1. Introduction
The European Commission has set a target of reducing the greenhouse gas (GHG) emissions of the European Union by over 90% by 2050, compared to the 2000 levels [
1]. For the aerospace industry, this objective translates into a drastic reduction in sector emissions by 2030, reaching carbon neutrality in 2050, and reducing Nitrogen Oxide (NO
x) emissions by 90% compared to those of 2000. In the context of increasing air traffic, this commitment requires the aerospace industry to adapt and propose innovative and ambitious solutions to meet environmental challenges while ensuring flight safety, complying with emission noise standards, and ensuring its economic sustainability.
When it comes to technology, the lubrication of power transmissions is one of the technological elements that is subject to risk assessments while contributing to the overall efficiency of mechanical transmissions. While a small amount of oil is sufficient to form a film that ensures contact surface separation between moving mechanical parts, a much larger amount is necessary for cooling these components. In aerospace mechanical transmissions subject to high rotational speeds, several studies have shown that losses generated by the interaction of oil and rotating components constitute a major source of dissipation [
2]. As these losses increase with the amount of lubricant used, this quantity must be adjusted to avoid degrading the efficiency of the transmission under consideration [
3,
4].
Gears are a priority as they convey the majority of the power and must therefore be properly cooled from the lubricant. Blok [
4] defines a convective exchange on the tooth of a gear based on fling-off cooling. This result has been used in several studies to estimate an average convective heat transfer coefficient which can be employed as a boundary condition in finite element models. As an example: (i) Patir N. and Cheng H.S. [
5] proposed a model to predict the overall temperature of a mechanical transmission; (ii) Long [
6] simulated the temperature of gear teeth at high speeds, taking into account the effects of gear geometry, rotational speed, and lubrication conditions; (iii) Townsend et al. [
7] developed a computer program for predicting gear-tooth temperatures and the importance of oil jet cooling in preventing scoring, with the need for an experimental determination of heat-transfer coefficients. In addition to the above-mentioned studies, the works of Xing C. [
8], G. Niemann [
9], Peng Jie [
10], and Duan Yang [
11] showed that for gears operating under high speeds, a pressurized oil jet is required to provide adequate cooling and prevent gear failure caused by high bulk temperatures.
In order to optimize gear lubrification, several studies have developed numerical approaches to investigate this question: Xiaozhou H. [
12] simulated the oil jet lubrication of meshing spur gears with computational fluid dynamics (CFD) simulations based on the Boltzmann lattice method (LBM). The results show that lubrication performance is improved by adjusting the injection parameters and that the LBM method can be used to further investigate the effects of heat in gears. The volume of fluid (VOF) method can be used to model immiscible multiphase fluid systems at the interface scale and was used by Fondelli T. [
13,
14], Dai Y. [
15,
16,
17], and Wang J. [
18,
19,
20]. The works of Keller M. [
21], Liu H. [
22], and Ji Z. [
23] on Smoothed Particle Hydrodynamics (SPH) can also be mentioned. The finite volume (FV) method is another way of representing partial differential equations in the form of algebraic equations. This method is based on the work of Concli F. [
24] and Fondelli T. [
25].
Studies from the literature have concluded that the accurate prediction of the gear bulk temperature using numerical methods is possible, despite difficulties in obtaining accurate values of frictional heat flux and heat transfer coefficients. In an attempt to study a distribution of the convective exchange coefficient along the gear, Von Plehwe et al. [
26] developed a new approach based on a comparison between a specific test rig and finite element model. However, all the cases assumed that this exchange occurs over the entire tooth. This is not necessarily the case, as suggested by the work of Akin [
27]. His study shows that the industrial standard nozzle orientation can cause incipient gearing failure in high-speed drives due to the deprivation of primary impingement on the gear. It also suggests that a minimum jet velocity is necessary to lubricate the gear teeth, and a minimum offset is required to guarantee impingement on the gear under certain operating conditions. The maximum impingement depth is not more than 10% of the tooth profile depth.
Based on previous studies, it appears that experimental data on the thermal heat exchange between a rotating gear and an oil jet flow are relatively scarce. To overcome this lack, a new test rig has been designed to directly study the above-mentioned thermal resistance without making assumptions about surfaces in convective exchange with oil. The proposed test bench allows to simulate real operating conditions and measure the performance of gear oil heat exchanges under controlled conditions. With this test bench, it is possible to study in-depth the various lubrication parameters, such as nozzle geometry, injection speed, and oil flow rate, and their influence on gear performance. The aim of this paper is to present the experimental procedure which was developed to determine the thermal resistance of convection between oil flow and a rotating sample. To illustrate this procedure, this work focuses on studying the thermal resistance of two types of test samples, namely a disc and a spur gear, by varying a parameter (oil jet velocity, oil flow rate, or rotational speed) in each experiment. First, the test bench is presented, then the operating procedure and the related assumptions are explained. Finally, the experimental results are compared with existing models and discussed.
3. Experimental Approach
3.1. Protocol
After positioning the test sample and the injection nozzle with the associated type K thermocouples, the oil tank heaters and pumps must be activated. Once the oil has reached the desired temperature, the test sample is set into rotation, and the valve is opened. Upon completion of the test, the valve is closed, and the test sample is stopped.
The data collected include the temperature measurements from the thermocouples and experimental constants such as flow rate, rotational speed, geometry, and positioning of the nozzle(s) and the test sample. After the data have been collected, they are processed to determine the time constant and calculate thermal coefficients such as (hS) product or thermal resistance. These results are then analyzed.
3.2. Temperature Measurement
The gear, oil, and housing air temperatures obtained from the test are shown in
Figure 8. Oil corresponds to the temperature of the oil at the nozzle outlet, Air corresponds to the temperature of the air inside the housing, and Gear represents the bulk temperature of the gear. The characteristics of this test are given in
Table 5.
Figure 8 highlights the different temperatures associated with the studied gear, including heat exchanges with the oil and the air. The temperatures show that there is a thermal equilibrium at a temperature different from the injection one. Therefore, there is a need to construct a thermal model to correctly identify and quantify the two phenomena in order to isolate the convection between the oil and the gear.
3.3. Time Constant Determination
The data retrieved from the experiment are used to specify a test duration for the test, which starts when the injection valve is opened, i.e., when oil is injected onto the test sample.
It is considered that the test sample is impacted by both oil and air. By assuming an isothermal test sample (as shown by previous results), the first law of thermodynamics leads to the following equation:
where
,
are thermal resistance (K/W) between oil (and air, respectively) and the test sample, and
(J/K) is the thermal inertia of the test sample.
The use of the implicit Euler [
14] method results in the following system formulation:
where
i is the index corresponding to a time step,
(°C) is the temperature of the moving device at time step
i,
(°C) is the temperature of the oil at time step
i + 1, and
(°C) is the temperature of the air heat exchange at time step
i + 1.
The thermal time constant τ (s) of a system corresponds to the time required by this system to reach approximately 63.2% of its equilibrium temperature after a sudden change in its operating conditions. Here, the thermal time constants associated with oil and air heat exchanges are defined by:
The theoretical temperature of the test sample is calculated with curve fitting, which estimates the temperature from two parameters,
and
, to be optimized. This method uses the minimization of the differences between the experimental values and the calculated ones:
In this case, the curve-fitting algorithm can be decomposed as follows:
where
(°C) is the initial temperature of the test sample, and
(°C) is the initial temperature measured from the experiment.
Because of this calculation, it is possible to plot the simulated temperature of the test sample (
Figure 9). The curve fitting corresponds to the temperature measurements of the test sample, and the residual error of Equation (5) between the two curves is 0.4 °C over almost 400 measuring points.
3.4. Convective Thermal Resistance
Once the time constant is determined, the thermal resistance (K/W) or
product (W/K) between oil and the test sample is directly obtained by:
Experiments on the repeatability of the injection test bench measurements were carried out. These experiments were essential to ensure the validity and reliability of the obtained results, as well as to improve the performance of the method by identifying potential sources of error.
3.5. Measurement Error and Uncertainty
The error is calculated using the uncertainty range for type K thermocouples, i.e., ±0.5 °C. Once a test and its post-processing have been carried out, it is possible to determine the error induced on the product. By varying the temperature data by ±0.5 °C, the error which is induced on the product is very small: relative error less than 1%. Over several tests, an average of 0.8% relative error on the product was found with this experimental protocol, this post-processing, and this numerical curve-fitting method.