scholarly journals Monte Carlo and Medical Physics

2021 ◽  
Author(s):  
Omaima Essaad Belhaj ◽  
Hamid Boukhal ◽  
El Mahjoub Chakir
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ionizing Radiation ◽  
Radiation Protection ◽  
Medical Physics ◽  
Absorbed Dose ◽  
Equivalent Dose ◽  
Clinical Environment ◽  
The Monte Carlo Method

The different codes based on the Monte Carlo method, allows to make simulations in the field of medical physics, so the determination of all the magnitudes of radiation protection namely the absorbed dose, the kerma, the equivalent dose, and effective, what guarantees the good planning of the experiment in order to minimize the degrees of exposure to ionizing radiation, and to strengthen the radiation protection of patients and workers in clinical environment as well as to respect the 3 principles of radiation protection ALARA (As Low As Reasonably Achievable) and which are based on: -Justification of the practice -Optimization of radiation protection -Limitation of exposure.

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 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.

scholarly journals Validating a virtual source model based in Monte Carlo method for profiles and percent depth doses calculation

2019 ◽  
Vol 7 (2A) ◽  
Author(s):  
Renata Aline Del Nero ◽  
Marcos Vinicius Nakaoka Nakandakari ◽  
Hélio Yoriyaz
Keyword(s):  
Monte Carlo ◽  
Phase Space ◽  
Monte Carlo Method ◽  
Radiation Transport ◽  
Medical Physics ◽  
Source Model ◽  
Good Precision ◽  
Virtual Source ◽  
The Monte Carlo Method ◽  
Space Data

The Monte Carlo method for radiation transport has been adapted for medical physics application. More specifically, it has received more attention in clinical treatment planning with the development of more efficient computer simulation techniques. In linear accelerator modeling by the Monte Carlo method, the phase space data file (phsp) is an alternative representation for radiation source. However, to create a phase space file and obtain good precision in the results, it is necessary detailed information about the accelerator's head and commonly the supplier does not provide all the necessary data. An alternative to the phsp is the Virtual Source Model (VSM). This alternative approach presents many advantages for the clinical Monte Carlo application. This is the most efficient method for particle generation and can provide an accuracy similar when the phsp is used. This research propose a VSM simulation with the use of a Virtual Flattening Filter (VFF) for profiles and percent depth doses calculation. Two different sizes of open fields (40 x 40 cm² and 40 x 40 cm² rotated 45°) were used and two different source to surface distance (SSD) were applied: the standard 100 cm and custom SSD of 370 cm, which is applied in radiotherapy treatments of total body irradiation. The data generated by the simulation was analyzed and compared with experimental data to validate the VSM. This current model is easy to build and test.

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.

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