Predictive Optimization of Vibrations in a Biodiesel-Fueled Variable Compression Ratio Engine Using BO-CNN-LSTM
- 1Cukurova University , , ,
Abstract
Integrating sustainable biodiesel blends into variable compression ratio (VCR) diesel engines significantly alters combustion dynamics and structural vibrations. The present study introduces a Bayesian optimized 1D convolutional neural network-long-short-term memory (CNN-LSTM) deep learning framework designed for optimizing the multiaxis engine vibrations. The predictive architecture processes a six-parameter input matrix encompassing engine load, compression ratio (CR), and critical thermophysical fuel properties. A precise evaluation demonstrated the superior fidelity of the hybrid network, achieving up to a coefficient of determination (R2) of 0.985 and a root mean square error (RMSE) of 0.288 m/s2, substantially outperforming optimized support vector machine (SVM) and multilayer perceptron (MLP) benchmarks. Multiaxis spatial stability was visually confirmed using Taylor diagrams, while a 3000-iteration bootstrap analysis validated algorithmic robustness within a narrow 95% confidence interval (0.38–0.79 m/s2). Permutation feature importance and SHapley Additive exPlanations (SHAP) analysis clarified the thermodynamic decision-making process, revealing that elevated engine load and CR intensify mechanical ringing, whereas the higher density and viscosity of biodiesel blends act as natural vibration dampeners. High-resolution contour maps generated through systematic response surface mapping provided precise predictive optimization surfaces. Deploying this advanced methodology establishes a highly reliable, cost-effective virtual calibration tool for next-generation biodiesel-fueled engines, eliminating the necessity for exhaustive physical testing.
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BibTeX
@article{uluocak2026predictive,
title = {{Predictive Optimization of Vibrations in a Biodiesel-Fueled Variable Compression Ratio Engine Using BO-CNN-LSTM}},
author = {Ihsan Uluocak},
journal = {ACS Omega},
year = {2026},
month = oct,
doi = {10.1021/acsomega.6c08418},
url = {https://pubs.acs.org/acsodf/article/11/42/65616/5452989/Predictive-Optimization-of-Vibrations-in-a},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predictive Optimization of Vibrations in a Biodiesel-Fueled Variable Compression Ratio Engine Using BO-CNN-LSTM
AU - Ihsan Uluocak
PY - 2026
DA - 2026/10/16
JO - ACS Omega
DO - 10.1021/acsomega.6c08418
UR - https://pubs.acs.org/acsodf/article/11/42/65616/5452989/Predictive-Optimization-of-Vibrations-in-a
AB - Integrating sustainable biodiesel blends into variable compression ratio (VCR) diesel engines significantly alters combustion dynamics and structural vibrations. The present study introduces a Bayesian optimized 1D convolutional neural network-long-short-term memory (CNN-LSTM) deep learning framework designed for optimizing the multiaxis engine vibrations. The predictive architecture processes a six-parameter input matrix encompassing engine load, compression ratio (CR), and critical thermophysical fuel properties. A precise evaluation demonstrated the superior fidelity of the hybrid network, achieving up to a coefficient of determination (R2) of 0.985 and a root mean square error (RMSE) of 0.288 m/s2, substantially outperforming optimized support vector machine (SVM) and multilayer perceptron (MLP) benchmarks. Multiaxis spatial stability was visually confirmed using Taylor diagrams, while a 3000-iteration bootstrap analysis validated algorithmic robustness within a narrow 95% confidence interval (0.38–0.79 m/s2). Permutation feature importance and SHapley Additive exPlanations (SHAP) analysis clarified the thermodynamic decision-making process, revealing that elevated engine load and CR intensify mechanical ringing, whereas the higher density and viscosity of biodiesel blends act as natural vibration dampeners. High-resolution contour maps generated through systematic response surface mapping provided precise predictive optimization surfaces. Deploying this advanced methodology establishes a highly reliable, cost-effective virtual calibration tool for next-generation biodiesel-fueled engines, eliminating the necessity for exhaustive physical testing.
SN - 2470-1343
ER -