Hydrostatic Determination of Test Weight Density and Uncertainty Evaluation by Monte Carlo Method

2021 ◽  
Vol 13 (2) ◽  
pp. 56-66
Author(s):  
Loreibelle Abian ◽  
Alvin Caparanga
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
The Philippines ◽  
Test Weight ◽  
Tolerance Interval ◽  
Propagation Of Uncertainty ◽  
Weight Density ◽  
Solid Density ◽  
Hydrostatic Weighing

This paper presents the characterization of the hydrostatic weighing facility of the National Metrology Laboratory (NML) of the Philippines. The study aimed to evaluate its suitability for determination of solid density. It was used to hydrostatically measure the density of a stainless steel (OIML Class F1) test weight weighing 200 g. The measurement result obtained was 7.5827 g cm-3 ± 0.0041 g cm-3 at an approximately 95 % level of confidence. The uncertainty evaluated by the Law of Propagation of Uncertainty (LPU) according to JCGM 100:2008 (GUM) was verified by the Monte Carlo method (MCM), which gave a result of 0.0040 g cm-3. The value determined for the solid density of the sample with its associated expanded uncertainty was found to be within the tolerance interval between 7.39 g cm-3 and 8.73 g cm-3 as required in OIML R111-1 for Class F1 test weights.

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.

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