The signal resolution is related to the sampling frequency and heavily depends on the sensor manufacturing properties, which do not improve much after being selected and are not worth focusing on. The last issue of uncorrelated noise compared to correlated noise was addressed in previous efforts by [PERSON] et al. [19] using periodic fluid injection. Using this method, the authors showed how manually adding the correlated signals was able to improve the CCF method. The methodology is intended to support monitoring and situational awareness in advanced reactors and integral reactors; however, concerns remain regarding the implementation of periodic injection in a nuclear primary system. The proposed magnitude of injection is not sufficient to affect bulk temperature and is meant to only introduce local perturbations. A small amount of flow could be diverted from one side (i.e., inlet or outlet) of a heat exchanger or steam generator to the other side (i.e., outlet or inlet) to introduce a local temperature perturbation without affecting the inventory of the coolant. The purpose of this work is to demonstrate the efficacy and optimization of injection-enhanced CCF flow estimation. Specific implementation in a reactor system will depend on the particulars of that system. Further, the proposed method could be used at any location with redundant sensors that provide time-delayed signals relative to each other. In fact, it may be preferable to implement this solution in multiple locations (e.g., cold leg and hot leg) to support more complete flow monitoring. This paper follows up on previous experimental results and focuses on verifying those results and optimizing the injection configuration using Computational Fluid Dynamics (CFD) simulations. The conditions considered in this work and the previous experimental work are not directly relevant to NPPs, but the findings can be extrapolated to such systems. In the experimental flow loop, a series of flow measurements were conducted with different setups for the sensor and injection pipe locations. However, these configurations provide only limited temperature data at specific locations. CFD simulations can provide a larger data set to investigate the sensitivities of the CCF measurement method. To analyze and optimize the performance of CCF flow estimation under different flow rates of the mean flow, it is necessary to collect the temperature and velocity distribution to observe how the injected flow spreads out in the pipe and to determine the best locations for the sensors. This application of CFD aims to simulate the experimental conditions to collect the temperature and velocity in ANSYS Fluent, v19.3.0 [20]. There are two main studies conducted in this paper. The first study aims to verify and validate the ability to simulate the flow (velocity and pressure fields) and heat transfer (temperature fields) behavior of the experiments in the CFD simulation. The second and most important study is the optimization of sensor pair locations in the mean flow based on flow-rate estimation using the CCF method. The optimization study is conducted with a range of flow rates for the mean flow and injected flow. The resulting information from the second CFD simulations will enable a more detailed study of the impact of sensor location on the accuracy of the CCF method. Section 2 briefly introduces the CCF-based flow estimation methodology. The subsequent computational domain section discusses the CFD simulation model built in Fluent, including the geometry, boundary and initial conditions, meshing, and computational setup. Next, is Section 4, which mainly discusses the two main studies: the validation experiment and the optimal temperature sensor location investigation. The last section provides the conclusions.