The aim of the research is to improve the indicators assessment accuracy of the vehicle energy load by improving the method of experimentally-theoretical determination of the aerodynamic drag parameters of vehicle in motion. To achieve this goal, it is necessary to solve the problem of determining the dependence of the energy load level on vehicle speed with varying frontal aerodynamic drag coefficient. Studies we carried out to clarify the calculation of the parameters of vehicle aerodynamic drag in motion made it possible to clarify the correlation between the actual effective engine capacity and the maximum kinetic energy of vehicle at translational motion. When determining the vehicle aerodynamic drag, the constant coefficient of aerodynamic drag is used depending on the speed in all range of vehicle speeds. This leads to significant mistakes in determining the necessary engine capacity expendable to overcome the aerodynamic drag, and vehicle fuel consumption. Analytical expressions, allowing to take into account additional energy losses and correlation between the kinetic energy of the vehicle steady motion and the effective engine capacity are obtained. The correlation coefficient between the kinetic energy of vehicle in motion and the effective engine capacityw K have been proposed. Studies have shown that if speed of vehicle increases the indicator w K will monotonously decrease in the range of actual speeds.

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Randomization, one of the fundamental principles of statistically designed experiments, is not always easy to implement in practice. In fact, many industrial settings involve factors of interest that are difficult to change, requiring some modification to a completely randomized test sequence. Practical issues associated with restricted randomization commonly arise regarding design efficiencies, design requirements for error estimation, overall ease of software-assisted analysis, required replication, tests for nonlinearity, and sequential testing plans. The purpose of this paper is to deal directly with these roadblocks to efficient and effective experimentation under randomization restrictions. The general approach involves modifying standard split-plot designs with appropriate augmentation followed by a two-stage model building method using a standard general linear model framework. An alternative procedure for analyzing two-level factorial split-plot designs is provided for commonly occurring situations involving more than one hard-tochange factor. The proposed methods were developed and implemented in conducting experiments for wind tunnel applications. These tests serve as case studies for the paper. We also suggest possible methods for limiting the replication required for estimating whole plot error.

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