Formula 101 Using 2022 Formula One Season Data to Understand the Race Results

The reason why I am interested in Formula One is that my friend showed me what Formula One was all about. It became interesting to see the action of the sport, including the battles the drivers have during the race and how fast they go through a corner. Also, when qualifying comes around, they push their car to the absolute limit to gain a few seconds off their opponents. The drivers only in the top 10 receive points from the winner getting 25 points, the last driver in the top 10 getting 1 point, and those below the top ten end up with no points. The competitiveness that Formula One creates is amazing to see from all the drivers competing to be the best. I collected my data through the Formula One 2022 race result website, which shows the race winners’ race data and the whole grid of drivers where they all placed in the race. It also includes the racers that were unable to finish the race due to problems with the car and could crash, resulting in a DNF. The main reason we will use the data is to figure out the averages of each driver where they usually place on the grid. Also, we can figure out the averages of points they get for the season. I expect the results to be finding the outcomes of each driver, such as where they place in the race, and also figuring out each team\u27s average points for the 2022 season

Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference

A Monte Carlo-based Bayesian inference model is applied to the prediction of reactor operation parameters of a PWR nuclear power plant. In this non-perturbative framework, high-dimensional covariance information describing the uncertainty of microscopic nuclear data is combined with measured reactor operation data in order to provide statistically sound, well founded uncertainty estimates of integral parameters, such as the boron letdown curve and the burnup-dependent reactor power distribution. The performance of this methodology is assessed in a blind test approach, where we use measurements of a given reactor cycle to improve the prediction of the subsequent cycle. As it turns out, the resulting improvement of the prediction quality is impressive. In particular, the prediction uncertainty of the boron letdown curve, which is of utmost importance for the planning of the reactor cycle length, can be reduced by one order of magnitude by including the boron concentration measurement information of the previous cycle in the analysis. Additionally, we present first results of non-perturbative nuclear-data updating and show that predictions obtained with the updated libraries are consistent with those induced by Bayesian inference applied directly to the integral observables.Comment: 10 pages, 11 figure