AI and Machine Learning In Nuclear Fusion

Author(s):  
Andrew Kamal

With the emergence of regressional mathematics and algebraic topology comes advancements in the field of artificial intelligence and machine learning. Such advancements when looking into problems such as nuclear fusion and entropy, can be utilized to analyze unsolved abnormalities in the area of fusion related research. Proof theory will be utilized throughout this paper. For logical mathematical proofs: n represents an unknown number, e represents point of entropy, and m represents maximum point, f represents fusion. This paper will look into analysis of the topic of nuclear fusion and unsolved problems as hardness problems and attempt to formulate computational proofs in relation to entropy, fusion maximum, heat transfer, and entropy transfer mechanisms. This paper will not only be centered around logical proofs but also around computational mechanisms such as distributed computing and its potential role in analyzing computational hardness in relation to fusion related problems. We will summarize a proposal for experimentation utilizing further logical proof formalities and the decentralized-internet SDK for a computational pipeline in order to solve fusion related hardness problems.

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Amnart Boonloi ◽  
Withada Jedsadaratanachai

Numerical assessments in the square channel heat exchanger installed with various parameters of V-orifices are presented. The V-orifice is installed in the heat exchanger channel with gap spacing between the upper-lower edges of the orifice and the channel wall. The purposes of the design are to reduce the pressure loss, increase the vortex strength, and increase the turbulent mixing of the flow. The influence of the blockage ratio and V-orifice arrangement is investigated. The blockage ratio, b/H, of the V-orifice is varied in the range 0.05–0.30. The V-tip of the V-orifice pointing downstream (V-downstream) is compared with the V-tip pointing upstream (V-upstream) by both flow and heat transfer. The numerical results are reported in terms of flow visualization and heat transfer pattern in the test section. The thermal performance assessments in terms of Nusselt number, friction factor, and thermal enhancement factor are also concluded. The numerical results reveal that the maximum heat transfer enhancement is found to be around 26.13 times higher than the smooth channel, while the optimum TEF is around 3.2. The suggested gap spacing for the present configuration of the V-orifice channel is around 5–10%.


Author(s):  
Loı̈c M. Roch ◽  
Florian Häse ◽  
Christoph Kreisbeck ◽  
Teresa Tamayo-Mendoza ◽  
Lars P. E. Yunker ◽  
...  

<div>Autonomous or “self-driving” laboratories combine robotic platforms with artificial intelligence to increase the rate of scientific discovery. They have the potential to transform our traditional approaches to experimentation. Although autonomous laboratories recently gained increased attention, the requirements imposed by engineering the software packages often prevent their development. Indeed, autonomous laboratories require considerable effort in designing and writing advanced and robust software packages to control, orchestrate and synchronize automated instrumentations, cope with databases, and interact with various artificial intelligence algorithms. To overcome this limitation, we introduce ChemOS, a portable, modular and versatile software package, which supplies the structured layers indispensable for operating autonomous laboratories. Additionally, it enables remote control of laboratories, provides access to distributed computing resources, and comprises state-of-the-art machine learning methods. We believe that ChemOS will reduce the time-to-deployment from automated to autonomous discovery, and will provide the scientific community with an easy-to-use package to facilitate novel discovery, at a faster pace.</div>


2020 ◽  
pp. 1-22
Author(s):  
Rasool Alizadeh ◽  
Javad Mohebbi Najm Abad ◽  
Abolfazl Fattahi ◽  
Mohammad Reza Mohebbi ◽  
Mohammad Hossein Doranehgard ◽  
...  

Abstract This study numerically investigates heat convection and entropy generation in a hybrid nanofluid (Al2O3-Cu-water) flowing around a cylinder embedded in porous media. An artificial-neural-network is used for predictive analysis, in which numerical data are generated to train an intelligence algorithm and to optimize the prediction errors. Results show that the heat transfer of the system increases when the Reynolds number, permeability parameter, or volume fraction of nanoparticles increases. However, the functional forms of these dependencies are complex. In particular, increasing the nanoparticle concentration is found to have a non-monotonic effect on entropy generation. The simulated and predicted data are subjected to particle swarm optimization to produce correlations for the shear stress and Nusselt number. This work demonstrates the capability of artificial intelligence algorithms in predicting the thermohydraulics and thermodynamics of thermal and solutal systems.


Author(s):  
Ganesh Guggilla ◽  
Arvind Pattamatta ◽  
Ramesh Narayanaswamy

Abstract Due to the advancements in computing services such as machine learning and artificial intelligence, high-performance computing systems are needed. Consequently, the increase in electron chip density results in high heat fluxes and required sufficient thermal management to maintain the servers. In recent times, the liquid cooling techniques become prominent over air cooling as it has significant advantages. Spray cooling is one such efficient cooling process which can be implemented in electronics cooling. To enhance the knowledge of the process, detailed studies of fundamental mechanisms involved in spray cooling such as single droplet and multiple droplet interactions are required. The present work focuses on the study of a train of droplets impinging over a heated surface using FC-72 liquid. The surface temperature is chosen as a parameter, and the Dynamic Leidenfrost point (DLP) for the present impact conditions is identified. Spread hydrodynamics and heat transfer characteristics of these consecutively impinging droplets till the Leidenfrost temperature, are studied and compared.


2020 ◽  
Vol 10 (11) ◽  
pp. 3681
Author(s):  
Hosung Woo ◽  
JaMee Kim ◽  
WonGyu Lee

Artificial intelligence (AI) is bringing about enormous changes in everyday life and today’s society. Interest in AI is continuously increasing as many countries are creating new AI-related degrees, short-term intensive courses, and secondary school programs. This study was conducted with the aim of identifying the interrelationships among topics based on the understanding of various bodies of knowledge and to provide a foundation for topic compositions to construct an academic body of knowledge of AI. To this end, machine learning-based sentence similarity measurement models used in machine translation, chatbots, and document summarization were applied to the body of knowledge of AI. Consequently, several similar topics related to agent designing in AI, such as algorithm complexity, discrete structures, fundamentals of software development, and parallel and distributed computing were identified. The results of this study provide the knowledge necessary to cultivate talent by identifying relationships with other fields in the edutech field.


Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 354
Author(s):  
Octavian Sabin Tătaru ◽  
Mihai Dorin Vartolomei ◽  
Jens J. Rassweiler ◽  
Oșan Virgil ◽  
Giuseppe Lucarelli ◽  
...  

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.


Author(s):  
Loı̈c M. Roch ◽  
Florian Häse ◽  
Christoph Kreisbeck ◽  
Teresa Tamayo-Mendoza ◽  
Lars P. E. Yunker ◽  
...  

<div>Autonomous or “self-driving” laboratories combine robotic platforms with artificial intelligence to increase the rate of scientific discovery. They have the potential to transform our traditional approaches to experimentation. Although autonomous laboratories recently gained increased attention, the requirements imposed by engineering the software packages often prevent their development. Indeed, autonomous laboratories require considerable effort in designing and writing advanced and robust software packages to control, orchestrate and synchronize automated instrumentations, cope with databases, and interact with various artificial intelligence algorithms. To overcome this limitation, we introduce ChemOS, a portable, modular and versatile software package, which supplies the structured layers indispensable for operating autonomous laboratories. Additionally, it enables remote control of laboratories, provides access to distributed computing resources, and comprises state-of-the-art machine learning methods. We believe that ChemOS will reduce the time-to-deployment from automated to autonomous discovery, and will provide the scientific community with an easy-to-use package to facilitate novel discovery, at a faster pace.</div>


2019 ◽  
Vol 214 ◽  
pp. 00001
Author(s):  
Alessandra Forti ◽  
Latchezar Betev ◽  
Maarten Litmaath ◽  
Oxana Smirnova ◽  
Petya Vasileva ◽  
...  

The 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP) took place in the National Palace of Culture, Sofia, Bulgaria from 9th to 13th of July 2018. 575 participants joined the plenary and the eight parallel sessions dedicated to: online computing; offline computing; distributed computing; data handling; software development; machine learning and physics analysis; clouds, virtualisation and containers; networks and facilities. The conference hosted 35 plenary presentations, 323 parallel presentations and 188 posters.


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