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the serpentine architecture. In Figure 4, we can see the base model, model A (90˚), model B (180˚), and model C (270˚) [9]. In each model, the flow direction has been shifted by increments of 90˚. Multiphase simulations were conducted under multiple optimal parameters. In Figure 5, pressure loss and power consumption were measured at different cell voltages. Pressure loss is an issue where the ends of channels are unable to reach a proper pressure (around 1.0 atm), resulting in an imbalance of pressure within the system, which makes it challenging to obtain the correct cell voltage. Consumed power is the power consumed to output a specific voltage. The greater the required power, the lower the voltage. The results concluded that model A showed the lowest amount of pressure loss, and model C showed the lowest amount of power consumption for the demanded voltage.
Figure 4 Shows the serpentine fuel cells, along with the 4 orientations in the trials [9].
Looking at Figure 3, the CO2 emissions ANN model has an MSE
of 0.888 in total, compared to the other model, which ranges from the high 0.90s to the low 0.90s, indicating either an issue within the artificial neural network itself or poor data quality [7]. BTE, or brake thermal efficiency, is one of the most highly valued indicators of engine performance. A high BTE results from a high caloric value and a high cetane number, both of which are key indicators of high-quality diesel. VE, or volumetric efficiency, is a reliable method for evaluating the success of fuel blends in a diesel engine. This parameter measures the ratio of the available volume in the cylinder and the intake of volume of air. BTE, VE, and emission ANN models are critical, especially regarding the more complex direct-injection diesel engines, which are far more efficient compared to their indirect injection counterparts. This study demonstrates how AI capabilities now extend to more nuanced and complex constraints, enabling the return of parameters based on multiple criteria.
Artificial Intelligence in PEM Deep Neural Networks
Proton exchange membranes (PEM) fuel cells are a promising alternative fuel source and a sustainable energy source. PEM fuels operate through an electrochemical process where hydrogen is converted into protons via a platinum catalyst. This chemical process results in only heat and water as byproducts, making it an immaculate process. However, the operating cost of this fuel cell is expensive, requiring high investments in platinum catalysts, which makes the energy produced expensive and unattractive. Researcher Ali Basem utilizes deep neural networks to optimize the parameters of PEM fuel cells, studying the fuel effect of flow orientation on power output to improve commercial availability. Basem builds off of previous innovations in PEM fuel cells, specifically serpentine channel fuel cells. This structure (seen in Fig. 5) has been proven to increase fuel cell performance significantly. The primary purpose of the deep neural network in this experiment is to predict additional points of the inlet humidity effect on output power. Initial data were analyzed from numerical simulations and validated through comparisons to previous studies. Then, a 4th-order polynomial regression is applied to the series of points generated by the deep neural network to derive a function that determines the optimal humidity to be at 14.57%. The lack of experimental study likely stems from the fact that it is a project undertaken by a single researcher, which may contribute to the low accuracy of the deep neural network model, at 87.53% [9].
This experiment studies three innovative flow orientations within
Proton exchange membrane fuel cells remain financially unattractive, despite being beneficial, as they produce clean energy with minimal to no harmful byproducts. In this experiment, optimal operating parameters were determined to maximize specific characteristics of PEM fuel cells. The use of a deep neural network offers the potential for significant efficiency gains. The improvement in efficiency, however, was not enough to make PEM fuel cells more cost-efficient than other renewable energy sources, such as biofuels. However, the advancements highlighted in this experiment make PEM-fuel cell power increasingly likely.
Researchers S. Polymeni et al. utilized AI to enhance user experience and develop a cost-effective solution to optimize the refueling process. Their proposal is summarized as a model that predicts a vehicle’s energy consumption using existing or predicted traffic data to determine optimal charging schedules and locations [10]. Energy consumption forecasts will inform the scheduling software that each driver should use based on destination and location, and when and where they should refuel.
The researchers implemented an autoregressive integrated moving average model (ARIMA). The ARIMA model has three components: the autoregressive component of the evolving variable of interest is based on past values, the integration component is based on differences in data values, and the moving-average component is based on past errors. The ARIMA model is designed to predict future points in a time series. In this experiment, it is utilized to predict energy consumption.
Artificial 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.
AI-assisted refueling for hydrogen-powered vehicles
The reality is that existing infrastructure limitations often hinder the implementation of innovative and novel technologies. A prime example of this is the increasing advancement of hydrogen- powered vehicles, a sustainable alternative to traditional gasoline vehicles. They are gaining popularity as new technologies continue to advance them. However, a limiting factor is the lack of refueling stations for fuel cell electric vehicles. Gasoline and diesel refueling stations are numerous and accessible, which makes it uncomfortable for electric cars to switch to fuel cells. Consumers often trade off their time and money when they switch to FCEV, ultimately ruining the consumer experience.
Conclusion:
Artificial Intelligence and its capabilities continue to grow year by year. With those innovations come novel applications in nuclear energy, hydrogen fuel cells, biodiesel, and sustainable aviation fuel. AI’s applications range from faster simulation of complex phenomena to predicting fuel characteristics from input data. AI’s applications only accelerate the advancement of fuel technology, making it quicker and cheaper. Despite this, AI still faces challenges regarding accuracy and computing power. Our current processing units are expensive and demanding, while complex neural networks provide highly accurate results; others do not. However, the role of Artificial Intelligence in society continues to grow as more funding is poured into this field. With further advances in neural network models, optimizations, and improvements that would have taken years of experimental trials are now achieved in seconds, accelerating advancements to new heights.
Works Cited
[1] S. A. Cancemi, A. Ambrutis, M. Povilaitis, and R. Lo Frano, “AI-Powered Convolutional Neural Network Surrogate Modeling for High-Speed Finite Element Analysis in the NPPs Fuel Performance Framework,” Energies, vol. 18, no. 10, p. 2557, Jan. 2025, doi: 10.3390/en18102557.
[2] “Fuel Reliability Guidelines: Pellet-Cladding Interaction.” Accessed: Nov. 07, 2025. [Online]. Available: https://www.epri. com/research/products/1015453
Figure 5 The graph on the left shows the pressure loss in the 4 different orientations, and the graph on the right shows the consumed power at 0.4 V, 0.5 V, 0.6V, and 0.7V [9]
[3] “Von Mises Criteria - an overview | ScienceDirect Topics.” Accessed: Nov. 14, 2025. [Online]. Available: https://www-
sciencedirect-com.proxy.library.stonybrook.edu/topics/
Figure 6 Comparison of ARIMA-predicted fuel consumption and Actual fuel consumption [10].
Looking at Figure 6, the results are surprisingly accurate, considering the ambitious nature of this goal. Predicting the future based on statistics and probability is a challenging task, as it is impossible to fully model randomness. The values are accurate to the extent that they are much higher than the actual values. This poses issues, as energy consumption forecasting aims to optimize the refueling process by carefully scheduling individual drivers to avoid frustrating long queue times. This prediction could optimize the process to an extent, but it can never fully achieve complete optimization. This experiment is excellent proof of concept, as the idea faces other limitations. If faced with large fleets of vehicles, the data processing and infrastructure requirements would overload this process, leading to prolonged process times and a resurgence of the infrastructure issue.
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