Measuring the Sea Water Absorption Factor Using Integrating Sphere

2018 ◽  
pp. 120-126 ◽  
Author(s):  
Dmitry I. Glukhovets ◽  
Sergei V. Sheberstov ◽  
Oleg V. Kopelevich ◽  
Anna F. Zaytseva ◽  
Sergei I. Pogosyan
Keyword(s):  
Monte Carlo ◽  
Water Absorption ◽  
Numerical Experiments ◽  
Sea Water ◽  
Integrating Sphere ◽  
Reference Solution ◽  
Absorption Factor ◽  
The Monte Carlo Method ◽  
Specular Component

Practical questions of quickly determining the sea water absorption factor using an integrating sphere are considered: measurement technique and data processing, as well as reference solution calibration. Numerical experiments using the Monte-Carlo method are performed to evaluate the influence of the device features (absence of spherical symmetry and the presence of a reflection specular component from the quartz shell) on the determination of the absorption factor considering scattering properties of the medium. Examples of how the results of the proposed technique can be used are given under the conditions of sea expeditions.

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 Progress in the development of the semi-empirical calibration of neutron probes based on Monte Carlo modeling

2021 ◽  
Vol 46 (3) ◽  
pp. 251
Author(s):  
Urszula Woźnicka
Keyword(s):  
Monte Carlo ◽  
Numerical Experiments ◽  
Original Method ◽  
Neutron Probe ◽  
The Monte Carlo Method ◽  
Empirical Calibration ◽  
Wide Range ◽  
Significant Extension ◽  
Simulation Results ◽  
Semi Empirical

The method of the semi-empirical calibration of a neutron well logging probe was developed by Jan Andrzej Czubek on the concept of the general neutron parameter (GNP) and tested positively at the neutron calibration station in Zielona Góra, Poland. The neutron probe responses in a wide range of neutron parameters (and thus lithology, porosity and saturation) were also computed using the Monte Carlo method. The obtained simulation results made it possible to determine the calibration curves using the Czubek concept in a wider range than by means of the original method. The very good compatibility of both methods confirms the applicability of the GNP as well as the Monte Carlo numerical experiments, which allow for a significant extension of the semi-empirical calibration in complex well geometries taking into account e.g., casing or invaded zones.

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.

Monte Carlo Simulation for the Angle-Dependent Light Transmittance of Thermotropic Material

2010 ◽  
Vol 29-32 ◽  
pp. 2781-2784
Author(s):  
Jian Yao ◽  
Jin Xu
Keyword(s):  
Energy Efficiency ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Building Energy ◽  
Light Transmittance ◽  
Integrating Sphere ◽  
Building Energy Efficiency ◽  
Scattering Light

In this paper we propose a Monte-Carlo method for the simulation of the angle-dependent light transmittance of thermotropic material. The results show that the scattering light increased as temperature rose, and most of light transmitted went through the sample of thermotropic material at the angles between 10~40 deg. The results also indicate that the light transmittance measurement of thermotropic material by spectrophotometer without an integrating sphere is not accurate. As a conclusion, Monte Carlo simulation is an effective method for the determination of angle-dependent light transmittance of thermotropic material, and results of these simulations can be used to calculate the shading coefficient of window for building energy efficiency.

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