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EFFECT OF AI ON VARIOUS ALTERNATIVE FUEL TECHNOLOGIES
Abstract
With the growing advancements in Artifi cial Intelligence, researchers are introducing innovative applications to combat the growing global demand for energy. For instance, researchers are aiming to make nuclear fuel safer and more effi cient with AI assistance by reducing simulation times to anticipate potential failures quickly. Proton exchange membrane fuel cells, an expensive energy alternative, leverage AI-powered parameter optimization to make PEM fuel cells a more economically viable energy source, offering far greater effi ciency and lower costs than traditional experimentation. Other sustainable sources, such as biodiesel, are benefi ting from AI modeling and predictions that aid in effi ciency optimizations and emission reductions. AI’s increasing ability to tackle the complexity that plagues traditional theoretical models allows it to be applied to predict the low fl ammability limit of sustainable aviation fuel blends. Besides having a direct impact on fuel production, AI can be utilized to assist in creating infrastructure that enables sustainable habits to thrive.
Introduction:
Artifi cial intelligence, or AI, is technology that simulates human intelligence, mimicking our abilities to solve problems, learn, and develop connections. Recently, AI has been growing rapidly with billions of dollars in private and government investment. In 2025, the US government allocated approximately $11 billion in AI funding [1]. AI in the research fi eld has been increasingly used for its powerful AI models which are programs trained with data to perform specifi c tasks. For instance, in the energy sector, AI models are being used to predict results that would have been unfeasible to do so with traditional complex models.
Alternative energy sources have been a focus over the past few
decades due to pressure to switch to more sustainable options, such as nuclear power, sustainable fuels, and hydrogen. Nuclear energy is when energy from the nucleus of atoms is harnessed through fi ssion or fusion. Nuclear energy produces vast amounts of energy, with the trade-off of radioactive waste and questions about its safety. Sustainable fuel sources like Sustainable Aviation Fuel (SAF) and biodiesel are biofuels derived from biomass. Hydrogen Fuel Cells and hydrogen energy in general are expensive and diffi cult to synthesize. Hydrogen energy is chemical potential energy stored as hydrogen, which is then converted to water. This paper will examine how AI plays a role in all these different fuel types and how AI can boost sustainable fuel production and usage.
AI in Nuclear Energy
In nuclear engineering, the behavior of systems, structures, and components regarding nuclear reactors is studied. Complex partial differential equations (PDEs) govern these physical models. Traditionally, we use tools like fi nite element analysis (FEA) simulations to break down the behavior of systems, structures, and components in real-life conditions into manageable variables, each governed by its own partial differential equation (PDE). By solving each PDE iteratively, each one builds upon the previously solved one to develop a cohesive model [2]. The resolution of these models depends on the time it spent processing information; the higher the resolution and the more intensive the calculations lead to more computational resources being used and the computing cost to increase. Currently, a possible solution is reduced-order modeling, where the dimensions of a complex model are simplifi ed and linearized. This has proven to be useful but is limited by its inherent linearity. AI models have been rapidly developed as surrogates that overcome the limitations of simpler models, enabling the evaluation of both linear and non-linear functions. Researchers S.A. Cancemi et al utilize Convolutional Neural Networks (CNN) to model pellet-cladding interactions, an inherently complex fi nite element simulation, to improve the safety of nuclear plants [2]. Pellet cladding interaction occurs when the cladding of a pellet fails due to large power surges [3]. The cladding on the pellets serves as a crucial barrier, preventing radioactive materials within the pellets from escaping and contaminating the reactor [4]. In this experiment, a surrogate model (a simpler model of a more complex mathematical model) is introduced to predict the microstructural evolution and mechanical properties of AISI 316L stainless steel fuel cladding at different temperatures and radiation doses, thereby creating an accurate model that can be used to predict failures under various conditions.
Figure 1: Real average von Mises stress of the pellet vs Predicted average von Mises stress from the CNN model [2].
CNNs are trained on a series of images and excel at image processing and pattern recognition. Images are passed through a hierarchy of “fi lters” where features are extracted. 13 parameters are included in this model: Mass density, Young’s modulus, Poisson’s ratio, Thermal expansion coeffi cient, Yield strength, Specifi c heat, Thermal conductivity, Shear modulus, Back stress for implicit creep, Yield stress for implicit creep, Specifi c heat, Centerline temperature, and Pressure [2]. These input features characterize materials and determine thermochemical behaviors. If we use a fi nite element model to simulate typical pressurized water reactor conditions, the FE fails to provide a real-time assessment of the cladding integrity on the pellet. However, the CNN surrogate model developed can offer rapid results (0.98 seconds) in predicting displacement, von Mises stress, and creep strain [2]. Displacement is the physical change of position of a point on an object (in this case, the pellet). Creep strain is the deformation that occurs (relative to time) due to high temperatures, and von Mises stress is used to
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