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Simulation of a Rocket Engine Injection under Flashing Conditions

The recent rapid development of international space transportation demands novel, lightweight rocket engines with restart capabilities, while fulfilling current environmental regulations. The combination of combustion of cryogenic propellants with laser ignition is a promising solution. However, prior to ignition the cryogenic liquids are injected into conditions far below their saturation pressure leading to immediate boiling, so called flash evaporation. The flow field and processes at the injector exit and face plate are not yet fully understood and further research is required.

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Towards the Application of Machine Learning in Sooting Flames

Reliable simulations pertaining to soot remain a considerable challenge. One of the primary reasons for this challenge is the intricate nature of fuel/soot mechanisms, which can consist of hundreds of species. Direct integration (DI) of the extensive and stiff ordinary differential equations (ODEs) accounts for over 90% of the total CPU hours, thereby limiting the feasibility of high-fidelity soot simulations. Artificial neural networks (ANNs) are a powerful tool for emulating non-linear systems. They are universal function approximators and have both the advantages of high mapping complexity and high computational speed. Therefore, utilizing ANN techniques for modeling thermodynamic evolution in sooting flames holds great promise.

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Simulation of Bioreactors

Bioreactors are an important part of many industries. They are used in medical applications for the production of vaccine components, in the food industry—like beer brewing—the production of biogas for providing sustainable energy or the treatment of sewage and waste recycling. They work by having microorganisms or enzymes submerged in a liquid, which is continually provided with oxygen, nutrients and reactants. To better understand the processes inside bioreactors and to optimize current reactor designs, simulations of the conversion inside the reactors can be performed. However, the complex physical processes and the different time scales governing industrial-scale reactors make detailed simulations with current computing power difficult.

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Parameter study of a freely-propagating turbulent premixed flame

Minimizing production of pollutants such as NOx and soot is one of the main objectives in modern combustion research, and one of the ways to achieve the pollution reduction is by lean premixed combustion. Numerical models that accurately capture all aspects of such combustion processes including non-linear interactions of chemistry with the turbulence often help to design advanced combustion devices.

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Statistical modelling and simulation of nanoparticle agglomeration

Agglomeration of small particles is an important growth mechanism in many industrial and natural processes. It plays an essential role during the formation of soot and the production of flame-made commodities like fumed silica or titania which are used, for example, as agents in the food-processing and pharmaceutical industry. As the shape and size of the forming agglomerates determine the product characteristics, it is desirable to develop suitable models that help predict and control the growth dynamics in these processes.

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Improved Closures for Spray Combustion based on Deep-Learning Strategies

As an energy conversion approach, spray combustion is widely used in industrial furnaces and transportation systems. Increasingly stringent restriction on pollutant emissions has been demanding a deeper understanding of the combustion process. Computer simulation of spray combustion can provide insights into the dynamics of the combustion process and help to predict the performance of burners. However, accurate simulation of spray combustion remains a very challenging task because spray combustion is a highly complex process involving turbulence, atomisation, droplet evaporation and chemical reactions.

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Nanoparticle agglomeration in polydisperse systems

Agglomeration is widely applicable across a variety of industrial fields, such as pharmaceuticals, materials science, and biotechnology, playing a significant role in enhancing both operational efficiency and product quality. However, most research has primarily concentrated on agglomerating uniform-sized primary particles, with limited attention given to polydisperse primary particle size distributions. It is essential to develop models for understanding the growth dynamics of agglomerates composed of polydisperse primary particle size distributions, as this can ultimately contribute to the enhancement of the final product’s quality.

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Modelling of differential diffusion with sparse particle methods in detailed H2-O2 reactions

The pursuit of clean energy sources requires the search for novel methods for generating, storing and transporting these. Among them, hydrogen has emerged as a solution capable of addressing society’s growing demands and the call for more environmentally friendly substitutes. However, accurately and efficiently simulating hydrogen combustion, as well as appropriately modeling its underlying physical phenomena, remains a significant challenge.

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Dieses Bild zeigt Jonas Kirchmann

Jonas Kirchmann


Wissenschaftlicher Mitarbeiter

Dieses Bild zeigt Thorsten Zirwes

Thorsten Zirwes


Stellvertretende Institutsleitung

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