Computational Engineering and Physics-Based Modelling in Food Manufacturing

The global food sector faces unprecedented pressure to deliver sustainable, nutritious, and resilient products. Historically, food formulation and processing have relied on trial-and-error methodologies. While foundational, these empirical approaches are resource-intensive, slow, and struggle to manage the highly complex, non-linear physical and chemical interactions within modern food matrices.To meet shifting consumer demands and climate targets, the industry requires a fundamental shift toward physics-grounded, computational engineering approaches. Recent advances in multi-physics simulations, mechanistic modelling, molecular dynamics, and computational fluid dynamics, augmented by machine learning (ML) and artificial intelligence (AI), have introduced a paradigm shift in food engineering by enabling predictive, physics-informed, and highly optimised process design strategies. This research topic focuses on these computational engineering workflows, bridging the gap between fundamental physical and chemical sciences and scalable food manufacturing to foster a more efficient, sustainable, safe, and precision-driven future for the food industry. In doing so, this topic supports the development of next-generation food engineering solutions aligned with global sustainability targets and Industry 4.0/5.0 transformation.The primary goal of this research topic is to establish a comprehensive multidisciplinary platform for the integration of computational engineering, spanning mechanistic modelling, multi-physics simulations, and molecular dynamics, complemented by AI and machine learning within the food processing domain, to understand, predict, and improve ingredient-processing interactions. Modern food systems are bottlenecked by traditional, empirical development cycles that fail to efficiently handle the complex, non-linear realities of novel ingredients such as plant-based proteins or clean-label alternatives. This collection aims to address this inefficiency by accelerating the transition from trial-and-error methodologies to physics-informed, data-driven food design and manufacturing.This research topic aims to establish a strong interdisciplinary convergence between computational sciences and food engineering, fostering the integration of physics-based modelling, mechanistic simulation, and molecular-level analysis with innovative food formulation and processing strategies. We look to gather breakthroughs that deploy multi-physics simulations, molecular dynamics, and digital twins, supported by machine learning algorithms, to accurately map molecular interactions, optimise industrial unit operations, predict food quality, and minimise resource wastage. By consolidating high-quality research from both academia and industry, this collection aims to provide a scalable roadmap for precision food engineering, ultimately driving the food sector toward a more agile, safe, and sustainable future.This Research Topic welcomes high-quality original research, reviews, and perspective articles at the intersection of computational engineering and food science. We are specifically interested in studies addressing complex food systems through physics-based, mechanistic, and hybrid computational modelling approaches, including AI and machine learning, to predict, design, control, and optimise complex food systems and unit operations. The scope includes, but is not limited to, these specific themes:o Predictive Design: Physics-informed and hybrid AI/machine learning models for predicting food quality, shelf life, microbiological and contamination risk assessment, and optimising novel ingredient formulations with particular focus on plant-based proteins, clean-label alternatives, and complex food matrices.o Process Simulation: Multi-scale and multi-physics modelling of transport phenomena and reaction kinetics during complex unit operations in food processing. Mechanistic, mathematical, and/or hybrid modelling of heat and mass transfer, rheology, phase change, and reaction kinetics.o Molecular Dynamics: Computational insights into structure-function relationships of food ingredients, including molecular-level interactions between novel or alternative ingredients and food matrices.o Smart Manufacturing and Industry 4.0/5.0 Transformation: Digital twins, computer vision, and Industry 4.0/5.0 for automation and process efficacy, grounded in engineering-validated simulation frameworks.o Scale-up and Sustainability: Physics-based and data-driven decision-making for energy efficiency, waste minimisation, and bench-to-industrial scaling of novel food formulations and manufacturing processes.o Verification and validation of food processes: Computational and physics-based models for the verification and validation of food processing methodologies.Our goal is to compile actionable, engineering-grounded insights that bridge fundamental physical and chemical sciences with scalable, sustainable industrial applications. Topic editors: Amit Kumar, Poonam Rani Source: https://www.frontiersin.org/research-topics/81999/computational-engineering-and-physics-based-modelling-in-food-manufacturing