Monte Carlo method for the reduction of measurement errors in the material parameter estimation with cavities

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronny Peter ◽  
Luca Bifano ◽  
Gerhard Fischerauer
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Errors ◽  
Input Data ◽  
Material Parameter ◽  
Cavity Resonator ◽  
Calculation Algorithm ◽  
The Monte Carlo Method ◽  
Electromagnetic Resonances

Abstract The quantitative determination of material parameter distributions in resonant cavities is a relatively new method for the real-time monitoring of chemical processes. For this purpose, electromagnetic resonances of the cavity resonator are used as input data for the reverse calculation (inversion). However, the reverse calculation algorithm is sensitive to disturbances of the input data, which produces measurement errors and tends to diverge, which leads to no measurement result at all. In this work a correction algorithm based on the Monte Carlo method is presented which ensures a convergent behavior of the reverse calculation algorithm.

scholarly journals Determination of distributions of fatigue crack length by Monte Carlo method

Mechanik ◽  
2017 ◽  
Vol 90 (7) ◽  
pp. 568-570
Author(s):  
Józef Drewniak ◽  
Leszek Hojdys
Keyword(s):  
Monte Carlo ◽  
Fatigue Crack ◽  
Monte Carlo Method ◽  
Fatigue Crack Growth ◽  
Crack Length ◽  
Crack Growth ◽  
Input Data ◽  
The Monte Carlo Method ◽  
Random Distributions

Described is the determination of random distributions of the fatigue crack length by the Monte Carlo method and the Bogdanov–Kozin model. Input data needed to determine the distributor were obtained by simulation of fatigue crack growth using the Paris–Erdogan model.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

Determination of Allowable Manipulator Link Shapes; and Task, Installation, and Obstacle Spaces Using the Monte Carlo Method

1993 ◽  
Vol 115 (3) ◽  
pp. 457-461 ◽  
Author(s):  
Q. Tu ◽  
J. Rastegar
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Shape Design ◽  
Task Space ◽  
Operating Environment ◽  
Design Task ◽  
The Monte Carlo Method ◽  
Manipulator Link ◽  
And Task

The Monte Carlo method is used to solve a number of manipulator link shape design, task space, and obstacle placement problems. The shape of links of manipulators that are to operate within geometrically specified enclosures are determined. Within the enclosure, one or several obstacles may be present. For a specified operating environment, the spaces within which a given manipulator may be installed in order to perform the required tasks are identified. For a given enclosure, the allowable task spaces, and regions within which obstacles may be placed are mapped. By defining weighted distributions for the task and/or obstacle spaces, weighted allowable link shapes, and task and obstacle spaces are determined. The information can be used for optimal link shape synthesis, and for optimal task, obstacle, and manipulator placement purposes. The developed methods are very simple, numeric in nature, and readily implemented on computer. Several examples are presented.

scholarly journals Akurasi dalam Memprediksi Penetapan Besaran Anggaran Proposal Pendapatan dan Belanja Universitas Menggunakan Metode Monte Carlo

2020 ◽  
pp. 60-66
Author(s):  
Yogo Turnandes ◽  
Yuhandri Yunus
Keyword(s):  
Higher Education ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Community Service ◽  
The Monte Carlo Method ◽  
Average Accuracy ◽  
Service Activities ◽  
High Level ◽  
The University

The Institute for Research and Community Service at the University of Lancang Kuning has the mandate in research and service activities which are the two dharmas of the Tri Dharma of Higher Education. The purpose of this study is to predict the determination of the budget amount for the University Income and Expenditure Budget (APBU) proposal approved at LPPM Unilak for the following year. Thus, it will make it easier for the LPPM leadership to make decisions on the acceptance of APBU proposals that are approved quickly and optimally. The data used in this research is APBU research and service proposal data approved in 2018 to 2020 which is processed using the monte carlo method. The APBU proposal budget prediction will be carried out every year. Based on the results of tests that have been carried out with the monte carlo method, it is found that the system used to predict the amount of APBU proposal budget approved in 2019 with an average accuracy of 84% and in 2020 with an average accuracy of 73%. Then with a fairly high level of accuracy, the application of the Monte Carlo method is considered to be able to predict the amount of the APBU proposal budget that is approved by each faculty each year.

scholarly journals MULTIPROCESSOR MODELING TECHNOLOGIES FOR THE APPLIED STATISTICAL TASKS

2019 ◽  
pp. 3-9
Author(s):  
Shvachych G. G. ◽  
Sazonova M. S. ◽  
Zaporozhchenko O. E. ◽  
Karpova T. P. ◽  
Sushko L. F.
Keyword(s):  
Monte Carlo ◽  
Parallel Computing ◽  
Monte Carlo Method ◽  
Parallel Algorithms ◽  
Input Data ◽  
Parallel Calculations ◽  
Random Generator ◽  
The Monte Carlo Method ◽  
Productive Systems ◽  
Modified Algorithm

The work considers the multiprocessors technologies of modeling for Monte Carlo tasks. It is shown that only application of the modern super productive systems permitted the new way to realize the mechanism of corresponding partitioned computations. The calculating schemes that supply to provide the increase of productivity and calculations' speed effectiveness are shown. In this article the modified algorithm of parallel calculations is offered based on the Monte Carlo method. Here every calculator has its own random generator of numbers. Thus intermediate calculations come true independently on the different, separately taken blades of cluster, "calculators". The results are already processed on some separately taken master -blades ("analyzer"). This allows to get rid from the necessary presence of router-communicator between the random generator of numbers and "calculator". Obviously, that such decision allows to accelerate the process of calculations. It is shown that the parallel algorithms of the Monte Carlo method are stable to any input data and have the maximal parallel form and, thus, minimal possible time of realization using the parallel computing devices. If it is possible to appoint one processor to one knot of calculation. Thus the realization of calculations becomes possible in all knots of net area in parallel and simultaneously.

Determination of the Allowable Manipulator Link Shapes; and Task, Installation, and Obstacle Spaces Using the Monte Carlo Method

1989 ◽  
Author(s):  
J. Rastegar ◽  
Q. Tu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Shape Design ◽  
Task Space ◽  
Operating Environment ◽  
End Effector ◽  
The Monte Carlo Method ◽  
Manipulator Link ◽  
And Task

Abstract The Monte Carlo method is used to solve a number of problems in manipulator link shape design, and in task space and obstacle placement. The shape of links of manipulators that are to operate within geometrically specified enclosures are determined. Within the enclosure, one or several obstacles may be present. The end effector operates within the task space, and may be required to reach points in different regions with different orientations. For a specified operating environment (enclosure geometry and obstacles), the spaces within which a given manipulator may be installed in order to perform the required tasks are identified. For a given enclosure, task space, and position of the fixed joint of the manipulator, regions within which obstacles may be placed are mapped. The developed methods are very simple, numeric in nature, and readily implemented on computer. Several examples are presented.

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