Developing tools and models to predict thermal hazard conditions, develop risk mitigation plans, and assess combustion technologies
Thermal hazards
In the thermal hazard space, our group studies how explosives respond to heating, which is especially relevant for fire scenarios. Under such conditions, we need to predict time-to-explosion to inform emergency planning. We use LLNL’s multi-physics code, ALE3D, which includes thermal transport, chemical kinetics, and mechanical properties, to analyze thermal hazard scenarios and develop risk mitigation plans.
We use genetic algorithms to optimize the calibration of chemical kinetics models to experimental data, which drastically reduces fitting time and improves accuracy compared to manual model calibration. The close coupling of highly diagnosed lab-scale experiments and predictive modeling powered by supercomputers enables us to enhance our understanding hazards, develop mitigation strategies for existing explosives, and identify desired safety properties for new explosives.
Our thermal hazards work addresses the missions of key stakeholders, including the National Nuclear Security Administration and the Department of Defense, helping to ensure safe operations that involve weapons systems. Our research provides foundational knowledge that enables us to:
- Support national security partners tasked with manufacturing, assembling, and transporting weapons systems—providing the safety basis for these hazardous operations and aiding efforts to adopt novel manufacturing processes needed to accelerate weapon modernization.
- Analyze the tradeoff between performance and safety, enabling design and production of inherently safer weapon systems.
- Develop assessment tools that support efforts to enhance emergency response planning and mitigate hazards across the national security complex.
Combustion
Our group studies chemically reacting systems typically composed of a fuel and an oxidizer, such as the burning of sustainable aviation fuels (SAF) in aircraft engines. Conventional jet fuel has largely been unchanged since the 1950s, creating a gap in predictive models for the way existing and emerging SAF burn, ignite, extinguish, and form emissions.
To help accelerate the utilization of SAF, we develop chemical kinetic models for surrogate fuel representations of these highly complex mixtures. Surrogate fuels often contain only a handful of chemical compounds and can be designed using machine learning tools. Detailed chemical kinetic models for this smaller set of compounds are comprised of reaction networks, reaction rate constant parameters, thermodynamic properties, and transport properties.
Leveraging LLNL’s world class high-performance computing resources and codes like Zero-RK, we can comprehensively validate the chemical kinetic model with fundamental measurements from our collaborators. We collaborate closely with colleagues in electronic structure calculations, computational engineering and optimization, chemical and mechanical engineering, and computational fluid dynamics.
Our combustion work addresses the missions of key stakeholders, including the Department of Energy, helping to ensure energy and climate security. Our research provides the foundational knowledge that enables us to:
- Support academic, industrial, and government stakeholders in assessments of low- and non-carbon fuels for hard to electrify transportation sectors such as the off-road, rail, marine, and aviation markets.
- Inform predictive multi-scale models for hazards such as aircraft induced cloudiness, battery fires, urban and wildland fires, and nuclear incidents.
- Develop chemical kinetic models which are used to optimize combustion technologies for power generation, transportation, and industrial processes with reacting flow simulations, maximizing efficiency and minimizing harmful pollutants.
