## 1 Introduction  
In nuclear power plants, boiling occurrence on the fuel assembly can lead to a boiling crisis and damage some fuel elements within the core assembly. It is then an important matter to be able to determine the conditions leading to DNB (departure from nucleate boiling) as accurately as possible. It is believed that such a goal will require a better understanding of the physical mechanisms driving the nucleate boiling phenomena. The purpose of this work is to study and characterize nucleate boiling from the onset of nucleate boiling (ONB) up to the boiling crisis triggering, under working conditions that are prototypical of PWR (pressurized water reactor) thermal-hydraulic operating conditions. PWR's primary circuit is characterized by a pressure of 155 bar, a temperature close to 300 \({}^{\circ}\)C, and a flow rate of 68,350 m\({}^{3}\)/h. In terms of modeling, nucleate boiling is often described in CFD tools (NEPTUNE CFD, Fluent) using the heat flux wall partitioning approach as first introduced by [PERSON] and [PERSON] [1]. This model assumes that the wall heat flux \(q_{w}^{\circ}\) can be divided in three components--the evaporation heat flux \(q_{e}^{\circ}\), the quenching heat flux \(q_{q}^{\circ}\), and the single-phase convective heat flux \(q_{e}^{\circ}\), respectively (see Figure 1). The evaporation heat flux depends on three parameters--the nucleation site density \(N^{\prime\prime}\), the bubble departure frequency \(f\), and the lift-off bubble diameter \(D_{b}\). These parameters are often provided by different correlations [2; 3]. Unfortunately, there are two main drawbacks. First, most of those correlations are based on experimental measurements performed at low pressure and low-temperature conditions or for different working fluids (refrigerant, e.g., R12, R134a, R113). For instance, the bubble diameter \(D_{b}\) is often determined using the well-known correlation by Unal [3]. Despite this correlation often being used for high-pressure and high-temperature steam/water flows [4], one should notice that the high-pressure database for this correlation is based on the [PERSON] experiments, which were performed with sodium [5]. Considering this correlation is dimensional, it is legitimate to wonder about the applicability of this sub-model to PWR thermal-hydraulic conditions. Secondly, those parameters were not simultaneously measured during the experiments. This means that from a practical point of view, two parameters were measured (for example the bubble diameter \(D_{b}\) and the bubble detachment frequency \(f\)) and the remaining parameter (in this example, the nucleation site density \(N^{\prime\prime}\)) was fitted by comparing the model prediction to some secondary experimental measurements, such as the wall temperature or the void fraction. This approach may be risky because even if the final calculations seem to be consistent, they may result from error compensations within the model. To ensure the validity of the partitioning model, it seems necessary to perform direct and complete measurements of all the sub-model parameters in the operating conditions as close as possible to the targeted ones (PWR). To achieve this goal, it is worth mentioning the work of [PERSON] et al. [6], who designed an experimental setup that allows measurements of each parameter mentioned above (bubble diameter \(D_{b}\), bubble detachment frequency \(f\), and nucleation site density \(N^{\prime\prime}\)). This setup is made of a test section (square cross-sectional) equipped with quartz windows to obtain optical access to the flow. The heater (where nucleate boiling occurs, see  
Figure 1: Diagram of the heat flux wall partitioning ([PERSON] and [PERSON] [1]), \(q_{e}^{\prime\prime}\) is the evaporation heat flux, \(q_{q}^{\prime\prime}\) is the quenching heat flux, and \(q_{c}^{\prime\prime}\) is the single-phase convective heat flux. Figure 2) is made of a sapphire plate (substrate) coated with a thin layer of indium-tin oxide (ITO).