AS THE GLOBAL POPULATION APPROACHES 10 BILLION, THE DEMAND FOR SUSTAINABLE FOOD SOURCES INTENSIFIES. THIS PROJECT AIMS TO DEVELOP A COMPUTER-AIDED ENGINEERING FRAMEWORK FOR HIGH-MOISTURE MEAT ANALOGS (HMMA) THAT OPTIMIZES COOLING PROFILES TO IMPROVE FIBROUS STRUCTURE FORMATION, ENHANCING PLANT-BASED PROTEIN PRODUCTION AND ADDRESSING CRITICAL CHALLENGES IN MATERIAL BEHAVIOR AND PROCESSING CONDITIONS.THIS PROJECT SEEKS TO ADVANCE HIGH-MOISTURE MEAT ANALOG PRODUCTION BY DEVELOPING A NOVEL FRAMEWORK THAT INTEGRATES CUTTING-EDGE COMPUTATIONAL METHODS WITH EXPERIMENTAL VALIDATION. IT FOCUSES ON THREE MAIN OBJECTIVES. FIRST, A DATA-DRIVEN APPROACH WILL BE USED TO CHARACTERIZE THE RHEOLOGICAL PROPERTIES OF MATERIALS, WHICH ARE CRITICAL IN SHAPING FLOW DYNAMICS, HEAT TRANSFER, AND THE RESULTING FIBROUS STRUCTURE. SECOND, A PHYSICS-INFORMED DIGITAL TWIN WILL SIMULATE THE STRUCTURE-FORMATION PROCESS BY IDENTIFYING KEY MATERIAL PARAMETERS AND LEVERAGING NEURAL NETWORKS TO UNCOVER GOVERNING FACTORS, ALL VALIDATED AGAINST EXPERIMENTAL DATA. FINALLY, CONTROL STRATEGIES WILL BE IMPLEMENTED TO OPTIMIZE COOLING PROFILES, CONDUCTING NUMERICAL EXPERIMENTS TO LINK COOLING CONDITIONS WITH STRUCTURAL OUTCOMES AND REFINING THESE DESIGNS THROUGH EXPERIMENTAL VALIDATION.OVERALL, THIS RESEARCH AIMS TO REVOLUTIONIZE HMMA PRODUCTION BY DEEPENING THE UNDERSTANDING OF FIBROUS STRUCTURE FORMATION, CONTRIBUTING TO SUSTAINABLE FOOD SYSTEMS, AND ADDRESSING THE INCREASING GLOBAL DEMAND FOR PLANT-BASED PROTEINS.