Pathmanaban Pugazhendi

Improving the efficiency and emission characteristics of biodiesel fueled CI engine remains challenging due to limitations in fuel reactivity and combustion behavior. This study investigates the combined effect of dimethyl carbonate (DMC) and cobalt chromite nanoparticles on engine performance and emissions, supported by machine learning-based prediction. Experiments were carried out on a single-cylinder diesel engine using five fuel blends including diesel, J20, J20D10, J20D10C50, and J20D10C100 across the full load range. The J20D10C100 blend delivered the highest BTE of 29.67%, showing improvements of 13.57% over diesel and 20.78% over the J20 blend. Brake specific fuel consumption decreased to 0.375 kg per kWh, corresponding to reductions of 5.44% and 6.38% compared with diesel and the J20 blend. At full load, CO decreased from 0.53% to 0.454%, HC reduced by 14.6%, and nitrogen oxides decreased from 1070 ppm to 990 ppm. Smoke opacity showed a reduction from 67.21% to 65.19%. Among the predictive models, gradient boosting achieved the highest accuracy with an R ² of 0.965, followed closely by random forest with 0.964. The results confirm that the proposed fuel formulation enhances engine performance while reducing emissions, which supports reliable predictive modeling.

The greenhouse gas benefit of battery electric vehicles (BEVs) depends on the electricity that charges them, yet country comparisons rely on annual-average grid intensities and fixed vehicle parameters. We combine Ember grid data for 209 countries (2000–2025) with a 10,000-draw Monte Carlo life-cycle model of a medium car, using monthly data for 21 markets to test seasonal effects. BEVs emitted less than petrol cars with a probability ≥ 0.95 in 130 countries, whereas India (0.47) and South Africa (0.51) remained undecided. Wind and solar growth widened the within-year range of grid intensity from 17 % to 28 % of the annual mean in 2019–2025, but changed annual charging intensities by about 1 % or less in 20 of 21 markets. For coal-intensive grids, relative real-world vehicle efficiency, lifetime and grid trajectory decided the verdict; India’s probability ranged from 0.24 to 0.98. Such countries should promote efficient vehicles alongside power-sector decarbonisation.

Cylindrical plunge grinding of piston rings is a precision finishing operation, and its dimensional accuracy governs engine sealing and emission compliance. It is widely assumed that such accuracy can be controlled through fixed machine settings; this study tests this assumption under realistic production noise. An industrial dataset of repeated dimensional measurements collected across 30 experimental runs with 2 recorded noise sources (mandrel type and ring position) was analysed. Three predictive models were evaluated using a grouped validation scheme that kept all repeats of a condition together, avoiding the optimistic accuracy that ordinary random splitting produces. All models predicted the held-out conditions no better than a simple average, showing that unmeasured disturbances rather than adjustable settings dominate variability. Therefore, a model-free robust design framework was developed, ranking the conditions directly based on their observed performance and scatter without a fitted model. The recommended shallow-dressing setting achieved a capable performance on two of the three dimensions, while the third indicated an irreducible variability floor associated with unmeasured disturbances. This study contributes by examining the parameter-only predictive performance under a leakage-free validation protocol and by outlining the in-process sensing layer that a reliable grinding digital twin would require.

Renewable fuels and advanced nanomaterials are being investigated to address the growing demand for sustainable energy and reduced diesel engine emissions. Biodiesel blends and nanocomposite additives can improve CI engine efficiency and emission characteristics. This research investigates the combined use of hydrogen enrichment and a multifunctional CuO‐WO ₃ ‐g‐C ₃ N ₄ (CWOGCN) ternary nanocomposite to improve the performance and emission characteristics of a CI engine fueled with a 20% honge oil methyl ester blend (H20). The developed CWOGCN nanocomposite was characterized using XRD, SEM–EDX with elemental mapping, FTIR, and XPS analyses. The results confirmed the crystallinity, surface morphology, elemental composition, chemical states, and uniform elemental distribution of the synthesized nanocomposite. Engine experiments were conducted using 50 and 100 ppm CWOGCN nanocomposites with 10 LPM hydrogen enrichment under varying engine load conditions. The highest brake thermal efficiency (BTE) of 30.16% was obtained at 4.78 kW, while the lowest brake specific fuel consumption (BSFC) was 0.446 kg/kWh. Compared with diesel operation, the hydrocarbon emission was reduced to 38 ppm, carbon monoxide emission decreased to 0.12%, NO _x emission was limited to 842 ppm, and smoke opacity was reduced to 42%. The RSM models showed high predictive capability ( R ² = 0.991), while machine learning models, particularly Gradient Boosting, showed superior nonlinear prediction performance. The combined experimental, RSM, and machine learning analysis demonstrated that H20CWOGCN100H provided improved thermal efficiency and reduced exhaust emissions, highlighting the potential of hydrogen‐enriched ternary nanocomposite biodiesel blends for cleaner and more efficient CI engine operation.

This study provides a systematic robustness evaluation of classical machine learning for vibration-based bearing fault detection in low-RPM internal combustion engines (1000–2000 RPM) across a controlled temperature × humidity grid, a regime underrepresented in benchmark datasets that emphasise high-speed applications. A publicly available dataset from a 658cc engine (–10 °C–45 °C, 0%–100% humidity) was analysed; vibration features were derived from the non-zero channels of a tri-axial acquisition, with the bearing-housing vibration carried primarily by channel Ch3. To prevent temporal leakage, 390 263 continuous measurements were aggregated into 89 steady-state units, each spanning 90 s, yielding a deliberately independence-preserving but low sample-to-feature ratio (89:92). Four algorithms Random Forest, Support Vector Machine, Logistic Regression, and Neural Network were evaluated using stratified 5-fold cross-validation. All models achieved apparent accuracy exceeding 95%, with Random Forest performing best (97.8% ± 2.7%), but no statistically significant differences were found (Friedman test, p = 0.732). Vibration features, particularly crest factor and root mean square, provided the greatest discriminative power, while environmental factors accounted for less than 17% combined importance. The near-perfect linear separability (98.2% with Logistic Regression) indicates that the dataset’s binary, controlled-laboratory labelling rather than intrinsic bearing-degradation physics drives the clean classification. Accordingly, the reported accuracies are apparent upper-bound estimates from an exploratory study, not expected field performance; validation on 500–1000 or more samples with progressive-degradation labelling is essential before any operational claim can be supported.

The transient thermal behavior of natural fiber hybrid sandwich composites governs their suitability for thermally loaded structural applications, yet spatially resolved transient response across a single panel is rarely documented. In this study, infrared thermography was used to characterize the location-dependent transient surface-temperature response of an areca–glass fiber hybrid epoxy sandwich composite under localized hot-air heating. A controlled protocol (60 s heating, 180 s cooling) was applied at central, corner, and edge locations of a 30 cm × 30 cm panel, and the surface temperature was monitored at 16 grid points using a FLIR One Pro LT camera. Exponential heating and Newtonian cooling expressions were fitted to extract effective time constants at each location. The corner location reached the highest peak surface temperature (42.0 °C) and the slowest response (heating time constant τh = 24.4 s; cooling τc = 45.1 s), whereas the edge location responded fastest (τh = 9.8 s, τc = 19.6 s), an approximately 2.5-fold variation attributable to the differing lateral heat-dissipation pathways available at each location. Heating proceeded 2.7–3.1 times faster than cooling at all locations. The fitted expressions described the measured curves with R² > 0.95 for heating and R² > 0.80 for cooling (RMSE < 0.6 °C). The extracted time constants are configuration- and geometry-dependent descriptors of the surface response under the imposed boundary condition rather than intrinsic material properties, and independent measurement of thermophysical properties would be required for material-level characterization. This study indicated that the proposed infrared thermography procedure provided a valuable reference for the reproducible, spatially resolved comparison of transient thermal response in natural fiber hybrid sandwich composites.

Natural fiber hybrid sandwich composites are emerging as bio-derived structural materialsfor thermally relevant engineering applications, yet the relationship between their heterogeneous layered architecture and the resulting transient thermal response remains insufficiently characterized. This study investigates the location-dependent transient thermalbehavior of areca–glass fiber hybrid epoxy sandwich composites under localized heatingusing infrared thermography. A controlled heating protocol (60 s heating, 180 s cooling)was applied at central, corner, and edge locations, with temperature monitored at 16 gridpoints. Exponential heating models and Newton’s cooling law were employed to extractthermal time constants. Corner regions exhibited the highest peak temperatures (42.0◦C)and slowest dynamics (heating time constant τh = 24.4 s; cooling τc = 45.1 s), whileedge regions responded fastest (τh = 9.8 s, τc = 19.6 s), representing a 2.5-fold variationattributable to differences in heat dissipation pathways governed by the layered sandwichgeometry. Biot number analysis (Bi = 0.036–0.090) is consistent with the lumped capacitance assumption. Heating occurred 2.7–3.1 times faster than cooling at all locations,indicating fundamental asymmetric thermal behavior. Validated analytical models achievedstrong agreement with experimental data (R2 > 0.95 for heating; R2 > 0.80 for cooling;RMSE < 0.6◦C). These results identify location within the sandwich panel as a first-ordermaterial-design parameter for transient thermal response in this class of bio-derived hybridmaterials, with implications for component-level material selection in thermally loadedstructural applications.