Accelerating Dust Temperature Calculations with Graphics Processing Units

When calculating the infrared spectral energy distributions (SEDs) of galaxies in radiation-transfer models, the calculation of dust grain temperatures is generally the most time-consuming part of the calculation. Because of its highly parallel nature, this calculation is perfectly suited for massively parallel general-purpose Graphics Processing Units (GPUs). This paper presents an implementation of the calculation of dust grain equilibrium temperatures on GPUs in the Monte-Carlo radiation transfer code Sunrise, using the CUDA API. The GPU can perform this calculation 69 times faster than the 8 CPU cores, showing great potential for accelerating calculations of galaxy SEDs.Comment: 7 pages, 2 figures, accepted to New Astronomy. Minor updates to text and performance based on feedback from refere

The use of primitives in the calculation of radiative view factors

Compilations of radiative view factors (often in closed analytical form) are readily available in the open literature for commonly encountered geometries. For more complex three-dimensional (3D) scenarios, however, the effort required to solve the requisite multi-dimensional integrations needed to estimate a required view factor can be daunting to say the least. In such cases, a combination of finite element methods (where the geometry in question is sub-divided into a large number of uniform, often triangular, elements) and Monte Carlo Ray Tracing (MC-RT) has been developed, although frequently the software implementation is suitable only for a limited set of geometrical scenarios. Driven initially by a need to calculate the radiative heat transfer occurring within an operational fibre-drawing furnace, this research set out to examine options whereby MC-RT could be used to cost-effectively calculate any generic 3D radiative view factor using current vectorisation technologies

Falsification Of The Atmospheric CO2 Greenhouse Effects Within The Frame Of Physics

The atmospheric greenhouse effect, an idea that many authors trace back to the traditional works of Fourier (1824), Tyndall (1861), and Arrhenius (1896), and which is still supported in global climatology, essentially describes a fictitious mechanism, in which a planetary atmosphere acts as a heat pump driven by an environment that is radiatively interacting with but radiatively equilibrated to the atmospheric system. According to the second law of thermodynamics such a planetary machine can never exist. Nevertheless, in almost all texts of global climatology and in a widespread secondary literature it is taken for granted that such mechanism is real and stands on a firm scientific foundation. In this paper the popular conjecture is analyzed and the underlying physical principles are clarified. By showing that (a) there are no common physical laws between the warming phenomenon in glass houses and the fictitious atmospheric greenhouse effects, (b) there are no calculations to determine an average surface temperature of a planet, (c) the frequently mentioned difference of 33 degrees Celsius is a meaningless number calculated wrongly, (d) the formulas of cavity radiation are used inappropriately, (e) the assumption of a radiative balance is unphysical, (f) thermal conductivity and friction must not be set to zero, the atmospheric greenhouse conjecture is falsified.Comment: 115 pages, 32 figures, 13 tables (some typos corrected

AN EXPERIMENTAL INVESTIGATION OF GASEOUS-FILM COOLING OF A ROCKET MOTOR

Gaseous-film cooling of rocket combustion chambe

Coupled fluid-thermal analysis of low-pressure sublimation and condensation with application to freeze-drying

Freeze-drying is a low-pressure, low-temperature condensation pumping process widely used in the manufacture of bio-pharmaceuticals for removal of solvents by sublimation. The goal of the process is to provide a stable dosage form by removing the solvent in such a way that the sensitive molecular structure of the active substance is least disturbed. The vacuum environment presents unique challenges for understanding and controlling heat and mass transfer in the process. As a result, the design of equipment and associated processes has been largely empirical, slow and inefficient.^ A comprehensive simulation framework to predict both, process and equipment performance is critical to improve current practice. A part of the dissertation is aimed at performing coupled fluid-thermal analysis of low-pressure sublimation-condensation processes typical of freeze-drying technologies. Both, experimental and computational models are used to first understand the key heat transfer modes during the process. A modeling and computational framework, validated with experiments for analysis of sublimation, water-vapor flow and condensation in application to pharmaceutical freeze-drying is developed.^ Augmented with computational fluid dynamics modeling, the simulation framework presented here allows to predict for the first time, dynamic product/process conditions taking into consideration specifics of equipment design. Moreover, by applying the modeling framework to process design based on a design-space approach, it has demonstrated that there is a viable alternative to empiricism

Deep learning for the modeling and inverse design of radiative heat transfer

Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting increasing attention in many other disciplines, including the physical sciences. In this work, we show that artificial neural networks can be successfully used in the theoretical modeling and analysis of a variety of radiative-heat-transfer phenomena and devices. By using a set of custom-designed numerical methods able to efficiently generate the required training data sets, we demonstrate this approach in the context of three very different problems, namely (i) near-field radiative heat transfer between multilayer systems that form hyperbolic metamaterials, (ii) passive radiate cooling in photonic crystal slab structures, and (iii) thermal emission of subwavelength objects. Despite their fundamental differences in nature, in all three cases we show that simple neural-network architectures trained with data sets of moderate size can be used as fast and accurate surrogates for doing numerical simulations, as well as engines for solving inverse design and optimization in the context of radiative heat transfer. Overall, our work shows that deep learning and artificial neural networks provide a valuable and versatile toolkit for advancing the field of thermal radiatio

Ion engine thrust vector study, phase 2 Quarterly report

Performance prediction for expected thrust misalignment in electron bombardment ion thruste