## 8 Current Challenges and Future Perspectives  
### Model Validation and Scalability  
Despite the growing use of CFD in food process engineering, key challenges remain in model validation, generalization, and scalability. Accurate simulations rely not only on the strength of numerical solvers but also on reliable input data, realistic boundary conditions, and robust validation methods across different scales and equipment types. Bridging the gap between laboratory-scale modeling and industrial-scale implementation remains a crucial objective for both researchers and practitioners [57]. Model validation in food systems is inherently difficult due to the complexity of the materials and the limited accessibility of internal process variables. Many food matrices are opaque, sticky, or thermally sensitive, making direct measurement of velocity fields, temperature gradients, or concentrations infeasible. Moreover, thermophysical and rheological properties--such as viscosity, diffusivity, or thermal conductivity--are often poorly characterized and highly variable. When multiphysics interactions such as reaction kinetics or phase transitions are included, the number of uncertain parameters increases further. Practical boundary and initial conditions also tend to be non-uniform, with heterogeneities in moisture content, particle size, or composition that deviate from idealized simulation assumptions [74]. Due to these constraints, model validation often relies on indirect or surrogate metrics. Common approaches include comparing simulation outputs with product temperature profiles, RTDs, drying curves, or quality attributes such as porosity, expansion ratio, or texture. However, spatially resolved, quantitative validation--particularly for velocity or shear fields--is typically limited to laboratory or pilot-scale systems with specialized instrumentation [107]. Scalability poses another challenge. As models are scaled from lab to industrial conditions, geometric complexity increases, flow regimes shift (e.g., from laminar to turbulent), and process dynamics become more nonlinear. In large-scale equipment, control systems, mechanical wear, and operator interventions further complicate predictions. High-resolution 3D simulations of industrial systems are often computationally expensive, limiting their use for design iterations or real-time control. Moreover, scaling laws based on dimensionless numbers such as Reynolds or Nusselt may not apply consistently in non-Newtonian or multiphase systems [73]. To improve both validation and scalability, several strategies are being pursued. Hybrid modeling approaches that combine CFD with experimental calibration, empirical correlations, or machine learning can reduce reliance on fully resolved physics. Modular validation, in which subsystems (e.g., mixing, heating, drying) are tested independently before being integrated, helps isolate uncertainties. SA and Bayesian inference techniques can be used to quantify parameter uncertainty and evaluate model robustness under varying conditions. DoE provides a systematic framework for validation across a broad operational space, while non-invasive imaging techniques such as MRI, PIV (Particle Image Velocimetry), or X-ray CT allow for more detailed measurements in research settings [158]. Emerging digital twin platforms offer a promising solution for industrial-scale validation, enabling CFD models to be continuously updated using real-time process data. Looking ahead, further improvements are expected through the development of standardized property databases, automated meshing tools, and cloud-based GPU-accelerated simulation environments. Advances in physics-informed machine learning (PIML) could also support real-time model correction and adaptive control [120]. Ultimately, overcoming the current limitations in model validation and scalability will be essential for mainstream adoption of CFD in food engineering.