scholarly journals Parallel Testing in Behavior Driven Development

2021 ◽  
Author(s):  
Felipe De Amorim ◽  
Rodolfo Adamshuk Silva ◽  
Lincoln Costa ◽  
Francisco Carlos Souza
Keyword(s):  
Parallel Execution ◽  
Computational Time ◽  
Test Cases ◽  
Parallel Testing ◽  
Complete Automation ◽  
Test Scenarios

The testing process consists of activities that demand efforts asproducing, executing, and validating test scenarios. Covering alltest scenarios manually is unfeasible since it is error-prone andlabor-expensive. Thereby, partial or complete automation reducescosts and increases tests’ effectiveness. The increasing availabilityof hardware resources provides opportunities to scale testingusing parallel execution of test cases or suites blocks. Some toolsperform parallel execution of tests, but their use requires complicatedsettings, and when combined with some methodologies asBehavior-Driven Development, it may create an overhead for users.This paper presents the Multi-Threaded Testing (MTT) tool for parallelexecution of test scenarios in the context of Behavior-DrivenDevelopment that aims to reduce the computational time requiredto test Java projects. Furthermore, the present paper reports anexperimental study to evaluate the MTT tool’s performance intwo different hardware configurations. Our results demonstrate theMTT reached a speedup of 4,59 using ten threads in CPU Intel Corei5-9300H with an efficiency of 46%, and a speedup of 3,45 with anefficiency of 43% using eight threads in CPU Intel Core i7-7700HQ.

VENDOR-NEUTRAL STOCHASTIC INVERSION OF LWD DEEP AZIMUTHAL RESISTIVITY DATA AS A STEP TOWARD EFFICIENCY STANDARDIZATION OF GEOSTEERING SERVICES

2021 ◽  
Author(s):  
Mikhail Sviridov ◽  
◽  
Anton Mosin ◽  
Sergey Lebedev ◽  
Ron Thompson ◽  
...  
Keyword(s):  
Markov Chains ◽  
Quality Indicators ◽  
Parallel Execution ◽  
Oil Field ◽  
Model Complexity ◽  
Computational Time ◽  
Model Parameters ◽  
Formation Model ◽  
Inversion Algorithm ◽  
Azimuthal Resistivity

While proactive geosteering, special inversion algorithms are used to process the readings of logging-while-drilling resistivity tools in real-time and provide oil field operators with formation models to make informed steering decisions. Currently, there is no industry standard for inversion deliverables and corresponding quality indicators because major tool vendors develop their own device-specific algorithms and use them internally. This paper presents the first implementation of vendor-neutral inversion approach applicable for any induction resistivity tool and enabling operators to standardize the efficiency of various geosteering services. The necessity of such universal inversion approach was inspired by the activity of LWD Deep Azimuthal Resistivity Services Standardization Workgroup initiated by SPWLA Resistivity Special Interest Group in 2016. Proposed inversion algorithm utilizes a 1D layer-cake formation model and is performed interval-by-interval. The following model parameters can be determined: horizontal and vertical resistivities of each layer, positions of layer boundaries, and formation dip. The inversion can support arbitrary deep azimuthal induction resistivity tool with coaxial, tilted, or orthogonal transmitting and receiving antennas. The inversion is purely data-driven; it works in automatic mode and provides fully unbiased results obtained from tool readings only. The algorithm is based on statistical reversible-jump Markov chain Monte Carlo method that does not require any predefined assumptions about the formation structure and enables searching of models explaining the data even if the number of layers in the model is unknown. To globalize search, the algorithm runs several Markov chains capable of exchanging their states between one another to move from the vicinity of local minimum to more perspective domain of model parameter space. While execution, the inversion keeps all models it is dealing with to estimate the resolution accuracy of formation parameters and generate several quality indicators. Eventually, these indicators are delivered together with recovered resistivity models to help operators with the evaluation of inversion results reliability. To ensure high performance of the inversion, a fast and accurate semi-analytical forward solver is employed to compute required responses of a tool with specific geometry and their derivatives with respect to any parameter of multi-layered model. Moreover, the reliance on the simultaneous evolution of multiple Markov chains makes the algorithm suitable for parallel execution that significantly decreases the computational time. Application of the proposed inversion is shown on a series of synthetic examples and field case studies such as navigating the well along the reservoir roof or near the oil-water-contact in oil sands. Inversion results for all scenarios confirm that the proposed algorithm can successfully evaluate formation model complexity, recover model parameters, and quantify their uncertainty within a reasonable computational time. Presented vendor-neutral stochastic approach to data processing leads to the standardization of the inversion output including the resistivity model and its quality indicators that helps operators to better understand capabilities of tools from different vendors and eventually make more confident geosteering decisions.

scholarly journals Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design

2019 ◽  
Author(s):  
Simon Johansson ◽  
Oleksii Ptykhodko ◽  
Josep Arús-Pous ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Research Area ◽  
Computational Time ◽  
Test Cases ◽  
Sequential Data ◽  
Network Architectures ◽  
De Novo Molecular Design ◽  
Molecule Design ◽  
The Cost

In recent years, deep learning for de novo molecular generation has become a rapidly growing research area. Recurrent neural networks (RNN) using the SMILES molecular representation is one of the most common approaches used. Recent study shows that the differentiable neural computer (DNC) can make considerable improvement over the RNN for modeling of sequential data. In the current study, DNC has been implemented as an extension to REINVENT, an RNN-based model that has already been used successfully to make de novo molecular design. The model was benchmarked on its capacity to learn the SMILES language on the GDB-13 and MOSES datasets. The DNC shows improvement on all test cases conducted at the cost of significantly increased computational time and memory consumption.

A Synchronized Test Control Execution Model of Distributed Systems

2021 ◽  
pp. 1377-1395
Author(s):  
Salma Azzouzi ◽  
Sara Hsaini ◽  
My El Hassan Charaf
Keyword(s):  
Test Cases ◽  
Agent Based ◽  
Parallel Testing ◽  
Test Execution ◽  
Support Interaction ◽  
Distributed Testing ◽  
Implementation Under Test ◽  
Execution Model ◽  
Local Test ◽  
Control And Synchronization

Conformance testing may be seen as mean to execute an IUT (implementation under test) by carrying out test cases in order to observe whether the behavior of the IUT is conforming to its specifications. However, the development of distributed testing frameworks is more complex and the implementation of the parallel testing components (PTCs) should take into consideration the mechanisms and functions required to support interaction during PTC communication. In this article, the authors present another way to control the test execution of PTCs by introducing synchronization messages into the local test sequences. Then, they suggest an agent-based simulation to implement synchronized local test sequences and resolve the problem of control and synchronization.

scholarly journals Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1243 ◽  
Author(s):  
Riccardo Cecchetti ◽  
Francesco de Paulis ◽  
Carlo Olivieri ◽  
Antonio Orlandi ◽  
Markus Buecker
Keyword(s):  
Genetic Algorithm ◽  
Power Delivery ◽  
Computational Time ◽  
Test Cases ◽  
Experimental Measurements ◽  
New Approach ◽  
Power Delivery Network ◽  
Capacitor Placement ◽  
Minimum Number ◽  
Artificial Neural Network Ann

An iterative optimization for decoupling capacitor placement on a power delivery network (PDN) is presented based on Genetic Algorithm (GA) and Artificial Neural Network (ANN). The ANN is first trained by an appropriate set of results obtained by a commercial simulator. Once the ANN is ready, it is used within an iterative GA process to place a minimum number of decoupling capacitors for minimizing the differences between the input impedance at one or more location, and the required target impedance. The combined GA–ANN process is shown to effectively provide results consistent with those obtained by a longer optimization based on commercial simulators. With the new approach the accuracy of the results remains at the same level, but the computational time is reduced by at least 30 times. Two test cases have been considered for validating the proposed approach, with the second one also being compared by experimental measurements.

scholarly journals Two-Dimensional Evaluation of ATHAM-Fluidity, a Nonhydrostatic Atmospheric Model Using Mixed Continuous/Discontinuous Finite Elements and Anisotropic Grid Optimization

2016 ◽  
Vol 144 (11) ◽  
pp. 4349-4372 ◽  
Author(s):  
Julien Savre ◽  
James Percival ◽  
Michael Herzog ◽  
Chris Pain
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Atmospheric Model ◽  
Computational Time ◽  
Limited Area ◽  
Test Cases ◽  
Two Dimensional ◽  
Element Discretization ◽  
Wide Range ◽  
Grid Optimization

Abstract This paper presents the first attempt to apply the compressible nonhydrostatic Active Tracer High-Resolution Atmospheric Model–Fluidity (ATHAM-Fluidity) solver to a series of idealized atmospheric test cases. ATHAM-Fluidity uses a hybrid finite-element discretization where pressure is solved on a continuous second-order grid while momentum and scalars are computed on a first-order discontinuous grid (also known as ). ATHAM-Fluidity operates on two- and three-dimensional unstructured meshes, using triangular or tetrahedral elements, respectively, with the possibility to employ an anisotropic mesh optimization algorithm for automatic grid refinement and coarsening during run time. The solver is evaluated using two-dimensional-only dry idealized test cases covering a wide range of atmospheric applications. The first three cases, representative of atmospheric convection, reveal the ability of ATHAM-Fluidity to accurately simulate the evolution of large-scale flow features in neutral atmospheres at rest. Grid convergence without adaptivity as well as the performances of the Hermite–Weighted Essentially Nonoscillatory (Hermite-WENO) slope limiter are discussed. These cases are also used to test the grid optimization algorithm implemented in ATHAM-Fluidity. Adaptivity can result in up to a sixfold decrease in computational time and a fivefold decrease in total element number for the same finest resolution. However, substantial discrepancies are found between the uniform and adapted grid results, thus suggesting the necessity to improve the reliability of the approach. In the last three cases, corresponding to atmospheric gravity waves with and without orography, the model ability to capture the amplitude and propagation of weak stationary waves is demonstrated. This work constitutes the first step toward the development of a new comprehensive limited area atmospheric model.

The Development and Application of a Time-Domain Harmonic Balance Flow Solver

2012 ◽  
Author(s):  
Pengcheng Du ◽  
Fangfei Ning
Keyword(s):  
Time Domain ◽  
Harmonic Balance ◽  
Unsteady Flows ◽  
Harmonic Balance Method ◽  
Balance Method ◽  
Computational Time ◽  
Test Cases ◽  
Flow Solver ◽  
The Time Domain ◽  
Time Periodic

Time periodic unsteady flows are often encountered in turbomachinery. Simulating such flows using conventional time marching approach is very time-consuming and hence expensive. To handle this problem, several Fourier-based reduced order models have been developed recently. Among these, the time-domain harmonic balance method solves the governing equations purely in the time domain and there is also no need for the turbulence model to be linearized, making it easy to be implemented in an existing RANS code. Thus, the time-domain harmonic balance method was chosen and incorporated into an in-house Navier-Stokes flow solver. Several test cases were performed for the validations of the developed code. They cover standard unsteady test cases such as the low speed vortex shedding cylinder flow and the Sajben transonic diffuser under periodically oscillating back pressure. Further, two different practical turbomachinery unsteady flows were considered. One is a transonic fan under circumferential inlet distortion and the other is the rotor-stator interactions in a single stage compressor. The results illustrate the capability of the harmonic balance method in capturing the dominant nonlinear effects. The number of harmonics should be retained in the harmonic balance method is depend on the strength of the nonlinear unsteady effects and differs from case to case. With appropriate number of harmonics retained, it can resolve the unsteady flow field satisfactory, meanwhile, reducing the computational time significantly. In a word, the harmonic balance method promise to be an effective way to simulate time periodic unsteady flows.

The Evaluation of an Integral Equation Method for Two-Dimensional Shock-Free Flows

1975 ◽  
Vol 26 (1) ◽  
pp. 59-70 ◽  
Author(s):  
D Nixon ◽  
J Patel
Keyword(s):  
Integral Equation ◽  
Integral Equation Method ◽  
Free Flow ◽  
Equation Method ◽  
Computational Time ◽  
Test Cases ◽  
Two Dimensional ◽  
Exact Methods ◽  
Wide Range ◽  
Steady Shock

SummaryThe numerical aspects of the integral equation method developed by Nixon and Hancock for two-dimensional steady shock-free flow have been rationalised; this numerically refined method is evaluated by calculating the pressure distribution around a wide range of aerofoils. These test cases include aerofoils in supercritical shock-free flow as well as subcritical flow and exact solutions are available for comparison. The computational time in the present method is significantly less than that required by the exact methods. The present results compare satisfactorily with the exact results.

scholarly journals Inherently Conservative Nonpolynomial-Based Remapping Schemes: Application to Semi-Lagrangian Transport

2008 ◽  
Vol 136 (12) ◽  
pp. 5044-5061 ◽  
Author(s):  
Matthew R. Norman ◽  
Ramachandran D. Nair
Keyword(s):  
Sine Wave ◽  
Computational Time ◽  
Test Case ◽  
Test Cases ◽  
Error Norm ◽  
Nonsmooth Data ◽  
Local Extrema ◽  
Piecewise Parabolic ◽  
Efficient Sequence ◽  
Hyperbolic Method

Abstract A group of new conservative remapping schemes based on nonpolynomial approximations is proposed. The remapping schemes rely on the conservative cascade scheme (CCS), which employs an efficient sequence of 1D remapping operations to solve a multidimensional problem. The present study adapts three new nonpolynomial-based reconstructions of subgrid variation to the CCS: the Piecewise Hyperbolic Method (PHM), the Piecewise Double Hyperbolic Method (PDHM), and the Piecewise Rational Method (PRM) for comparison with the baseline method: the Piecewise Parabolic Method (PPM). Additionally, an adaptive hybrid approximation scheme, PPM-Hybrid (PPM-H), is constructed using monotonic PPM for smooth data and local extrema and using PHM for steep jumps where PPM typically suffers large accuracy degradation because of its original monotonic filter. Smooth and nonsmooth data profiles are transported in 1D, 2D Cartesian, and 2D spherical frameworks under uniform advection, solid-body rotation, and deformational flow. Accuracy is compared via the L1 global error norm. In general, PPM outperformed PHM, but when the majority of the error came from PPM degradation at sharp derivative changes (e.g., the vicinity near sine wave extrema), PHM was more accurate. PRM performed very similarly to PPM for nonsmooth functions, but the order of convergence was worse than PPM for smoother data. PDHM performed the worst of all of the nonpolynomial methods for nearly every test case. PPM-H outperformed PPM and all of the nonpolynomial methods for all test cases in all geometries, offering a robust advantage in the CCS scheme with a negligible increase in computational time.

A Correlation-Based Transition Model Using Local Variables—Part II: Test Cases and Industrial Applications

2004 ◽  
Vol 128 (3) ◽  
pp. 423-434 ◽  
Author(s):  
R. B. Langtry ◽  
F. R. Menter ◽  
S. R. Likki ◽  
Y. B. Suzen ◽  
P. G. Huang ◽  
...  
Keyword(s):  
Guide Vane ◽  
Current Model ◽  
Mathematical Formulation ◽  
Low Pressure ◽  
Transport Equations ◽  
Parallel Execution ◽  
Test Cases ◽  
Transition Model ◽  
Compressor Cascade ◽  
Low Pressure Turbine

A new correlation-based transition model has been developed, which is built strictly on local variables. As a result, the transition model is compatible with modern computational fluid dynamics (CFD) methods using unstructured grids and massive parallel execution. The model is based on two transport equations, one for the intermittency and one for the transition onset criteria in terms of momentum thickness Reynolds number. The proposed transport equations do not attempt to model the physics of the transition process (unlike, e.g., turbulence models), but form a framework for the implementation of correlation-based models into general-purpose CFD methods. Part I of this paper (Menter, F. R., Langtry, R. B., Likki, S. R., Suzen, Y. B., Huang, P. G., and Völker, S., 2006, ASME J. Turbomach., 128(3), pp. 413–422) gives a detailed description of the mathematical formulation of the model and some of the basic test cases used for model validation. Part II (this part) details a significant number of test cases that have been used to validate the transition model for turbomachinery and aerodynamic applications, including the drag crisis of a cylinder, separation-induced transition on a circular leading edge, and natural transition on a wind turbine airfoil. Turbomachinery test cases include a highly loaded compressor cascade, a low-pressure turbine blade, a transonic turbine guide vane, a 3D annular compressor cascade, and unsteady transition due to wake impingement. In addition, predictions are shown for an actual industrial application, namely, a GE low-pressure turbine vane. In all cases, good agreement with the experiments could be achieved and the authors believe that the current model is a significant step forward in engineering transition modeling.

A Correlation-Based Transition Model Using Local Variables: Part I — Model Formulation

2004 ◽  
Author(s):  
F. R. Menter ◽  
R. B. Langtry ◽  
S. R. Likki ◽  
Y. B. Suzen ◽  
P. G. Huang ◽  
...  
Keyword(s):  
Mathematical Formulation ◽  
Turbulence Models ◽  
Transition Process ◽  
General Purpose ◽  
Transport Equations ◽  
Parallel Execution ◽  
Test Cases ◽  
Transition Model ◽  
Significant Step ◽  
Transition Modeling

A new correlation-based transition model has been developed, which is based strictly on local variables. As a result, the transition model is compatible with modern CFD approaches such as unstructured grids and massive parallel execution. The model is based on two transport equations, one for intermittency and one for the transition onset criteria in terms of momentum thickness Reynolds number. The proposed transport equations do not attempt to model the physics of the transition process (unlike e.g. turbulence models), but form a framework for the implementation of correlation-based models into general-purpose CFD methods. Part I (this part) of this paper gives a detailed description of the mathematical formulation of the model and some of the basic test cases used for model validation, including a 2-D turbine blade. Part II of the paper details a significant number of test cases that have been used to validate the transition model for turbomachinery and aerodynamic applications. The authors believe that the current formulation is a significant step forward in engineering transition modeling, as it allows the combination of correlation-based transition models with general purpose CFD codes.

Sign in / Sign up

Export Citation Format

Share Document