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Las Vegas, NV|Mandalay Bay Resort and Casino
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Terrestrial Energy, Schneider partner on molten salt reactor
Terrestrial Energy and Schneider Electric are teaming to deploy Terrestrial Energy's integral molten salt reactor (IMSR) to provide zero-emission power to industrial facilities and large data centers.
The companies signed a memorandum of understanding in April to jointly develop commercial opportunities with high-energy users looking for reliable, affordable, and zero-carbon baseload supply. Terrestrial Energy said that working with Schneider “offers solutions to the major energy challenges faced by data center operators and many heavy industries operating a wide range of industrial processes such as hydrogen, ammonia, aluminum, and steel production.”
Robert Nshimirimana, Ajith Abraham, Gawie Nothnagel, Andries Engelbrecht
Nuclear Technology | Volume 207 | Number 1 | January 2021 | Pages 147-166
Technical Paper | doi.org/10.1080/00295450.2020.1740562
Articles are hosted by Taylor and Francis Online.
A manual approach to radiography process optimization is a time-consuming and labor-intensive process. Therefore, a virtual environment in which all of the processes of optimization for a desired radiography experiment or setup are conducted is highly desirable. Such an environment should be able to provide the capability to arrive at radiographic scanning parameters that are optimized to within preset criteria for design purposes. In this paper, a simplified approach toward achieving this is described, and calculated radiography results are benchmarked against experiments. A ray-tracing technique combined with the exponential law of attenuation was used to provide the primary function of such a virtual environment, which is the modeling of the radiography system. Radiography quality parameters such as contrast, penetration, unsharpness, and resolution were calculated using predefined definitions and fed directly into a particle swarm optimization routine that searched for the best radiography design parameters in an iterative feedback loop between the simulator and the optimizer modules. The aim of this paper is to show that a rather simple radiography simulation approach can already provide sufficient data for system design optimization purposes without the need to develop or utilize a comprehensive, competitive radiography simulator. The simplified approach provides a direct “uncomplicated” virtual environment for basic radiography training and basic experimental planning.