Journal articles: 'Testing and simulation'

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Lankford, Philip M. "Testing Simulation Models." Geographical Analysis 6, no. 3 (September 3, 2010): 295–302. http://dx.doi.org/10.1111/j.1538-4632.1974.tb00514.x.

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NAKA, Tetsuo. "Material Testing and Simulation." Journal of the Japan Society for Technology of Plasticity 57, no. 669 (2016): 950–54. http://dx.doi.org/10.9773/sosei.57.950.

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Bersky, Anna K., June Krawczak, and Tara D. Kumar. "Computerized Clinical Simulation Testing." Nurse Educator 23, no. 1 (January 1998): 20–25. http://dx.doi.org/10.1097/00006223-199801000-00010.

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Stannett, Mike. "Simulation testing of automata." Formal Aspects of Computing 18, no. 1 (January 24, 2006): 31–41. http://dx.doi.org/10.1007/s00165-005-0080-y.

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Lassche, Madeline, and Barbara Wilson. "Transcending Competency Testing in Hospital-Based Simulation." AACN Advanced Critical Care 27, no. 1 (February 1, 2016): 96–102. http://dx.doi.org/10.4037/aacnacc2016952.

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Simulation is a frequently used method for training students in health care professions and has recently gained acceptance in acute care hospital settings for use in educational programs and competency testing. Although hospital-based simulation is currently limited primarily to use in skills acquisition, expansion of the use of simulation via a modified Quality Health Outcomes Model to address systems factors such as the physical environment and human factors such as fatigue, reliance on memory, and reliance on vigilance could drive system-wide changes. Simulation is an expensive resource and should not be limited to use for education and competency testing. Well-developed, peer-reviewed simulations can be used for environmental factors, human factors, and interprofessional education to improve patients’ outcomes and drive system-wide change for quality improvement initiatives.

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Shao, Guo Dong, Swee Leong, and Charles McLean. "Simulation-Based Manufacturing Interoperability Standards and Testing." Key Engineering Materials 407-408 (February 2009): 283–86. http://dx.doi.org/10.4028/www.scientific.net/kem.407-408.283.

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Software applications for manufacturing systems developed using software from different vendors typically cannot work together. Develop¬ment of custom integrations of manufacturing software incurs costs and delays that hurt industry productivity and competitiveness. Software applications need to be tested in live operational systems. It is impractical to use real industrial systems to support dynamic interoperability test¬ing and research due to: 1) access issues - manu¬facturing facilities are not open to outsiders, as proprietary data and processes may be compro¬mised; 2) technical issues - operational systems are not instrumented to support testing; and 3) cost issues - productivity suffers when actual production systems are taken offline to allow testing. Publicly available simulations do not exist to demonstrate simulation integration issues, validate potential standards solu¬tions, or dynamically test the interoperability of simulation systems and other software applica¬tions. A new, dynamic, simulation-based interoperability testing facility for manufacturing software applications is being developed at the National Institute of Standards and Technology (NIST).

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Ray, L. Bryan. "Testing biochemical data by simulation." Science 369, no. 6502 (July 23, 2020): 387.10–389. http://dx.doi.org/10.1126/science.369.6502.387-j.

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Joneson, Eric. "Trends in Distribution Simulation Testing." International Journal of Advanced Packaging Technology 2, no. 1 (February 13, 2014): 70–74. http://dx.doi.org/10.23953/cloud.ijapt.16.

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Steffelbauer, Markus. "Efficient Diagnostic Testing with Simulation." ATZelectronics worldwide 17, no. 7-8 (July 2022): 14–17. http://dx.doi.org/10.1007/s38314-022-0791-3.

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Bateman, Vesta I. "Pyroshock testing—shock simulation facilities." Journal of the Acoustical Society of America 111, no. 5 (2002): 2381. http://dx.doi.org/10.1121/1.4778079.

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Rinta-Valkama, Jarno, Martti Välisuo, Tommi Karhela, Pasi Laakso, and Matti Paljakka. "Simulation Aided Process Automation Testing." IFAC Proceedings Volumes 33, no. 24 (September 2000): 277–80. http://dx.doi.org/10.1016/s1474-6670(17)36905-7.

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Loh, Wei-Yin. "Testing multivariate normality by simulation." Journal of Statistical Computation and Simulation 26, no. 3-4 (December 1986): 243–52. http://dx.doi.org/10.1080/00949658608810966.

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Nasonov, V. V., V. A. Miroshnichenko, and A. B. Boitsov. "Simulation stand for explosion testing." Chemical and Petroleum Engineering 21, no. 7 (July 1985): 345–47. http://dx.doi.org/10.1007/bf01149664.

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Maunder, Colin. "Digital logic testing and simulation." Microprocessors and Microsystems 10, no. 10 (December 1986): 564. http://dx.doi.org/10.1016/0141-9331(86)90080-3.

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Unger, Nicholas R. "Testing the Untestable: Mitigating Simulation Bias During Summative Usability Testing." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (September 2020): 142–44. http://dx.doi.org/10.1177/2327857920091058.

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A literature review was conducted on the topic of sources of simulation bias, as it applies to test design and use scenario creation for simulated-use studies, on medical devices for FDA submission. When it comes to Summative Usability Testing, there isn’t room for simulation bias to impact the data collected. From working with clinicians to design more realistic/appropriate clinical scenarios, to traveling to medical simulation labs to set up a realistic operating room simulation, we, as researchers, are constantly learning and improving our testing designs to ensure that they are as realistic as possible. This poster will look at current research and study logistics to provide best practices for identifying common sources of simulation bias and mitigating those sources during your Summative Evaluation.

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Záruba, Petr, Jakub Jelínek, and Michal Kalinský. "Dynamic Testing of Buses and their Components." Journal of Middle European Construction and Design of Cars 15, no. 1 (June 27, 2017): 6–10. http://dx.doi.org/10.1515/mecdc-2017-0002.

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Abstract The article gives an overview of a virtual simulation method under ECE Regulation No. R66 - bus rollover. The first part of the article introduces the process of virtual simulations in terms of homologation. The conclusion is focused on the correlation of physical tests with virtual simulations.

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You, Shawn, X. Shawn Gao, and Arlin Nelson. "Breaking the Testing Pyramid with Virtual Testing and Hybrid Simulation." Fatigue of Aircraft Structures 2019, no. 11 (December 1, 2019): 1–10. http://dx.doi.org/10.2478/fas-2019-0001.

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AbstractVirtual testing and hybrid simulation have become an important trend in airplane design and validation. The traditional Testing Pyramid (or Building Block) approaches that emphasis on uniaxial coupon test and full structure certification test are being challenged. Researchers are trying to use advanced testing and simulation methods to replace the Testing Pyramid approach.Before physical testing, virtual testing can be conducted to simulate the physical test. Virtual model of the full testing system including controller, actuators, and fixtures can be constructed and validated. In this work, an example has been developed and validated to show the potentials of the virtual testing process.Hybrid simulation is an approach of analyzing an analysis model and physical structure integrated system under realistic loading conditions. Hybrid simulation combines the lab testing with numerical analysis to explore the benefits of both methodologies. In this study, a hybrid simulation for a simplified airplane wing was conducted to demonstrate the process.Virtual testing and hybrid simulation are alternative methods of Testing Pyramid approach. Full scale tests are still required for certification but the more that is known about the test article, the greater chances of success in the full-scale certification testing.

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Fussell, Stephanie G., and Michelle P. Hight. "Usability Testing of a VR Flight Training Program." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 1124–28. http://dx.doi.org/10.1177/1071181321651096.

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Virtual reality (VR) flight training programs are being developed as a low-cost, highly realistic training option and tested to ensure user expectations for skills mastery are met. A usability pilot study was conducted in two experimental courses comparing the training effectiveness of 2D and VR simulation for flight training at a university in the Southwestern United States. The results indicated that system usability was significantly higher in the VR group. There was no significant difference between groups for perceived workload nor user experience. Although both groups reported low symptoms of simulator sickness, users in the VR group reported significantly higher levels of eye strain. Both groups found the simulations to be enjoyable and several users stated that the simulations were beneficial for learning flight maneuvers. The results of the pilot study demonstrated that overall, VR simulation is similar to 2D simulation for flight training in terms of usability and user satisfaction.

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Sierra, Alonzo, Cihan Gercek, Stefan Übermasser, and Angèle Reinders. "Simulation-Supported Testing of Smart Energy Product Prototypes." Applied Sciences 9, no. 10 (May 17, 2019): 2030. http://dx.doi.org/10.3390/app9102030.

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Smart energy products and services (SEPS) have a key role in the development of smart grids, and testing methods such as co-simulation and scenario-based simulations can be useful tools for evaluating the potential of new SEPS concepts during their early development stages. Three innovative conceptual designs for home energy management products (HEMPs)—a specific category of SEPS—were successfully tested using a simulation environment, validating their operation using simulated production and load profiles. For comparison with reality, end user tests were carried out on two of the HEMP concepts and showed mixed results for achieving more efficient energy use, with one of the concepts reducing energy consumption by 27% and the other increasing it by 25%. The scenario-based simulations provided additional insights on the performance of these products, matching some of the general trends observed during end user tests but failing to sufficiently approximate the observed results. Overall, the presented testing methods successfully evaluated the performance of HEMPs under various use conditions and identified bottlenecks, which could be improved in future designs. It is recommended that in addition to HEMPs, these tests are repeated with different SEPS and energy systems to enhance the robustness of the methods.

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Kobayashi, Shigeyuki, David P. Stoten, Yoshitaka Yamashita, and Takayuki Usuda. "Dynamically substructured testing of railway pantograph/catenary systems." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 233, no. 5 (October 4, 2018): 516–25. http://dx.doi.org/10.1177/0954409718799900.

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This paper presents a methodology for testing railway pantograph/catenary systems based upon the dynamically substructured system approach for combined physical and numerical components, originally developed by Stoten and Hyde. The main advantage of the dynamically substructured system is that it can provide more stable substructured testing than alternative schemes, such as the commonly used hybrid simulation method, often referred to as hardware-in-the-loop simulation. The developed method is validated through experiments using a simple pantograph rig, together with a numerical simulation of the catenary. In order to realise a real-time simulation of the large catenary model, for the first time in dynamically substructured system testing this study uses (i) a modal analysis technique to reduce the dimension of the contact wire model and (ii) a moving window approach to represent long-distance travel of the pantograph. Finally, the experimental dynamically substructured system test results are compared with simulations of the benchmark pantograph/catenary emulated system.

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Bosmans, Stig, Siegfried Mercelis, Joachim Denil, and Peter Hellinckx. "Testing IoT systems using a hybrid simulation based testing approach." Computing 101, no. 7 (August 3, 2018): 857–72. http://dx.doi.org/10.1007/s00607-018-0650-5.

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Rascati, Ralph J. "A Paternity Testing Laboratory Simulation Exercise." American Biology Teacher 64, no. 3 (March 1, 2002): 212–18. http://dx.doi.org/10.2307/4451279.

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Rascati, Ralph J. "A Paternity Testing Laboratory Simulation Exercise." American Biology Teacher 64, no. 3 (March 2002): 212–18. http://dx.doi.org/10.1662/0002-7685(2002)064[0212:aptlse]2.0.co;2.

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Castejon, Luis, Antonio Miravete, and Jesus Cuartero. "Composite bus rollover simulation and testing." International Journal of Heavy Vehicle Systems 13, no. 4 (2006): 281. http://dx.doi.org/10.1504/ijhvs.2006.010584.

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Šomodi, Željko, Anica Hursa, and Dubravko Rogale. "Numerical simulation of textile flexibility testing." International Journal of Clothing Science and Technology 15, no. 3/4 (June 2003): 276–83. http://dx.doi.org/10.1108/09556220310478387.

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Liaw, Bor Yann, Keith P. Bethune, and Xiao Guang Yang. "Advanced integrated battery testing and simulation." Journal of Power Sources 110, no. 2 (August 2002): 330–40. http://dx.doi.org/10.1016/s0378-7753(02)00195-7.

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Silvestre, Nuno, and Leroy Gardner. "Steel Structures: Mechanics, Simulation and Testing." Structures 4 (November 2015): 1. http://dx.doi.org/10.1016/j.istruc.2015.10.009.

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Lu, Maozu, Grayham E. Mizon, and Chiara Monfardini. "Simulation Encompassing: Testing Non-nested Hypotheses*." Oxford Bulletin of Economics and Statistics 70 (December 2008): 781–806. http://dx.doi.org/10.1111/j.1468-0084.2008.00530.x.

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KLEMEŠ, V. "Operational testing of hydrological simulation models." Hydrological Sciences Journal 31, no. 1 (March 1986): 13–24. http://dx.doi.org/10.1080/02626668609491024.

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French, M. "AN INTRODUCTION TO ROAD SIMULATION TESTING." Experimental Techniques 24, no. 3 (May 2000): 37–38. http://dx.doi.org/10.1111/j.1747-1567.2000.tb00910.x.

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Fedorov, V. A., L. I. Chesakov, and M. N. Shafranovskii. "Simulation testing of acoustic level meters." Measurement Techniques 30, no. 7 (July 1987): 670–72. http://dx.doi.org/10.1007/bf00865228.

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Singh, Abhishek, Jim Plusquellic, Dhananjay Phatak, and Chintan Patel. "Defect Simulation Methodology for iDDT Testing." Journal of Electronic Testing 22, no. 3 (June 2006): 255–72. http://dx.doi.org/10.1007/s10836-006-9318-8.

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Beltz-Mohrmann, Gillian D., Andreas A. Berlind, and Adam O. Szewciw. "Testing the accuracy of halo occupation distribution modelling using hydrodynamic simulations." Monthly Notices of the Royal Astronomical Society 491, no. 4 (December 6, 2019): 5771–88. http://dx.doi.org/10.1093/mnras/stz3442.

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ABSTRACT Halo models provide a simple and computationally inexpensive way to investigate the connection between galaxies and their dark matter haloes. However, these models rely on the assumption that the role of baryons can easily be parametrized in the modelling procedure. We aim to examine the ability of halo occupation distribution (HOD) modelling to reproduce the galaxy clustering found in two different hydrodynamic simulations, Illustris and EAGLE. For each simulation, we measure several galaxy clustering statistics on two different luminosity threshold samples. We then apply a simple five parameter HOD, which was fit to each simulation separately, to the corresponding dark matter-only simulations, and measure the same clustering statistics. We find that the halo mass function is shifted to lower masses in the hydrodynamic simulations, resulting in a galaxy number density that is too high when an HOD is applied to the dark matter-only simulation. However, the exact way in which baryons alter the mass function is remarkably different in the two simulations. After applying a correction to the halo mass function in each simulation, the HOD is able to accurately reproduce all clustering statistics for the high luminosity sample of galaxies. For the low luminosity sample, we find evidence that in addition to correcting the halo mass function, including spatial, velocity, and assembly bias parameters in the HOD is necessary to accurately reproduce clustering statistics.

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Ramos Silva, António, Mário Vaz, Sofia Leite, and Joaquim Mendes. "Lock-In Thermal Test Simulation, Influence, and Optimum Cycle Period for Infrared Thermal Testing in Non-Destructive Testing." Sensors 23, no. 1 (December 28, 2022): 325. http://dx.doi.org/10.3390/s23010325.

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Lock-in thermal tests (LTTs) are one of the best ways to detect defects in composite materials. The parameter that most affects their performance is the cycle period of the stimulation wave. Its influence on the amplitude-phase results was determined by performing various numeric simulations and laboratory tests. The laboratory tests were used to infer part of the simulation parameters, namely the input and output heat, corresponding to the stimulation and natural convection. The simulations and the analysis of their results focus on the heat flow inside the sample and the manner they change for different geometries. This was performed for poly(methyl methacrylate (PMMA) and carbon fiber-reinforced polymers (CFRPs). The simulation of these materials was also used to create prediction surfaces and equations. These predict the amplitude and phase for a sample with a thickness l and a cycle period. These new findings were validated with new laboratory tests and two new samples. These validated the prediction surfaces and equations and can now be used as a reference for future works and industrial applications.

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Zhang, Zhen Hua, Xue Mei Chen, and Li Gao. "Constitution of the Stationary Scene of the Traffic Testing Field of the Transportation Ministry." Applied Mechanics and Materials 198-199 (September 2012): 713–16. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.713.

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The paper gives the process of building the model of the Traffic Testing Field’s stationary scene which includes the acquisition and modifying of the texture, the building of the model and testing simulation. In the process, the model builder uses Geometry tool, Face tool, billboard tool and so on to build the model. Through testing and simulating, the model turns out to be a perfect simulation.

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Duggan, Ben, John Metzcar, and Paul Macklin. "DAPT: A package enabling distributed automated parameter testing." Gigabyte 2021 (June 4, 2021): 1–10. http://dx.doi.org/10.46471/gigabyte.22.

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Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches), as well as storing simulation data, requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster with the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) “database”, multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here, we describe DAPT and provide an example demonstrating its use.

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Liu, Zhi Guo, Jun Yu Li, and Xiu Li Ren. "Research on the Parameter Model Simulation Based on Computer Simulation Basketball Match." Advanced Materials Research 791-793 (September 2013): 1203–7. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1203.

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With the development of computer hardware and software, the simulation of computer virtual simulation has been rapid development. The running process of computer simulation platform need to deal with massive data, and in general software testing technology can not meet the requirements. On the basis of this, an application computer virtual platforms parallel test method is proposed. Firstly, this paper gives a brief introduction for the process of computer simulation, and then the testing method can carry out software programming, it will realize the virtual process of serial FIFO buffer and register through reading and writing function. In order to develop basketball virtual platform as an example, the use of Simulink modules in MATLAB compare the serial and parallel testing of two testing methods used by software debugging time. Finally, we find that the parallel test is the best scheme of computer virtual platform software test, to provide theoretical reference for the design of software testing process.

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Tyler, Neil. "Engineering Simulation." New Electronics 52, no. 9 (May 14, 2019): 21–22. http://dx.doi.org/10.12968/s0047-9624(22)61045-9.

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Dimig, J., C. Shield, C. French, F. Bailey, and A. Clark. "Effective Force Testing: A Method of Seismic Simulation for Structural Testing." Journal of Structural Engineering 125, no. 9 (September 1999): 1028–37. http://dx.doi.org/10.1061/(asce)0733-9445(1999)125:9(1028).

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Raska, Pavel, and Zdenek Ulrych. "Testing Optimization Methods on Discrete Event Simulation Models and Testing Functions." Procedia Engineering 69 (2014): 768–77. http://dx.doi.org/10.1016/j.proeng.2014.03.053.

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LI, HAO, ZHONG-LIANG ZHAO, and ZHAO-LIN FAN. "SIMULATION METHOD FOR WIND TUNNEL BASED VIRTUAL FLIGHT TESTING." International Journal of Modern Physics: Conference Series 19 (January 2012): 381–89. http://dx.doi.org/10.1142/s2010194512008975.

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The Wind Tunnel Based Virtual Flight Testing (WTBVFT) could replicate the actual free flight and explore the aerodynamics/flight dynamics nonlinear coupling mechanism during the maneuver in the wind tunnel. The basic WTBVFT concept is to mount the test model on a specialized support system which allows for the model freely rotational motion, and the aerodynamic loading and motion parameters are measured simultaneously during the model motion. The simulations of the 3-DOF pitching motion of a typical missile in the vertical plane are performed with the openloop and closed-loop control methods. The objective is to analyze the effect of the main differences between the WTBVFT and the actual free flight, and study the simulation method for the WTBVFT. Preliminary simulation analyses have been conducted with positive results. These results indicate that the WTBVFT that uses closed-loop autopilot control method with the pitch angular rate feedback signal is able to replicate the actual free flight behavior within acceptable differences.

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Ge, Jingwei, Huile Xu, Jiawei Zhang, Yi Zhang, Danya Yao, and Li Li. "Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing." Journal of Advanced Transportation 2022 (May 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/8655514.

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Virtual simulation-based testing of autonomous vehicles (AVs) needs massive challenging corner cases to reach high testing accuracy. Current methods achieve this goal by finding testing scenarios with low sampling frequency in the empirical distribution. However, these methods neglect modeling heterogeneous driving behavior, which actually is crucial for finding corner cases. To fill this gap, we propose an interpretable and operable method for sampling corner cases. Firstly, we initialize a testing scenario and allocate testing tasks to AV. Then, to simulate the variability in driving behaviors, we design utility functions with several hyperparameters and generate aggressive, conservative, and normal driving strategies by adjusting hyperparameters. By changing the heterogeneous driving behavior of surrounding vehicles (SVs), we can sample the challenging corner cases in the scenario. Finally, we conduct a series of simulation experiments in a typical lane-changing scenario. The simulation results reveal that by adjusting the occurrence frequency of heterogeneous SVs in the testing scenario, more corner cases can be found in limited rounds of simulations.

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Gnambs, Timo, and Bernad Batinic. "Polytomous Adaptive Classification Testing." Educational and Psychological Measurement 71, no. 6 (January 28, 2011): 1006–22. http://dx.doi.org/10.1177/0013164410393956.

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Computer-adaptive classification tests focus on classifying respondents in different proficiency groups (e.g., for pass/fail decisions). To date, adaptive classification testing has been dominated by research on dichotomous response formats and classifications in two groups. This article extends this line of research to polytomous classification tests for two- and three-group scenarios (e.g., inferior, mediocre, and superior proficiencies). Results of two simulation experiments with generated and real responses ( N = 2,000) to established personality scales of different length (12, 20, or 29 items) demonstrate that adaptive item presentations significantly reduce the number of items required to make such classification decisions while maintaining a consistent classification accuracy. Furthermore, the simulations highlight the importance of the selected test termination criterion, which has a significant impact on the average test length.

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Fu, Huijun, Jing Yang, and Shihui Liu. "Development of Track Superelevation Simulation Testing System." Journal of Physics: Conference Series 1748 (January 2021): 052021. http://dx.doi.org/10.1088/1742-6596/1748/5/052021.

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Usman, U., and Mochammad Apriyadi Hadi Sirad. "Characteristic Testing Of Simulation-Based Photovoltaic Models." IOP Conference Series: Materials Science and Engineering 1125, no. 1 (May 1, 2021): 012064. http://dx.doi.org/10.1088/1757-899x/1125/1/012064.

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Tseng, N. T., R. G. Pelle, J. P. Chang, and T. C. Warholic. "Finite Element Simulation of Destructive Tire Testing." Tire Science and Technology 19, no. 1 (January 1, 1991): 2–22. http://dx.doi.org/10.2346/1.2141706.

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Abstract Three destructive tire tests, burst pressure, high speed free rotation, and DOT plunger energy are performed to check the ultimate strength of new tires. These tests represent some of the extreme, although unusual, overload conditions that may be applied to a tire. They are used to determine how far above normal service conditions one might take a tire before it reaches its ultimate strength. A nonlinear incompressible rubber model and a nonlinear cord-rubber composite model were used in the tire analyses. Various rubber compounds as well as the rubber in the cord-rubber composite were modeled as nonlinear incompressible Mooney-Rivlin materials. The bimodulus cord and the cord angle change effect due to deformation were also considered. In addition, gap elements were used at the tire-rim interface and between tread grooves where required to provide appropriate boundary conditions. Numerical simulations of these destructive tire tests represent three excellent benchmarks to verify and to evaluate the robustness of a finite element code due to very large strain and deformation occurring in the tire. The numerical results predicted by the finite element tire models agreed very well with the available experimental data.

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Song Weihong, 宋伟红, 伍凡 Wu Fan, 侯溪 Hou Xi, and 杨鹏 Yang Peng. "Simulation analysis on absolute testing of spherical." High Power Laser and Particle Beams 23, no. 12 (2011): 3229–34. http://dx.doi.org/10.3788/hplpb20112312.3229.

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Shafer, Steven L., Lawrence C. Siegel, James E. Cooke, and James C. Scott. "Testing Computer-controlled Infusion Pumps by Simulation." Anesthesiology 68, no. 2 (February 1, 1988): 261–66. http://dx.doi.org/10.1097/00000542-198802000-00013.

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Sterman, John D. "Testing Behavioral Simulation Models by Direct Experiment." Management Science 33, no. 12 (December 1987): 1572–92. http://dx.doi.org/10.1287/mnsc.33.12.1572.

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Yamawaki, H. "Computer Simulation for Air-coupled Ultrasonic Testing." Journal of Physics: Conference Series 520 (June 3, 2014): 012019. http://dx.doi.org/10.1088/1742-6596/520/1/012019.

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Journal articles on the topic 'Testing and simulation'

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