication-quality fluence and irradiance maps. The simulator can read and write file formats supported by other similar simulators, such as TIM-OS, MMC, COMSOL (finite-element simulations), and MCML to support comparison. Where simulator features permit, FullMonte can take a single test case, run it in multiple software packages, and load the results together for comparison. Example meshes, optical properties, set-up scripts, and output files are provided for user convenience. We demonstrate its use in several test cases, including photodynamic therapy of the brain, bioluminescence imaging (BLI) in a mouse phantom, and a comparison against MCML for layered geometries. Application domains that can benefit from use of FullMonte include photodynamic, photothermal, and photobiomodulation therapies, BLI, diffuse optical tomography, MC software development, and biophotonics education. Since MC results may be used for preclinical or even clinical experiments, a robust and rigorous verification process is essential. Being a stochastic numerical method, MC simulation has unique challenges associated with verification of output results since observed differences may be due simply to output variance or actual differences in expected output. We describe and have implemented a rigorous and statistically justified framework for comparing between simulators of the same class and for performing regression testing.

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

Method for Automatically Generating Test Cases for Web Services

Because manual test case design is time-consuming and laborious and has certain blindness and inclination, Web service testing is based on WSDL document and formal model tree automatic generation. The theoretical research and Web service single operation test case generation method. Web service operation test case method, and to build test tools. The application results show that this method can greatly save the time of manually analyzing the complex data types in WSDL files and the time of generating the test cases according to the test cases of built-in data types. It simplifies the generation of test cases, and prepares test cases.

Enhancement ofregression testing using genetic data generation and test case prioritization using m-ACO technique

The changes that occur during the software development process is rapid. Hence software has to undergo modification frequently. Due to this modification,the cost for testing increases due to repetitive retesting. This retesting process is called as the regression testing. Modification made in the single test case will make the side effect in all other related test cases. In order to overcome this problem all the test cases have to be retested again and again whenever the changes are incorporated in the software. But testing all the test cases is time consuming and will also increase the cost of testing. To address this problem, this work focuses on providing priority to the test cases. Test case which had more effect to changes is assigned with higher priority and the test case which had the less effect to changes is assigned lower priority. For test case prioritization, we employ m-ACO (Modified Ant colony optimization) method.Test case prioritization is done in two ways namely “Triangle classification problem” and “Quadratic Equation Problem”. Flow of the data in the test case is done by Genetic Algorithm. This identifies the changed code in the program under test. It identifies both indirectly and directly affected def-use association in the modified part of the software by using forward walk algorithm and backward walk algorithm.

Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems

To introduce more automation in the distribution level of the power system, increasingly more data are needed to serve as input to control algorithms. To examine the complex interaction between the various layers of the system and verify the effectiveness of automation, difficult to obtain, realistic test systems are necessary. An algorithm for automatically synthesizing realistic medium voltage distribution feeders, and thus circumvent the data access problem, is presented. The algorithm treats distribution system feeders as graphs with nodes and edges, each with various properties, and leverages this structure to search for emerging statistical patterns. Using a large data set from a DSO in The Netherlands, clear statistical distributions are identified linking properties, such as load, node degree, or cable length, to the feeder structure. Specifically, many properties are linked to a node’s or edge’s distance, in hops, from the primary substation. With consideration for standard engineering practices, the statistical trends are exploited in the synthesis process, to generate feeders, which display similar characteristics to the real samples. The KL-divergence is used in the evaluation of analysis and synthesis results. Beyond solving the data access problem, the use of automatically generated, synthetic, distribution systems will enable testing and validation techniques, such as Monte Carlo simulations, which are currently not possible in this field, where single test cases are the norm.

A Comparative Study on Arrhenius-Type Constitutive Models with Regression Methods

A comparative study was performed on strain-compensated Arrhenius-type constitutive models established with two regression methods: polynomial regression and regression Kriging. For measurements at high temperatures, experimental data of 70Cr3Mo steel were adopted from previous research. An Arrhenius-type constitutive model necessitates strain compensation for material constants to account for strain effect. To associate the material constants with strain, we first evaluated them at a set of discrete strains, then capitalized on surrogate modeling to represent the material constants as a function of strain. As a result, disparate flow stress models were formed via the two different regression methods. The constructed constitutive models were examined systematically against measured flow stresses by validation methods. The predicted material constants were found to be quite accurate compared to the actual material constants. However, notable mismatches between measured and predicted flow stresses were revealed by the proposed validation techniques, which carry out validation with not the entire, but a single tensile test case.

Variance analysis for model updating with a finite element based subspace fitting approach

Recently, a subspace fitting approach has been proposed for vibration-based finite element model updating. The approach makes use of subspace-based system identification, where the extended observability matrix is estimated from vibration measurements. Finite element model updating is performed by correlating the model-based observability matrix with the estimated one, by using a single set of experimental data. Hence, the updated finite element model only reflects this single test case. However, estimates from vibration measurements are inherently exposed to uncertainty due to unknown excitation, measurement noise and finite data length. In this paper, a covariance estimation procedure for the updated model parameters is proposed, which propagates the data-related covariance to the updated model parameters by considering a first-order sensitivity analysis. In particular, this propagation is performed through each iteration step of the updating minimization problem, by taking into account the covariance between the updated parameters and the data-related quantities. Simulated vibration signals are used to demonstrate the accuracy and practicability of the derived expressions. Furthermore, an application is shown on experimental data of a beam.

A user-oriented procedure for the commissioning and quality assurance testing of treatment planning system dosimetry in high-dose-rate brachytherapy

PurposeTo develop a user-oriented procedure for testing treatment planning system (TPS) dosimetry in high-dose-rate brachytherapy, with particular focus to TPSs using model-based dose calculation algorithms (MBDCAs). Methods and MaterialsIdentical plans were prepared for three computational models using two commercially available systems and the same 192Ir source. Reference dose distributions were obtained for each plan using the MCNP v.6.1 Monte Carlo (MC) simulation code with input files prepared via automatic parsing of plan information using a custom software tool. The same tool was used for the comparison of reference dose distributions with corresponding MBDCA exports. ResultsThe single source test case yielded differences due to the MBDCA spatial discretization settings. These affect points at relatively increased distance from the source, and they are abated in test cases with multiple source dwells. Differences beyond MC Type A uncertainty were also observed very close to the source(s), close to the test geometry boundaries, and within heterogeneities. Both MBDCAs studied were found equivalent to MC within 5 cm from the target volume for a clinical breast brachytherapy test case. These are in agreement with previous findings of MBDCA benchmarking in the literature. ConclusionsThe data and the tools presented in this work, that are freely available via the web, can serve as a benchmark for advanced clinical users developing their own tests, a complete commissioning procedure for new adopters of currently available TPSs using MBDCAs, a quality assurance testing tool for future updates of already installed TPSs, or as an admission prerequisite in multicentric clinical trials.

Generating test case chains for reactive systems

Testing of reactive systems is challenging because long input sequences are often needed to drive them into a state to test a desired feature. This is particularly problematic in on-target testing, where a system is tested in its real-life application environment and the amount of time required for resetting is high. This article presents an approach to discovering a test case chain—a single software execution that covers a group of test goals and minimizes overall test execution time. Our technique targets the scenario in which test goals for the requirements are given as safety properties. We give conditions for the existence and minimality of a single test case chain and minimize the number of test case chains if a single test case chain is infeasible. We report experimental results with our ChainCover tool for C code generated from Simulink models and compare it to state-of-the-art test suite generators.

Reduced-Order Modeling of Unsteady Aerodynamics Across Multiple Mach Regimes

A reduced-order model for unsteady aerodynamic calculations across a range of Mach regimes based on linear convolution and a nonlinear correction factor is developed. Separate investigations are conducted for the sub-, trans-, and supersonic Mach regimes, and overall good results are seen when reduced-order model results are compared with full-order computational-fluid-dynamics solutions, though the reduced-order model errors tend to decrease as the Mach number increases. To assist reduced-order model construction, the method-of-segments simplified model has been created and tested throughout these same Mach regimes. Finally, a practical example of the reduced-order model’s applicability is presented by following a single test case from subsonic up through supersonic flight.

Sensitivity of Subjective Decisions in the GLUE Methodology for Quantifying the Uncertainty in the Flood Inundation Map for Seymour Reach in Indiana, USA

Generalized likelihood uncertainty estimation (GLUE) is one of the widely-used methods for quantifying uncertainty in flood inundation mapping. However, the subjective nature of its application involving the definition of the likelihood measure and the criteria for defining acceptable versus unacceptable models can lead to different results in quantifying uncertainty bounds. The objective of this paper is to perform a sensitivity analysis of the effect of the choice of likelihood measures and cut-off thresholds used in selecting behavioral and non-behavioral models in the GLUE methodology. By using a dataset for a reach along the White River in Seymour, Indiana, multiple prior distributions, likelihood measures and cut-off thresholds are used to investigate the role of subjective decisions in applying the GLUE methodology for uncertainty quantification related to topography, streamflow and Manning’s n. Results from this study show that a normal pdf produces a narrower uncertainty bound compared to a uniform pdf for an uncertain variable. Similarly, a likelihood measure based on water surface elevations is found to be less affected compared to other likelihood measures that are based on flood inundation area and width. Although the findings from this study are limited due to the use of a single test case, this paper provides a framework that can be utilized to gain a better understanding of the uncertainty while applying the GLUE methodology in flood inundation mapping.

A sound and complete abstraction for reasoning about parallel prefix sums

Prefix sums are key building blocks in the implementation of many concurrent software applications, and recently much work has gone into efficiently implementing prefix sums to run on massively parallel graphics processing units (GPUs). Because they lie at the heart of many GPU-accelerated applications, the correctness of prefix sum implementations is of prime importance. We introduce a novel abstraction, the interval of summations, that allows scalable reasoning about implementations of prefix sums. We present this abstraction as a monoid, and prove a soundness and completeness result showing that a generic sequential prefix sum implementation is correct for an array of length $n$ if and only if it computes the correct result for a specific test case when instantiated with the interval of summations monoid. This allows correctness to be established by running a single test where the input and result require O(n lg(n)) space. This improves upon an existing result by Sheeran where the input requires O(n lg(n)) space and the result O(n 2 \lg(n)) space, and is more feasible for large n than a method by Voigtlaender that uses O(n) space for the input and result but requires running O(n 2 ) tests. We then extend our abstraction and results to the context of data-parallel programs, developing an automated verification method for GPU implementations of prefix sums. Our method uses static verification to prove that a generic prefix sum implementation is data race-free, after which functional correctness of the implementation can be determined by running a single test case under the interval of summations abstraction. We present an experimental evaluation using four different prefix sum algorithms, showing that our method is highly automatic, scales to large thread counts, and significantly outperforms Voigtlaender's method when applied to large arrays.

Efficient Path Selection Strategy Based on Static Analysis for Regression Testing

the changes made to the software system for enhancement. The most common approach to this problem is simply execute the existing test cases in the test suite; i.e. retest all approach, a systematically selected subset is chosen to be reuse, then substantial resources will be saved, due to the limited size of the selected test data (5), (6). There are distinctions between classes of techniques of regression testing: Test suites minimization techniques can be used to reduce test suite by eliminating redundant test suites. In safe approaches, every test case is selected that exercised any program element which could be affected by a program modification. In coverage approaches, test cases are selected which met some structural criteria. In this approach, select a single test case satisfying each coverage requirement introduced by the criterion, minimization techniques work like coverage approaches (7).