The paper introduces MOOSE: Multiphysics Object Oriented Simulation Environment, a parallel computational framework designed to solve coupled systems of nonlinear partial differential equations, particularly relevant in nuclear process simulations. Unlike traditional data-flow oriented frameworks, MOOSE is based on Jacobian-free Newton-Krylov (JFNK) methods, which allow for rapid development of new simulation tools by modularizing physics into "Kernels." The framework supports fully coupled and fully implicit solutions, utilizing physics-based preconditioning to handle large time scale variations. Key features include: 1. **Jacobian-free Newton Krylov (JFNK)**: This method avoids forming the full Jacobian matrix, reducing computational costs and enabling efficient solution of large linear systems. 2. **Modular Architecture**: MOOSE's layered structure allows for easy addition and coupling of new physics modules (Kernels) and boundary conditions. 3. **Physics-Based Preconditioning**: Effective preconditioning is achieved through physics-based methods, enhancing convergence rates. 4. **Advanced Capabilities**: The framework supports dimensionless physics, error estimation, and adaptivity, enabling high-fidelity, efficient, and predictive engineering simulations. Several applications, such as BISON for reactor fuel performance modeling and PRONGHORN for pebble bed reactor simulations, have been developed using MOOSE, demonstrating its versatility and performance in complex nuclear simulations. The paper concludes by highlighting the benefits of MOOSE in advancing multiphysics computation and providing a comprehensive, modern framework for rapid prototyping and production-ready codes.The paper introduces MOOSE: Multiphysics Object Oriented Simulation Environment, a parallel computational framework designed to solve coupled systems of nonlinear partial differential equations, particularly relevant in nuclear process simulations. Unlike traditional data-flow oriented frameworks, MOOSE is based on Jacobian-free Newton-Krylov (JFNK) methods, which allow for rapid development of new simulation tools by modularizing physics into "Kernels." The framework supports fully coupled and fully implicit solutions, utilizing physics-based preconditioning to handle large time scale variations. Key features include: 1. **Jacobian-free Newton Krylov (JFNK)**: This method avoids forming the full Jacobian matrix, reducing computational costs and enabling efficient solution of large linear systems. 2. **Modular Architecture**: MOOSE's layered structure allows for easy addition and coupling of new physics modules (Kernels) and boundary conditions. 3. **Physics-Based Preconditioning**: Effective preconditioning is achieved through physics-based methods, enhancing convergence rates. 4. **Advanced Capabilities**: The framework supports dimensionless physics, error estimation, and adaptivity, enabling high-fidelity, efficient, and predictive engineering simulations. Several applications, such as BISON for reactor fuel performance modeling and PRONGHORN for pebble bed reactor simulations, have been developed using MOOSE, demonstrating its versatility and performance in complex nuclear simulations. The paper concludes by highlighting the benefits of MOOSE in advancing multiphysics computation and providing a comprehensive, modern framework for rapid prototyping and production-ready codes.