## 4 Conclusions and Future Work  
As shown in the previous sections there are many challenges comparing simulation and experimental results. The challenge is to calibrate the CFD models to accurately represent the experimental dust concentration measurements, in particular the ability to compare data directly between experiments and simulations. This has been overcome through the development of a novel method that is easily implemented and delivers repeatable results for experiments. Figure 19: CRF value for experiments and simulations of alumina at flow rates of of \(1.9\times 10^{-2}\) kg/s. Figure 18: Alumina particle mass concentrations [kg/m\({}^{3}\)] from simulation. It has been found that the simulation results can be analysed using the same technique with the exception that the simulation can over predict the concentration ratio. CRF could be used to calibrate CFD simulations. A calibration methodology is presented here:  
1. Simulation data is extracted at the 220 mm plane; a plane above at 320 mm and a plane below at 120 mm. 2. The C\({}_{W}\) value is found at 2 mm away from the silo wall for each plane. 3. A C\({}_{H}\) value is calculated using C\({}_{W}\)/CRF for each plane. 4. The C\({}_{H}\) values are compared. If the simulation is calibrated correctly then the 220 mm plane should contain the largest C\({}_{H}\) value. Results of the calibration method are outlined in Table 3. The results show that the 220 mm plane results in the highest concentration when using 0.64 as the CRF value. This is promising as the ratio method seems to hold true for identifying high risk areas within the silo. Although progress has been made in developing a method for comparing simulation results with experiments, there is still a requirement to develop further the search algorithm for fine particles in the CFD domain. This improved search algorithm can then be applied to provide more reliable results for validations of CFD simulations and for scalability efforts to larger sized silos. Additional tests with different materials will provide more confidence in the calibration of the simulation approach and the validation of the simulation model based the newly developed CRF value. The other challenge faced is undertaking a simulation that has sufficient particles in the simulation domain. In this research, the silo has been simulated at a reduced flow rate. The effect of this can be seen in the reduced concentration ratio, as it is around 20% smaller than the experimental value. The particle mass concentrations are extracted from the simulation in terms of mass per unit volume. This means that there is a cubic relationship that may be influencing the CRF value. Future research could explore the following points:  
* Measure the particle mass concentration close to the silo wall (2 mm) in the experiments and apply the CRF value to this concentration. * Running experiments at a reduced scale to identify how the ratio scales. * Extend the simulation duration to improve the average results i.e., a more representative sample due to a larger data set. This research presented a methodology to use improved optical measurement techniques for dust assessments in material handling and storage equipment. The proposed dimensionless ratio (CRF value) can be applied to validate also CFD simulation models. The CRF can also be used by design engineers for evaluating dust explosion risks and dust mitigation strategies for new-build industrial storage silos by identifying high dust concentration areas in industrial storage facilities. Scientific contribution on simulation, conducted the research and developed the simulation model, main section of the paper was written by author (L.S.). Supervision of research topic and researchers and contributed to the paper by writing large sections of it (S.Z.). Scientific contribution on experimental investigation, conducted the research and developed the optical method, experimental section of the paper was written by author (L.L.L.W.).