ANALYTICAL INSTRUMENTATION
a total correlation coefficient of 0.994 [6]. The closer this value is to 1, the stronger the correlation.
Therefore, this is a potent tool that can be used to assess and design sustainable aviation fuels that meet the drop in fuel standards [6].
ANN in Biodiesel:
Figure 2: The correlation between predicted LFL values from multiple theoretical models and the experimental LFL values compared to the correlation shown by the ANN LFL model (blue) [6].
What makes Artificial Neural Networks so effective is that they can account for hidden, complex effects or layers by implementing artificial neurons. Some hidden complex effects include molecular structure and intramolecular and intermolecular forces. To avoid an overly complex model that would have resulted in a waste of resources, the researchers used the 43 most significant classes of hydrocarbons, which served as a surrogate for hydrocarbons with similar LFL within a 1% deviation. This model is so accurate that apparent deviations from traditional jet fuel values indicate that the fuel is “non- drop-in,” meaning it cannot be used as jet fuel with modification.
predict material yielding under complex loading conditions [5]. The researchers determined that the CNN model is 17x faster than traditional Finite Element Analysis models, taking the FEA model 17.08 seconds to generate results [2]. These results show that the new CNN model is more efficient than traditional linear FEA models.
A total of 3,360 images were used to train Cancemi’s model [1], which aimed to predict displacement, von Mises stress, and creep strain under given conditions. These images were generated using a finite element simulation depicting von Mises stress, creep strain, and displacement of the top, bottom, and side views under different conditions. The accuracy of the results produced was astonishing. Figure 1 displays the real average von Mises stress, compared to the predicted average von Mises stress [2]
The best performing models had a mean squared error of 0.000678 [2]. This indicates an extremely high level of accuracy for these models. The CNN model’s highly accurate pattern recognition capabilities enable it to produce simulation results at a fraction of the time while maintaining traditional FEA model accuracy. Cancemi’s study is extremely promising and is still in its early stages of development. These results demonstrate a significant reduction in processing time, with minimal compromise in error rates. With further research, pellet failures in nuclear reactors will be easier to predict and prevent, making nuclear energy a safer, more appealing, and cleaner alternative.
AI in Sustainable Aviation Fuel Blend
A previous study utilized a CNN surrogate model to simulate complex pellet cladding interactions, aiming to predict cladding failures. Another powerful AI model is an ANN model, or an Artificial Neural Network model. This model processes information through layers of interconnected nodes, mimicking how the human brain functions. The input data is analyzed, and subsequently, it is received by the hidden layer, which transforms the data through weighted connections and activation functions [6]. Finally, the model produces predictions or results. Researchers Z. Liu et al. trained an ANN model to cover the most significant classes of sustainable aviation fuel blends, aiming to accurately predict the low flammability limit (LFL) of fuel blends. LFL or the lowest fuel-gas mixture concentration that will facilitate a self-supported flame. In other words, the potency and efficiency of a fuel are better, the lower the flammability limit. The researchers fed the input matrix with data on the LFL of “drop-in fuel” which can be used as an immediate fuel alternative with no additional steps. The ANN model performed exceptionally well in predicting the LFL, as shown below, compared to other theoretical models. The blue dots (ANN LFL model) exhibit a strong correlation between the predicted and actual values, with
Currently, biodiesel accounts for 1% of the world’s energy sources and is growing in number. Considering how much of our energy comes from petroleum derivatives (around 30% in 2024), we are quite a length away from replacing diesel with biodiesel [7], [8]. Biodiesel inherently cannot compete with the raw performance capabilities of diesel and faces issues in engine and cost efficiency. Researchers U. Rajak et al. built upon previous research regarding the effect of biodiesel-enriched diesel blends in direct injection of diesel engines. They plan to examine further the effects of engine speed on engine efficiency and emissions using ANNs. The use of AI circumvents the expensive and rigorous testing that comes with experimentation under specific constraints. The researchers initially conducted 75 trials consisting of different diesel and soybean biodiesel blends at different engine rpm. Simple tools were used to determine engine efficiency and emissions. These tools had a combined uncertainty of ± 3.57. This uncertainty will most likely be reflected in the mean squared error (MSE) of the ANN model. The mean squared error is used as a value to determine the accuracy of a model; the closer it is to zero, the more accurate the model. Experimental data, along with data from databases, were then compiled and randomized. 70% of the data was used to train the ANN model, 15% to test, and the remaining 15% to validate. The model reported a mean squared error of 0.9 [7]. This series of training, testing, and validation was conducted to predict values for brake thermal efficiency, volumetric efficiency, CO2
emissions, and NOx emissions. Both brake thermal
efficiency and volumetric efficiency are crucial in determining an engine’s overall efficiency. However, it is worth noting that the model’s mean squared error for predicting CO2
emissions was particularly low compared to the other models.
Figure 3 Training, Testing, Validating tests for a. BTE, b. VE, c, CO2
Emissions, and d. NOx
Emissions [7].
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