No abstract

No abstract

Purpose -The purpose of this paper is to present an improved computational method for determining the friction factor for turbulent flow in pipes. Design/methodology/approach -Given that the absolute pipe roughness is generally constant in most systems, and that there are few changes to the pipe diameter, the proposed method uses a simplified equation for systems with a specific relative pipe roughness. The accuracy of the estimation of the friction factor using the proposed method is compared to the values obtained using the implicit Colebrook-White equation while the computational efficiency is determined by comparing the time taken to perform 300 million calculations. Findings -The proposed method offers a significant improvement in computational efficiency for its accuracy and is compared 28 of the explicit equations currently in use. Practical implications -This method enables a simplified equation to offer a significant improvement in computational efficiency for its accuracy and is easier to code, enabling engineers to more efficiently calculate frictional pressure loss for flow in pipes. Originality/value -Due to the complexities in flow regime and pipe roughness, there is a limit to the scope for further computational efficiency through simplification of the explicit equation. This paper presents a new method and simplified equation which combined are able to deliver results with similar accuracy to less computationally efficient explicit equations.

During the assembly of internal combustion engines, the specific size of crankshaft shell bearing is not known until the crankshaft is fitted to the engine block. Though the build requirements for the engine are consistent, the consumption profile of the different size shell bearings can follow a highly volatile trajectory due to minor variation in the dimensions of the crankshaft and engine block. The paper assesses the suitability of time series models including ARIMA and exponential smoothing as an appropriate method to forecast future requirements. Additionally, a Monte Carlo method is applied through building a VBA simulation tool in Microsoft Excel and comparing the output to the time series forecasts.

No abstract