Derivation of the Angular Dispersion Error Distribution of Mirror Surfaces for Monte Carlo Ray-Tracing Applications

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
T. Cooper ◽  
A. Steinfeld

Of paramount importance to the optical design of solar concentrators is the accurate characterization of the specular dispersion errors of the reflecting surfaces. An alternative derivation of the distribution of the azimuthal angular dispersion error is analytically derived and shown to be equivalent to the well-known Rayleigh distribution obtained by transforming the bivariate circular Gaussian distribution into polar coordinates. The corresponding inverse cumulative distribution function applied in Monte Carlo ray-tracing simulations, which gives the dispersion angle as a function of a random number sampled from a uniform distribution on the interval (0,1), does not depend on the inverse error function, thus simplifying and expediting Monte Carlo computations. Using a Monte Carlo ray-tracing example, it is verified that the Rayleigh and bivariate circular Gaussian distribution yield the same results. In the given example, the Rayleigh method is found to be ∼40% faster than the Gaussian method.

2020 ◽  
Author(s):  
Ignazio Ciufolini ◽  
Antonio Paolozzi

AbstractIn this paper we study the statistical evolution in time of the Covid-19 pandemic in Spain, Italy, Germany, Belgium, The Netherlands, Austria and Portugal, i.e., the countries of the European Union (EU) that have a number of positive cases higher than 12 thousand at April 7, 2020. France is the third country of the EU for number of cases but a jump in the data on April 3, 2020 does not allow, at least for the moment, to have a reliable prediction curve. The analysis is based on the use of a function of the type of a Gauss Error Function, with four parameters, as a Cumulative Distribution Function (CDF). A Monte Carlo analysis is used to estimate the uncertainty. The approach used in this paper is mathematical and statistical and thus does not explicitly consider a number of relevant issues, including number of nasopharyngeal swabs, mitigation measures, social distancing, virologic, epidemiological and models of contamination diffusion.


Author(s):  
Ignazio Ciufolini ◽  
Antonio Paolozzi

AbstractIn this paper are presented predictions on the evolution in time of the number of positive cases in Italy of the Covid-19 pandemic based on official data and on the use of a function of the type of a Gauss Error Function as a Cumulative Distribution Function (CDF). We have analyzed the available data for China and Italy. The evolution in time of the number of cumulative diagnosed positive cases of Covid-19 in China very well approximates a distribution of the type of the Error Function, that is, the integral of a normal, Gaussian distribution. We have then used such a function to study the potential evolution in time of the number of positive cases in Italy by performing a number of fits of the official data so far available. We then found a statistical prediction for the day in which the peak of the number of daily positive cases in Italy occurs, corresponding to the flex of the fit, i.e., to the change in sign of its second derivative (that is the change from acceleration to deceleration) as well as of the day in which a substantial attenuation of such number of daily cases is reached. We have then performed 150 Monte Carlo simulations in the attempt to have a more robust prediction of the day of the above-mentioned peak and of the day of the substantial decrease of the number of daily positive cases. Although, official data have been used, these predictions are obtained with a heuristic approach, since those predictions are based on statistical approach and do not take into account either a number of relevant issues (such as medical, social distancing, virologic, epidemiological, etc.) or models of contamination diffusion.


2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Hector Vazquez-Leal ◽  
Roberto Castaneda-Sheissa ◽  
Uriel Filobello-Nino ◽  
Arturo Sarmiento-Reyes ◽  
Jesus Sanchez Orea

The integral of the standard normal distribution function is an integral without solution and represents the probability that an aleatory variable normally distributed has values between zero and . The normal distribution integral is used in several areas of science. Thus, this work provides an approximate solution to the Gaussian distribution integral by using the homotopy perturbation method (HPM). After solving the Gaussian integral by HPM, the result served as base to solve other integrals like error function and the cumulative distribution function. The error function is compared against other reported approximations showing advantages like less relative error or less mathematical complexity. Besides, some integrals related to the normal (Gaussian) distribution integral were solved showing a relative error quite small. Also, the utility for the proposed approximations is verified applying them to a couple of heat flow examples. Last, a brief discussion is presented about the way an electronic circuit could be created to implement the approximate error function.


2018 ◽  
Vol 2 ◽  
pp. 114-122
Author(s):  
Yu.I. Nikolayenko ◽  
◽  
V.G. Ilvovsky ◽  
S.V. Moiseenko ◽  
◽  
...  

Author(s):  
Tejas U. Ulavi ◽  
Jane H. Davidson ◽  
Tim Hebrink

The technical performance of a non-tracking hybrid PV/T concept that uses a wavelength selective film is modeled. The wavelength selective film is coupled with a compound parabolic concentrator to reflect and concentrate the infrared portion of the solar spectrum onto a tubular absorber while transmitting the visible portion of the spectrum to an underlying thin-film photovoltaic module. The optical performance of the CPC/selective film is obtained through Monte Carlo Ray-Tracing. The CPC geometry is optimized for maximum total energy generation for a roof-top application. Applied to a rooftop in Phoenix, Arizona USA, the hybrid PV/T provides 20% more energy compared to a system of the same area with independent solar thermal and PV modules, but the increase is achieved at the expense of a decrease in the electrical efficiency from 8.8% to 5.8%.


2014 ◽  
Author(s):  
Guojin Feng ◽  
Ping Li ◽  
Yingwei He ◽  
Yu Wang ◽  
Houping Wu

2006 ◽  
Vol 128 (9) ◽  
pp. 945-952 ◽  
Author(s):  
Sandip Mazumder

Two different algorithms to accelerate ray tracing in surface-to-surface radiation Monte Carlo calculations are investigated. The first algorithm is the well-known binary spatial partitioning (BSP) algorithm, which recursively bisects the computational domain into a set of hierarchically linked boxes that are then made use of to narrow down the number of ray-surface intersection calculations. The second algorithm is the volume-by-volume advancement (VVA) algorithm. This algorithm is new and employs the volumetric mesh to advance the ray through the computational domain until a legitimate intersection point is found. The algorithms are tested for two classical problems, namely an open box, and a box in a box, in both two-dimensional (2D) and three-dimensional (3D) geometries with various mesh sizes. Both algorithms are found to result in orders of magnitude gains in computational efficiency over direct calculations that do not employ any acceleration strategy. For three-dimensional geometries, the VVA algorithm is found to be clearly superior to BSP, particularly for cases with obstructions within the computational domain. For two-dimensional geometries, the VVA algorithm is found to be superior to the BSP algorithm only when obstructions are present and are densely packed.


2018 ◽  
Vol 89 (10) ◽  
pp. 10E118 ◽  
Author(s):  
Seungtae Oh ◽  
Juhyeok Jang ◽  
Byron Peterson ◽  
Wonho Choe ◽  
Suk-Ho Hong

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