Botros Hanna, PhD, PE, is a Modeling & Simulation Nuclear Engineer at Idaho National Laboratory with more than 10 years of experience in advanced reactor modeling, computational fluid dynamics, nuclear reactor techno-economics, AI/machine learning, and scientific software development. His work focuses on connecting reactor design, performance, cost, and deployment decisions through physics-informed modeling and optimization tools.

At INL, Dr. Hanna created MOUSE — Microreactor Optimization Using Simulation and Economics — an open-source Python framework and web application that integrates OpenMC Monte Carlo neutronics, simplified balance-of-plant calculations, bottom-up cost modeling, and uncertainty analysis. Through MOUSE, he has modeled liquid-metal, gas-cooled, and heat-pipe microreactor concepts to evaluate how design choices affect neutronics, fuel cycle performance, thermal-hydraulic sizing, transportability, capital cost, levelized cost of electricity and heat, and market competitiveness. His work includes granular first-of-a-kind and nth-of-a-kind cost models spanning more than 100 cost accounts, as well as fast web-based scoping tools that provide second-scale estimates for cost breakdowns, transportability checks, tax-credit scenarios, and market benchmarks.

Dr. Hanna also contributes to national efforts to reduce advanced nuclear construction cost and risk. He has developed probabilistic capital cost and construction-schedule risk models using Monte Carlo simulation, as well as quantitative FOAK-to-NOAK cost-reduction frameworks that link stakeholder-controllable levers—such as standardization, modular construction, contractor proficiency, and order-book depth—to capital cost and schedule outcomes. This work received national visibility through White House recognition in 2024.

His research further includes nuclear-powered data center techno-economics, where he led development of reactor sizing and optimization frameworks that integrate power demand, service availability, electricity pricing, financing assumptions, and plant–data center coupling configurations to identify optimal reactor sizes and deployment strategies. He has also contributed to case studies comparing renewable, mixed renewable-nuclear, and fully nuclear configurations.

In artificial intelligence and decision support, Dr. Hanna has led development of a Python-based, LLM-assisted expert reviewer matching system that integrates large SQL databases of research proposals, CVs, and publications to reduce review time from hours to minutes. His verification, validation, and uncertainty quantification work includes extending representativity methods in neutronics beyond static applications to transient reactor physics, enabling time-dependent uncertainty quantification for fast transient scenarios.

Before joining INL full time, Dr. Hanna was a postdoctoral researcher in the Computer Science Department at New Mexico State University, where he developed automated operator-support systems for nuclear power plants using logic programming and AI-guided reasoning. He also previously interned at INL, developing full-scale 3D CFD models of a BWR Mark I suppression pool torus to evaluate containment venting effectiveness during station blackout scenarios.

Dr. Hanna earned his PhD and MS in Nuclear Engineering from North Carolina State University, where his doctoral research focused on machine-learning-based error prediction for coarse-grid CFD simulations. He also holds a BS in Nuclear Engineering from Alexandria University in Egypt. He has been recognized as co-PI on more than $3 million in INL Laboratory Directed Research and Development awards, including projects on pre-cast composite walls for accelerated nuclear construction and advanced manufacturing for fission battery mass production.

• Advanced nuclear reactor techno-economics
• Computational fluid dynamics and reduced-order CFD modeling
• Microreactor design optimization and deployment analysis
• Nuclear construction cost, risk, and FOAK-to-NOAK cost reduction
• Nuclear-powered data centers and integrated energy systems
• Verification, validation, and uncertainty quantification
• Scientific software development and open-source modeling tools
• AI, machine learning, and LLM-enabled workflow automation