## 6 Benefits, Challenges, and Limitations of CFD in the Food Industry
### Promoting Interdisciplinary Cooperation for Advancing CFD Applications in the Food Industry  
The application of CFD in the food industry has gained significant traction, offering insights into complex thermal and fluid dynamics that enhance process optimization and product quality. However, the successful deployment of CFD technologies in this domain often hinges on robust interdisciplinary collaboration. Effective interdisciplinary cooperation begins with creating platforms where professionals from diverse fields can interact. These platforms, which can take the form of joint workshops, seminars, and conferences focused on the integration of CFD in food technology, provide opportunities for sharing advancements in CFD tools and their applicability to food processes, identifying gaps in knowledge and challenges in specific applications, and building networks among researchers, technologists, and industry practitioners. To bridge the knowledge gap between disciplines, it is crucial to offer cross-disciplinary education and training programs. Workshops and certification courses can train food technologists in the basics of CFD and familiarize CFD experts with the unique challenges of food materials and processes. Universities can introduce specialized courses or modules combining CFD fundamentalswith food science and engineering applications. Additionally, online learning platforms can provide flexibility for professionals to acquire interdisciplinary skills [157]. Interdisciplinary research and development (R&D) projects can drive innovation by pooling expertise from different domains. Joint research grants, funded by agencies prioritizing collaboration between CFD researchers and food engineers, can play a significant role. Industry-academic partnerships, where industries provide practical problems and the academia develops CFD-driven solutions, can be equally impactful. Moreover, providing access to shared computational resources, such as high-performance computing facilities, can support joint simulations and data analysis [158]. One of the barriers to the widespread application of CFD in food technology is the lack of standardized models tailored to food processes. Collaborative efforts can address this by developing benchmark datasets and simulation protocols for common food processing scenarios, creating open-source CFD models validated for food-specific challenges, such as non-Newtonian fluid behaviors and complex heat transfer mechanisms, and forming working groups within international organizations to establish the best practices [159]. Integrating CFD with Artificial Intelligence (AI) and Machine Learning (ML) can significantly enhance its applicability by reducing computation time and improving prediction accuracy. Collaborative efforts in this area could focus on developing AI-driven tools for automated mesh generation and parameter optimization, utilizing ML algorithms to analyze large datasets generated from CFD simulations and experimental results, and building interdisciplinary teams to design hybrid AI-CFD models tailored to food processes [136,153]. Demonstrating the tangible benefits of interdisciplinary collaboration through real-world case studies can inspire the further adoption of CFD in the food industry. Pilot projects implementing CFD-based process optimization in food manufacturing facilities can showcase improvements in efficiency and product quality. Publishing detailed case studies highlighting the collaboration between CFD specialists and food engineers, and organizing interactive demonstrations such as live simulations or virtual reality-based walkthroughs of CFD models applied to food processes, can also play a crucial role [9]. Policymakers and industry leaders have a critical role in fostering interdisciplinary collaboration.