Peravali, Surya Kiran

Single-particle diffractive imaging (SPI) is a powerful technique used in structural biology and nanoscience to determine the three-dimensional structure of individual nanoparticles, biomolecules, and viruses without the need for crystallization. By exposing freely flowing particles to ultrafast X-ray free-electron laser (XFEL) pulses, SPI captures diffraction patterns that can be reconstructed into high-resolution images. Efficient and accurate modeling and simulation of nanoparticle injection systems are essential for designing and optimizing injectors that deliver high-density, well-collimated particle streams – an important requirement for maximizing hit rates and image quality in SPI experiments. This thesis addresses these challenges by developing and optimizing multiscale simulation methodologies for nanoparticle injection devices, with a particular focus on aerodynamic lens systems (ALS) and its combination with cryogenically cooled buffer-gas cells (BGC). A hybrid molecular-continuum simulation framework, integrating classic Computational Fluid Dynamics (CFD) based on the continuum assumption and the Direct Simulation Monte Carlo (DSMC) method based on the kinetic theory of gases, is employed to accurately capture the carrier gas flow and nanoparticle trajectories across diverse flow regimes. The approach improves the computational efficiency by selectively applying DSMC in regions where molecular-scale effects dominate, while using CFD for low Knudsen number regions. Comprehensive evaluations of drag force models from the literature including molecular drag formulations are conducted, along with the introduction of a relaxation-based correction for highly rarefied, low-speed flows, to enhance particle trajectory predictions, particularly in transitional and rarefied regimes. The framework’s scalability and computational performance are assessed through detailed benchmarking, while sensitivity analyses on DSMC parameters such as particle number, grid size, and time step size further guide efficient model implementation. Key benchmark cases, including gas dynamic nozzles and re-entry vehicles, demonstrate the framework’s versatility in simulating internal and external flows. The ALS configuration highlights the framework’s applicability to injector modeling, where the hybrid DSMC/CFD approach combined with improved drag models achieve excellent agreement with experimental data, outperforming conventional CFD. Further validation against measured beam widths and focus positions is carried out for BGC and combined BGC-ALS setups across different particle sizes and inlet pressures. This validated setup is then used to assess the injector performance, with emphasis on proteinsized nanoparticles, enabling an insightful evaluation of the focusing efficiency and beam quality under realistic SPI conditions. Notably, the BGC-ALS configuration, through cryogenic cooling, enhances the focusing of smaller particles by reducing thermal velocities and suppressing Brownian motion, thereby improving the beam collimation – ideal for SPI experiments. By bridging gaps in current methodologies, validating simulation results against experimental data, and advancing drag force modeling techniques, this thesis establishes a robust foundation for optimizing SPI injector systems and paving the way for future innovations in nanoparticle injection technologies.

Aerosol injectors applied in single-particle diffractive imaging experiments demonstrated their potential in efficiently delivering nanoparticles with high density. Continuous optimization of injector design is crucial for achieving high-density particle streams, minimizing background gas, enhancing x-ray interactions, and generating high-quality diffraction patterns. We present an updated simulation framework designed for the fast and effective exploration of the experimental parameter space to enhance the optimization process. The framework includes both the simulation of the carrier gas and the particle trajectories within injectors and their expansion into the experimental vacuum chamber. A hybrid molecular-continuum-simulation method [direct simulation Monte Carlo (DSMC)/computational fluid dynamics (CFD)] is utilized to accurately capture the multi-scale nature of the flow. The simulation setup, initial benchmark results of the coupled approach, and the validation of the entire methodology against experimental data are presented. The results of the enhanced methodology show a significant improvement in the prediction quality compared to previous approaches.

The Direct Simulation Monte Carlo (DSMC) method was widely used to simulate low density gas flows with large Knudsen numbers. However, DSMC encounters limitations in the regime of lower Knudsen numbers (Kn<0.05). In such cases, approaches from classical computational fluid dynamics (CFD) relying on the continuum assumption are preferred, offering accurate solutions at acceptable computational costs. In experiments aimed at imaging aerosolized nanoparticles in vacuo a wide range of Knudsen numbers occur, which motivated the present study on the analysis of the advantages and drawbacks of DSMC and CFD simulations of rarefied flows in terms of accuracy and computational effort. Furthermore, the potential of hybrid methods is evaluated. For this purpose, DSMC and CFD simulations of the flow inside a convergent–divergent nozzle (internal expanding flow) and the flow around a conical body (external shock generating flow) were carried out. CFD simulations utilize the software OpenFOAM and the DSMC solution is obtained using the software SPARTA. The results of these simulation techniques are evaluated by comparing them with experimental data (1), evaluating the time-to-solution (2) and the energy consumption (3), and assessing the feasibility of hybrid CFD-DSMC approaches (4).