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Madan Mohanka set up Tega Industries Ltd. in 1976 to manufacture abrasion-resistant rubber mill lining products used in the mining and mineral processing industries. In 2011, as part of its inorganic expansion strategy, Tega bought a company in Chile. However, post-acquisition, several managerial, legal and commercial problems crept up in its manufacturing facilities in Chile, leading to financial downturn in Tega's fortunes in 2016 and compelling it to planning a revival between 2016-19. However, political unrest and Covid 19 uncertainty has caused a dilemma related to further investments worth INR 1.25 billion. Management is contemplating the next steps.

This paper aims to present the design of a particular non-reactive test rig for combustion swirlers and first stage turbine nozzles. The test rig is required for important experimental activities aimed at the optimization of a specific class of gas turbines.

A multi-disciplinary team performed the design process by following a tailored design approach, which has been developed for the specific case. The design outcomes allowed to build a fully functional test rig to be introduced in a test cell and then to perform preliminary experiments about the fluid dynamic behaviour of the turbine elements.

The followed design approach allowed to efficiently perform the task, by supporting the information exchange among the different subjects involved in both the conceptual and the embodiment design of the test rig. Additionally, the performed experiments allowed to achieve a final configuration that makes the test rig a valuable test case for combustor-turbine interaction studies.

The study described in this paper is focused on the design of a specific test rig, used for first validation tests. However, the achieved results (both in terms of design and test) constitutes the underpinning of the in-depth investigations to be performed in the next steps of the experimental campaign.

To the best of the authors’ knowledge, the present paper is the first one that comprehensively describes the design activity of an experimental test rig for turbine application, also providing indications about the specific methodological procedure used to manage the process.

This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.