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Fuel consumption in heavy-duty off-road machinery depends on a wide range of interacting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parameters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption Assessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term operational data collected during real industrial work. Its universal structure makes it applicable not only to mining equipment, but also to construction machinery and transport vehicles, as well as conventional passenger cars, where it offers a systematic procedure for estimating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency.

This study presents an integrated experimental and computational investigation into the thermal and hydraulic performance of three oxide-based nanofluids: aluminum oxide (Al2O3), titanium dioxide (TiO2), and copper oxide (CuO) for advanced engine cooling applications. A custom-built test rig was used to assess nanofluid behavior under varying flow rates, nanoparticle volume fractions, and temperature gradients, replicating realistic engine conditions. According to the results, at ideal concentrations, CuO nanofluids continuously demonstrate better heat transfer properties, outperforming TiO2 by up to 15% and AlO3 by 7%. However, performance plateaus beyond 1.5% volume fraction due to increased viscosity and pressure drop. A multilayer feedforward artificial neural network (ANN) model was developed to predict convective heat transfer coefficients and friction factors based on experimental inputs, achieving a mean absolute percentage error below 5% and a coefficient of determination (R2) exceeding 0.98. The ANN demonstrated robust generalization across varying operating conditions and nanoparticle types, confirming its utility for surrogate modeling and optimization. This work is distinguished by its dual focus on thermal efficiency and hydraulic stability, as well as its use of data-driven modeling validated by empirical results. The findings provide actionable insights for thermal management system design in internal combustion, hybrid, and electric vehicles, where efficient, compact, and reliable cooling solutions are increasingly vital. The study advances the practical application of nanofluids by offering a comparative, ANN-validated framework that bridges the gap between lab-scale performance and real-world automotive cooling demands.

Presently, most passive safety tests are performed with a precisely specified seat position and carefully seated ATD (anthropomorphic test device) dummies. Facing the development of autonomous vehicles, as well as the need for safety verification during crashes with various seat positions such research is even more urgently needed. Apart from the numerical environment, the existing testing equipment is not validated to perform such an investigation. For example, ATDs are not validated for nonstandard seatback positions, and the most accurate method of such research is volunteer tests. The study presented here was performed on a sled test rig utilizing a 50cc Hybrid III dummy according to a full factorial experiment. In addition, input factors were selected in order to verify a safe test condition for surrogate testing. The measured value was head acceleration, which was used for calculation of a head injury criterion. What was found was an optimal seat angle −117°—at which the head injury criteria had the lowest represented value. Moreover, preliminary body dynamics showed a danger of whiplash occurrence for occupants in a fully-reclined seat.

This study presents an integrated experimental and computational investigation into the thermal and hydraulic performance of three oxide-based nanofluids: aluminum oxide (Al[sub.2]O[sub.3]), titanium dioxide (TiO[sub.2]), and copper oxide (CuO) for advanced engine cooling applications. A custom-built test rig was used to assess nanofluid behavior under varying flow rates, nanoparticle volume fractions, and temperature gradients, replicating realistic engine conditions. According to the results, at ideal concentrations, CuO nanofluids continuously demonstrate better heat transfer properties, outperforming TiO[sub.2] by up to 15% and AlO[sub.3] by 7%. However, performance plateaus beyond 1.5% volume fraction due to increased viscosity and pressure drop. A multilayer feedforward artificial neural network (ANN) model was developed to predict convective heat transfer coefficients and friction factors based on experimental inputs, achieving a mean absolute percentage error below 5% and a coefficient of determination (R[sup.2]) exceeding 0.98. The ANN demonstrated robust generalization across varying operating conditions and nanoparticle types, confirming its utility for surrogate modeling and optimization. This work is distinguished by its dual focus on thermal efficiency and hydraulic stability, as well as its use of data-driven modeling validated by empirical results. The findings provide actionable insights for thermal management system design in internal combustion, hybrid, and electric vehicles, where efficient, compact, and reliable cooling solutions are increasingly vital. The study advances the practical application of nanofluids by offering a comparative, ANN-validated framework that bridges the gap between lab-scale performance and real-world automotive cooling demands.