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Author

Rakesh Sarma

Bio: Rakesh Sarma is an academic researcher from Centrum Wiskunde & Informatica. The author has contributed to research in topics: Heliostat & Thermal. The author has an hindex of 5, co-authored 9 publications receiving 73 citations. Previous affiliations of Rakesh Sarma include Indian Institute of Technology, Jodhpur & Delft University of Technology.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the Open Volumetric Air Receiver (OVAR) is used for heat treatment of steels and other possible extractive metallurgy operations such as the smelting of metals from its ores.

26 citations

Journal ArticleDOI
TL;DR: In this article, an open volumetric air receiver for metal processing was designed and evaluated using the ANSYS-FLUENT computational fluid dynamics tool for uniform and non-uniform heating of porous absorbers.

20 citations

Journal ArticleDOI
TL;DR: In this article, the required free-stream air velocity for cleaning of such a mirror depending on particle size and location was analyzed and a strategy for collection of the removed dust particles from these pores was presented to avoid their passage to internals.

14 citations

Journal ArticleDOI
TL;DR: In this article, the authors demonstrate a probabilistic approach by inferring the values of the diffusion and loss term parameters, along with their uncertainty, in a Bayesian framework, where identification is obtained using the Van Allen Probe measurements.
Abstract: The Van Allen radiation belts in the magnetosphere have been extensively studied using models based on radial diffusion theory, which is derived from a quasi-linear approach with prescribed inner and outer boundary conditions. The 1D diffusion model requires the knowledge of a diffusion coefficient and an electron loss timescale, which is typically parameterized in terms of various quantities such as the spatial (L) coordinate or a geomagnetic index (e.g., Kp). These terms are typically empirically derived, not directly measurable, and hence are not known precisely, due to the inherent nonlinearity of the process and the variable boundary conditions. In this work, we demonstrate a probabilistic approach by inferring the values of the diffusion and loss term parameters, along with their uncertainty, in a Bayesian framework, where identification is obtained using the Van Allen Probe measurements. Our results show that the probabilistic approach statistically improves the performance of the model, compared to the empirical parameterization employed in the literature.

12 citations

Journal ArticleDOI
TL;DR: In this paper, a probabilistic approach was proposed to infer the values of the diffusion and loss term parameters, along with their uncertainty, in a Bayesian framework, where identification is obtained using the Van Allen Probe measurements.
Abstract: The Van Allen radiation belts in the magnetosphere have been extensively studied using models based on radial diffusion theory, which is based on a quasi-linear approach with prescribed inner and outer boundary conditions. The 1-d diffusion model requires the knowledge of a diffusion coefficient and an electron loss timescale, which are typically parameterized in terms of various quantities such as the spatial ($L$) coordinate or a geomagnetic index (for example, $Kp$). These terms are empirically derived, not directly measurable, and hence are not known precisely, due to the inherent non-linearity of the process and the variable boundary conditions. In this work, we demonstrate a probabilistic approach by inferring the values of the diffusion and loss term parameters, along with their uncertainty, in a Bayesian framework, where identification is obtained using the Van Allen Probe measurements. Our results show that the probabilistic approach statistically improves the performance of the model, compared to the parameterization employed in the literature.

12 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a test bench was developed and the solar-to-thermal efficiency of reticulate porous ceramics with open pores was characterized, and improvements with current state-of-theart were made by the use of a homogenizer, ensuring spatially homogenized concentrated solar flux irradiation.

80 citations

01 Dec 2011
TL;DR: In this paper, the authors analyzed PM 10 and PM 2.5 from four monitoring stations in the Gobi Desert region of Mongolia for a 16-month period in 2009-2010.
Abstract: Dust mass concentrations of PM 10 and PM 2.5 from four monitoring stations in the Gobi Desert region of Mongolia were analyzed for a 16-month period in 2009–2010. Annual averaged PM 10 concentration ranged from 9 μg m − 3 to 49 μg m − 3 at these stations during 2009. Concentrations were high in winter owing to air pollution and in spring owing to dust storms; the monthly mean concentrations of PM 10 (PM 2.5 ) at the three stations except for Sainshand reached yearly maxima in December and January, ranging from 60 (38) μg m − 3 to 120 (94) μg m − 3 . Diurnal variations of PM 10 and PM 2.5 concentrations at two sites, Dalanzadgad and Zamyn-Uud, included two maxima in the morning and evening and two minima in the afternoon and early morning. However, at Erdene PM 10 maxima occurred in the afternoon and evening. Both PM 10 and PM 2.5 concentrations were enhanced from March to May by dust storms. Dust storms raised huge amounts of fine dust particles in the Gobi of Mongolia. Maximum daily mean PM 10 (PM 2.5 ) concentrations reached 821 (500) μg m − 3 at Dalanzadgad, 308 (129) μg m − 3 at Zamyn-Uud, and 1328 μg m − 3 at Erdene. Hourly maximum PM 10 (PM 2.5 ) concentrations were as high as 6626 (2899) μg m − 3 at Dalanzadgad during a dust storm.

59 citations

Journal ArticleDOI
TL;DR: A water droplet behavior on a hydrophobic surface is examined relevant to the dust particles removal from the surface and it is found that predictions of droplet translational velocity agree well with those obtained from the experiment.
Abstract: A water droplet behavior on a hydrophobic surface is examined relevant to the dust particles removal from the surface. Surface crystallization of polycarbonate is realized in acetone bath and the resulting surface is coated by the functionalized nano-size silica particles towards reducing contact angle hysteresis. This arrangement provides droplet rolling/sliding on the hydrophobic surface. Droplet translational velocity is formulated and predictions are compared with those resulted from the high speed recorded data. Influence of surface inclination angle on droplet dynamics is investigated and the dust removal mechanism on the inclined surface is analyzed. It is found that predictions of droplet translational velocity agree well with those obtained from the experiment. Droplet rolling dominates over sliding on the inclined surface and droplet sliding velocity remains almost 10% of the droplet translational velocity. The main mechanism for the dust particles removal is associated with the droplet fluid cloaking of the dust particles during its transition on the hydrophobic surface. Droplet acceleration, due to increased surface inclination angle, has effect on the rate of dust particles removal from the surface, which is more apparent for large droplet volumes. Increasing droplet acceleration improves the coverage area of the clean surface.

47 citations

Journal ArticleDOI
19 May 2021-Nature
TL;DR: In this paper, the authors argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers.
Abstract: High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics-however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

39 citations

Journal ArticleDOI
TL;DR: Based on EnergyPlus, the optimal shading arrangements of NZEBs are obtained by evaluating the shading performance of different shading slat angles, orientations, window-to-wall ratios (WWRs) and locations as mentioned in this paper.

29 citations