MEchanics GAthering –MEGA- Seminar: Towards reduced-order models for metal-fueled chemical reactors in a carbonless circular energy economy / A probabilistic framework for describing reactive multiphase flows
Event details
| Date | 04.06.2026 |
| Hour | 12:40 › 13:40 |
| Speaker | Lisanne Gossel & Davide Mappelli (Dep of MEchanical Engineering, TU Darmstadt) |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract Lisanne: In search for an energy storage material, which enables future renewable energy supply on a global
scale, metal powders recently gain growing interest in energy science and engineering as a recyclable chemical energy carrier. While being by far not as present in the scientific and public discourse as other alternatives like hydrogen and its derivatives, metal powders are a promising alternative due to several advantageous properties. Iron is one of the mostly discussed candidates to be used as an energy carrier, since it has a high volumetric energy density, is cheap and abundant and already established as a trading good.
Metal powder combustion and reduction in chemical reactors is at the core of the novel technology. The talk will focus on a physics-based reduced-order model for describing chemical reactors, namely Chemical Reactor Networks (CRNs). CRNs are compartment-based reactor models employing strongly simplifying assumptions on the flow physics, while enabling fast and resource-efficient reactor simulations including detailed reaction mechanisms with thousands of species and reactions. We will focus on two research aspects relevant for the development of the metal fuel technology.
First, we will discuss recent achievements in the development of algorithms for automatically deriving CRN models from primary data obtained from Computational Fluid Dynamics (CFD) simulations. Algorithmic CRN construction does not only help in tapping the potential of time efficiency associated with CRNs, but is essentially a way of finding valid and robust CRN models. We will discuss a novel modeling approach, which puts a special emphasis on properly capturing the flow physics and mixing within the reactor, and yields CRN models that perform very good with respect to model consistency and robustness.
A second important aspect of CRN models in the context of metal energy carriers pertains to using CRNs in multi-query applications like process optimization and uncertainty quantification. Whereas CRNs are in principle very well-suited for these tasks due to their high efficiency, we will discuss how we can actually explore a design space using CRNs outside reference data points.
Abstract Davide: In the context of increasing efforts toward environmental sustainability, there is a growing need to improve the design and optimisation of industrial processes through predictive modelling tools. This is particularly relevant for high-temperature industrial processes, which account for a large portion of the energy consumed by the industrial sector. In the present contribution, we specifically consider industrial processes in which a granular assembly of particles is thermochemically treated by a hot gas. Examples include the calcination of limestone, the reduction of iron ore, the combustion of carbon particles and the pyrolysis of biomass. In view of industrial-scale applications, reactive granular assemblies with interstitial flows are often described using Discrete Element Methods (DEM) for the solid phase and unresolved Computational Fluid Dynamics (CFD) for the flow. A peculiarity of unresolved CFD methods is that the small-scale variability is eliminated upon the application of a filter. To close the resulting filtered equations, it is commonly assumed that the local gas composition coincides with its filtered value. However, in the presence of intricate small-scale flow structures, gas-particle mass and heat exchanges or highly non-linear chemical reactions, the composition may vary on smaller scales than the filter width, rendering the small-scale homogeneity assumption questionable. In this talk, we present a novel statistical approach, inspired by methods developed for turbulent flows, that addresses these limitations by promoting the usual thermochemical fluid descriptors to random variables. Their behaviour is modelled by the temporal evolution of a one-point probability density function (PDF) associated with the spatial distribution of the gas-phase composition inside the local filter volume. An evolution equation for the PDF is derived and reduced to the case of large-scale homogeneity. By recasting the reduced PDF equation in terms of a stochastic process, the solution is obtained by applying a Monte Carlo scheme. The model is applied to the case of a packed bed of carbon particles heated by interstitial methane combustion and the influence of the mixing limitations on predictions of global observables is quantified. These findings demonstrate that PDF methods provide a promising framework for predictive simulations of reactive granular systems.
Bio Lisanne: I obtained a Bachelor and Master’s degree in physics from Technical University of Darmstadt in Germany, and spent one semester at Grenoble INP, France during my Master’s. Being strongly interested in studying the energy transition, I then joined the cluster project Clean Circles at TU Darmstadt, where the use of iron powder as sustainable energy carrier has been studied in a strongly interdisciplinary research environment. During my doctoral studies, which I conducted with the Institute for Mathematical Modeling and Analysis at TU Darmstadt, I developed physics-based reduced-order models for iron-fueled chemical reactors. I completed my PhD in Mathematics in October 2025 before joining the Department of Mechanical Engineering at TU Darmstadt and starting a PostDoc in the Institute for Numerical Methods in Fluid Mechanics, where I am using statistical and population balance methods to study turbulent aluminum dust flames.
Bio Davide: Davide Mapelli is a PhD candidate at the Technical University of Darmstadt, within the Institute for Numerical Methods in Fluid Mechanics, under the supervision of Professor Fabian Sewerin. His research focuses on the development of probabilistic approaches for incorporating small-scale gas–particle thermochemistry into models of granular assemblies. He received his B.Sc. in Physics from the University of Milan in 2021, followed by an M.Sc. from Heidelberg University in 2024. His academic background spans probabilistic methods, stochastic dynamics, statistical physics, and fluid dynamics.
scale, metal powders recently gain growing interest in energy science and engineering as a recyclable chemical energy carrier. While being by far not as present in the scientific and public discourse as other alternatives like hydrogen and its derivatives, metal powders are a promising alternative due to several advantageous properties. Iron is one of the mostly discussed candidates to be used as an energy carrier, since it has a high volumetric energy density, is cheap and abundant and already established as a trading good.
Metal powder combustion and reduction in chemical reactors is at the core of the novel technology. The talk will focus on a physics-based reduced-order model for describing chemical reactors, namely Chemical Reactor Networks (CRNs). CRNs are compartment-based reactor models employing strongly simplifying assumptions on the flow physics, while enabling fast and resource-efficient reactor simulations including detailed reaction mechanisms with thousands of species and reactions. We will focus on two research aspects relevant for the development of the metal fuel technology.
First, we will discuss recent achievements in the development of algorithms for automatically deriving CRN models from primary data obtained from Computational Fluid Dynamics (CFD) simulations. Algorithmic CRN construction does not only help in tapping the potential of time efficiency associated with CRNs, but is essentially a way of finding valid and robust CRN models. We will discuss a novel modeling approach, which puts a special emphasis on properly capturing the flow physics and mixing within the reactor, and yields CRN models that perform very good with respect to model consistency and robustness.
A second important aspect of CRN models in the context of metal energy carriers pertains to using CRNs in multi-query applications like process optimization and uncertainty quantification. Whereas CRNs are in principle very well-suited for these tasks due to their high efficiency, we will discuss how we can actually explore a design space using CRNs outside reference data points.
Abstract Davide: In the context of increasing efforts toward environmental sustainability, there is a growing need to improve the design and optimisation of industrial processes through predictive modelling tools. This is particularly relevant for high-temperature industrial processes, which account for a large portion of the energy consumed by the industrial sector. In the present contribution, we specifically consider industrial processes in which a granular assembly of particles is thermochemically treated by a hot gas. Examples include the calcination of limestone, the reduction of iron ore, the combustion of carbon particles and the pyrolysis of biomass. In view of industrial-scale applications, reactive granular assemblies with interstitial flows are often described using Discrete Element Methods (DEM) for the solid phase and unresolved Computational Fluid Dynamics (CFD) for the flow. A peculiarity of unresolved CFD methods is that the small-scale variability is eliminated upon the application of a filter. To close the resulting filtered equations, it is commonly assumed that the local gas composition coincides with its filtered value. However, in the presence of intricate small-scale flow structures, gas-particle mass and heat exchanges or highly non-linear chemical reactions, the composition may vary on smaller scales than the filter width, rendering the small-scale homogeneity assumption questionable. In this talk, we present a novel statistical approach, inspired by methods developed for turbulent flows, that addresses these limitations by promoting the usual thermochemical fluid descriptors to random variables. Their behaviour is modelled by the temporal evolution of a one-point probability density function (PDF) associated with the spatial distribution of the gas-phase composition inside the local filter volume. An evolution equation for the PDF is derived and reduced to the case of large-scale homogeneity. By recasting the reduced PDF equation in terms of a stochastic process, the solution is obtained by applying a Monte Carlo scheme. The model is applied to the case of a packed bed of carbon particles heated by interstitial methane combustion and the influence of the mixing limitations on predictions of global observables is quantified. These findings demonstrate that PDF methods provide a promising framework for predictive simulations of reactive granular systems.
Bio Lisanne: I obtained a Bachelor and Master’s degree in physics from Technical University of Darmstadt in Germany, and spent one semester at Grenoble INP, France during my Master’s. Being strongly interested in studying the energy transition, I then joined the cluster project Clean Circles at TU Darmstadt, where the use of iron powder as sustainable energy carrier has been studied in a strongly interdisciplinary research environment. During my doctoral studies, which I conducted with the Institute for Mathematical Modeling and Analysis at TU Darmstadt, I developed physics-based reduced-order models for iron-fueled chemical reactors. I completed my PhD in Mathematics in October 2025 before joining the Department of Mechanical Engineering at TU Darmstadt and starting a PostDoc in the Institute for Numerical Methods in Fluid Mechanics, where I am using statistical and population balance methods to study turbulent aluminum dust flames.
Bio Davide: Davide Mapelli is a PhD candidate at the Technical University of Darmstadt, within the Institute for Numerical Methods in Fluid Mechanics, under the supervision of Professor Fabian Sewerin. His research focuses on the development of probabilistic approaches for incorporating small-scale gas–particle thermochemistry into models of granular assemblies. He received his B.Sc. in Physics from the University of Milan in 2021, followed by an M.Sc. from Heidelberg University in 2024. His academic background spans probabilistic methods, stochastic dynamics, statistical physics, and fluid dynamics.
Practical information
- General public
- Free
Organizer
- MEGA.Seminar Organizing Committee