@article{MTMT:31911935,
	title = {Simulation of a batch crystallizer using a multi-scale approach in time and space},
	url = {https://m2.mtmt.hu/api/publication/31911935},
	author = {de, Souza L.M. and Temmel, E. and Janiga, Gábor and Seidel-Morgenstern, A. and Thévenin, D.},
	doi = {10.1016/j.ces.2020.116344},
	journal-iso = {CHEM ENG SCI},
	journal = {CHEMICAL ENGINEERING SCIENCE},
	volume = {232},
	unique-id = {31911935},
	issn = {0009-2509},
	abstract = {The simulation of a crystallizer leads to a challenging problem with very different time and length scales. After checking a simple 0-D approach in time, and a brute-force coupling process describing the complete problem in space and time, the multi-scale methodology developed in this work combines three-dimensional Computational Fluid Dynamics simulations on a short time-scale (to describe hydrodynamic features) with zero-dimensional simulations relying on a Population Balance Model over long time-scales to compute the evolution of all important particle properties (volume fraction, particle size distribution, slip velocities, mass transfer coefficients). The flow field in the crystallizer is almost homogeneous, apart for a stagnation zone below the impeller leading to an increased solid-phase volume fraction but smaller crystals. The diffusive mass transfer coefficient evolves in a non-monotonic way. A proper prediction of dynamically changing local supersaturations requires a closer coupling between the two simulation steps in the multi-scale framework. © 2020 Elsevier Ltd},
	keywords = {Crystal growth; CRYSTALLIZATION; particle size; Particle size analysis; Mass transfer; Volume fraction; Crystallizers; Computational fluid dynamics; Multi-scale approaches; Diffusive mass transfer; CFD; Population balance modeling; BATCH CRYSTALLIZER; Three dimensional computational fluid dynamics; Slip velocity; CSD; particle properties; QMOM; Multi-scale frameworks; Solid phase volume},
	year = {2021},
	eissn = {1873-4405},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@article{MTMT:31911934,
	title = {Central moments multiple relaxation time LBM for hemodynamic simulations in intracranial aneurysms: An in-vitro validation study using PIV and PC-MRI},
	url = {https://m2.mtmt.hu/api/publication/31911934},
	author = {Hosseini, S.A. and Berg, P. and Huang, F. and Roloff, C. and Janiga, Gábor and Thévenin, D.},
	doi = {10.1016/j.compbiomed.2021.104251},
	journal-iso = {COMPUT BIOL MED},
	journal = {COMPUTERS IN BIOLOGY AND MEDICINE},
	volume = {131},
	unique-id = {31911934},
	issn = {0010-4825},
	abstract = {The lattice Boltzmann method (LBM) has recently emerged as an efficient alternative to classical Navier-Stokes solvers. This is particularly true for hemodynamics in complex geometries. However, in its most basic formulation, i.e. with the so-called single relaxation time (SRT) collision operator, it has been observed to have a limited stability domain in the Courant/Fourier space, strongly constraining the minimum time-step and grid size. The development of improved collision models such as the multiple relaxation time (MRT) operator in central moments space has tremendously widened the stability domain, while allowing to overcome a number of other well-documented artifacts, therefore opening the door for simulations over a wider range of grid and time-step sizes. The present work focuses on implementing and validating a specific collision operator, the central Hermite moments multiple relaxation time model with the full expansion of the equilibrium distribution function, to simulate blood flows in intracranial aneurysms. The study further proceeds with a validation of the numerical model through different test-cases and against experimental measurements obtained via stereoscopic particle image velocimetry (PIV) and phase-contrast magnetic resonance imaging (PC-MRI). For a patient-specific aneurysm both PIV and PC-MRI agree fairly well with the simulation. Finally, low-resolution simulations were shown to be able to capture blood flow information with sufficient accuracy, as demonstrated through both qualitative and quantitative analysis of the flow field while leading to strongly reduced computation times. For instance in the case of the patient-specific configuration, increasing the grid-size by a factor of two led to a reduction of computation time by a factor of 14 with very good similarity indices still ranging from 0.83 to 0.88. © 2021 Elsevier Ltd},
	keywords = {VALIDATION; BLOOD; Magnetic Resonance Imaging; Magnetic Resonance Imaging; Hemodynamics; Particle size analysis; Distribution functions; relaxation time; intracranial aneurysm; Computational fluid dynamics; INTRACRANIAL ANEURYSMS; Velocity measurement; particle image velocimetry; lattice Boltzmann method; Navier Stokes equations; Qualitative and quantitative analysis; Stereo image processing; Phase contrast magnetic resonance imaging; Single relaxation time; stereoscopic particle image velocimetry; Lattice boltzmann methods (LBM); Central hermite multiple relaxation time; Equilibrium distribution functions; Multiple-relaxation time; Multiple-relaxation-time models},
	year = {2021},
	eissn = {1879-0534},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@article{MTMT:31911933,
	title = {On-the-fly artificial neural network for chemical kinetics in direct numerical simulations of premixed combustion},
	url = {https://m2.mtmt.hu/api/publication/31911933},
	author = {Chi, C. and Janiga, Gábor and Thévenin, D.},
	doi = {10.1016/j.combustflame.2020.12.038},
	journal-iso = {COMBUST FLAME},
	journal = {COMBUSTION AND FLAME},
	volume = {226},
	unique-id = {31911933},
	issn = {0010-2180},
	abstract = {In this study, an on-the-fly artificial neural network (ANN) framework has been developed for the tabulation of chemical reaction terms in direct numerical simulations (DNS) of premixed and igniting flames. The procedure does not require any preliminary knowledge to generate samples for ANN training; the whole training process is based on the detailed simulation results and takes place on-the-fly, so that the obtained ANN model is perfectly adapted to the specific problem considered. The framework combines direct integration (DI) and ANN model in an efficient way to overcome the extrapolation issue of the monolithic ANN model. Auto-ignition processes as well as the characteristics of established flames can be very well predicted using the ANN model. In the final simulations, involving a case with 3D turbulent hot-spot ignition, and a flame propagating in a turbulent flow, the developed procedure reduces the computational times by a factor of almost 5, while keeping the error for all species below 1% compared to the standard, monolithic DI solution. © 2020 The Combustion Institute},
	keywords = {ARTICLE; NEURAL NETWORKS; computer simulation; FLAME; COMBUSTION; artificial neural network; artificial neural network; Turbulent flow; Numerical models; Training process; Specific problems; Direct numerical simulation; Computational time; DIRECT NUMERICAL SIMULATIONS; ignition; on-the-fly; premixed combustion; premixed combustion; Auto-ignition process; Direct integration; Flame propagating; Hot-spot ignition},
	year = {2021},
	eissn = {1556-2921},
	pages = {467-477},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@article{MTMT:31767416,
	title = {Hemodynamic Data Assimilation in a Subject-specific Circle of Willis Geometry},
	url = {https://m2.mtmt.hu/api/publication/31767416},
	author = {Gaidzik, Franziska and Pathiraja, Sahani and Saalfeld, Sylvia and Stucht, Daniel and Speck, Oliver and Thevenin, Dominique and Janiga, Gábor},
	doi = {10.1007/s00062-020-00959-2},
	journal-iso = {CLIN NEURORADIOL},
	journal = {CLINICAL NEURORADIOLOGY},
	volume = {31},
	unique-id = {31767416},
	issn = {1869-1439},
	abstract = {Purpose The anatomy of the circle of Willis (CoW), the brain's main arterial blood supply system, strongly differs between individuals, resulting in highly variable flow fields and intracranial vascularization patterns. To predict subject-specific hemodynamics with high certainty, we propose a data assimilation (DA) approach that merges fully 4D phase-contrast magnetic resonance imaging (PC-MRI) data with a numerical model in the form of computational fluid dynamics (CFD) simulations. Methods To the best of our knowledge, this study is the first to provide a transient state estimate for the three-dimensional velocity field in a subject-specific CoW geometry using DA. High-resolution velocity state estimates are obtained using the local ensemble transform Kalman filter (LETKF). Results Quantitative evaluation shows a considerable reduction (up to 90%) in the uncertainty of the velocity field state estimate after the data assimilation step. Velocity values in vessel areas that are below the resolution of the PC-MRI data (e.g., in posterior communicating arteries) are provided. Furthermore, the uncertainty of the analysis-based wall shear stress distribution is reduced by a factor of 2 for the data assimilation approach when compared to the CFD model alone. Conclusion This study demonstrates the potential of data assimilation to provide detailed information on vascular flow, and to reduce the uncertainty in such estimates by combining various sources of data in a statistically appropriate fashion.},
	keywords = {Hemodynamics; CFD; Uncertainty quantification; PC-MRI; LETKF},
	year = {2021},
	eissn = {1869-1447},
	pages = {643-651},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@{MTMT:31910937,
	title = {CFD Simulation of a Solid-Liquid Counter-Current Screw Extractor},
	url = {https://m2.mtmt.hu/api/publication/31910937},
	author = {Lehr, A. and Janiga, Gábor and Seidel-Morgenstern, A. and Thévenin, D.},
	booktitle = {Computer Aided Chemical Engineering},
	doi = {10.1016/B978-0-12-823377-1.50038-0},
	volume = {48},
	unique-id = {31910937},
	abstract = {More efficient processes to obtain artemisinin from Artemisia annua leaves via a solid-liquid extraction process are desirable, since artemisinin is increasingly needed as anti-malaria drug. As a substitute for conventional batch extraction technology, continuously operated counter-current processes are highly attractive for that purpose. To get first a better understanding of the hydrodynamics controlling the extraction, a multiphase 3D computational fluid dynamics (CFD) simulation model has been developed in the present project. It relies on the Volume of Fluid (VoF) model, leading to a purely Eulerian description of the flow. Using VoF, the distribution of the different phases within the screw extractor can be obtained. When varying the two inlet flow rates, different residence times for the liquid and the solid phases are obtained. This is particularly important, since the residence time is the most important process parameter to adjust. Currently, the predicted residence times for the liquid solvent amount to only one third of the experimentally determined values. However, accurate measurements are difficult, the assessment of residence times is different in the experiments and in the simulations, and the numerical model does not consider mass exchange processes between the phases yet. In spite of this discrepancy, a very good qualitative agreement is obtained, and these first results can be used to support the development of a compartment model, able to capture later both hydrodynamic and mass exchange processes with short computational times. © 2020 Elsevier B.V.},
	keywords = {fluid dynamics; CFD; Mass exchange; VOF; screw extractor},
	year = {2020},
	pages = {223-228},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@article{MTMT:31910936,
	title = {Numerical investigations of turbulent single-phase and two-phase flows in a diffuser},
	url = {https://m2.mtmt.hu/api/publication/31910936},
	author = {Kopparthy, S. and Mansour, M. and Janiga, Gábor and Thévenin, D.},
	doi = {10.1016/j.ijmultiphaseflow.2020.103333},
	journal-iso = {INT J MULTIPHAS FLOW},
	journal = {INTERNATIONAL JOURNAL OF MULTIPHASE FLOW},
	volume = {130},
	unique-id = {31910936},
	issn = {0301-9322},
	abstract = {This study presents numerical investigations of turbulent single-phase (water) and two-phase (air-water) flows in a horizontal diverging channel (diffuser), extending our previous experimental work (Mansour et al., 2018a). The main target is to examine and discuss the prediction accuracy of available computational fluid dynamics (CFD) models under such turbulent two-phase flow conditions with separation, based on direct comparisons with detailed experimental data. After performing a mesh-independence test, the numerical results for single-phase flows have been validated against experimental data of the axial pressure change in the channel. Four different turbulence models, including the Realizable k−ϵ, the k−ω shear stress transport (SST), the Spalart-Allmaras, and the Reynolds Stress Model (RSM) were considered and compared. The results show that the Realizable k−ϵ and RSM models can predict the pressure change in single-phase flows more accurately, while only RSM could as well predict a velocity field close to the experiments. Accordingly, only Realizable k−ϵ and RSM have been used for further two-phase flow simulations, which were performed using a transient setup together with the Volume of Fluid (VOF) method to model the interaction between the two phases. It was observed that only RSM performed reasonably well concerning flow regimes and air accumulations. Finally, considering higher flow rates, even the two-phase flow regimes predicted by RSM start to deviate from the experiments. The present study underlines the limitations of existing CFD models when applied to such complex two-phase flows. © 2020 Elsevier Ltd},
	keywords = {velocity; Forecasting; AIR; Shear stress; Reynolds number; Computational fluid dynamics; Turbulent flow; Prediction accuracy; Numerical investigations; mesh independence; Turbulence models; Turbulence models; Two phase flow; Shear-stress transport; Gas-liquid two-phase flow; Diverging channel; Phase separation and gas accumulation; Turbulent separated flow; Volume of fluid (VOF) method; Reynolds stress models; Turbulent two-phase flows; Two-phase flow regimes; Volume of fluid method},
	year = {2020},
	eissn = {1879-3533},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@article{MTMT:31910934,
	title = {Super-resolution and denoising of 4D-Flow MRI using physics-Informed deep neural nets},
	url = {https://m2.mtmt.hu/api/publication/31910934},
	author = {Fathi, M.F. and Perez-Raya, I. and Baghaie, A. and Berg, P. and Janiga, Gábor and Arzani, A. and D'Souza, R.M.},
	doi = {10.1016/j.cmpb.2020.105729},
	journal-iso = {COMPUT METH PROG BIO},
	journal = {COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE},
	volume = {197},
	unique-id = {31910934},
	issn = {0169-2607},
	abstract = {Background and Objective: Time resolved three-dimensional phase contrast magnetic resonance imaging (4D-Flow MRI) has been used to non-invasively measure blood velocities in the human vascular system. However, issues such as low spatio-temporal resolution, acquisition noise, velocity aliasing, and phase-offset artifacts have hampered its clinical application. In this research, we developed a purely data-driven method for super-resolution and denoising of 4D-Flow MRI. Methods: The flow velocities, pressure, and the MRI image magnitude are modeled as a patient-specific deep neural net (DNN). For training, 4D-Flow MRI images in the complex Cartesian space are used to impose data-fidelity. Physics of fluid flow is imposed through regularization. Creative loss function terms have been introduced to handle noise and super-resolution. The trained patient-specific DNN can be sampled to generate noise-free high-resolution flow images. The proposed method has been implemented using the TensorFlow DNN library and tested on numerical phantoms and validated in-vitro using high-resolution particle image velocitmetry (PIV) and 4D-Flow MRI experiments on transparent models subjected to pulsatile flow conditions. Results: In case of numerical phantoms, we were able to increase spatial resolution by a factor of 100 and temporal resolution by a factor of 5 compared to the simulated 4D-Flow MRI. There is an order of magnitude reduction of velocity normalized root mean square error (vNRMSE). In case of the in-vitro validation tests with PIV as reference, there is similar improvement in spatio-temporal resolution. Although the vNRMSE is reduced by 50%, the method is unable to negate a systematic bias with respect to the reference PIV that is introduced by the 4D-Flow MRI measurement. Conclusions: This work has demonstrated the feasibility of using the readily available machinery of deep learning to enhance 4D-Flow MRI using a purely data-driven method. Unlike current state-of-the-art methods, the proposed method is agnostic to geometry and boundary conditions and therefore eliminates the need for tedious tasks such as accurate image segmentation for geometry, image registration, and estimation of boundary flow conditions. Arbitrary regions of interest can be selected for processing. This work will lead to user-friendly analysis tools that will enable quantitative hemodynamic analysis of vascular diseases in a clinical setting. © 2020},
	keywords = {VALIDATION; ARTICLE; NEURAL NETWORKS; controlled study; Numerical methods; nuclear magnetic resonance imaging; Magnetic Resonance Imaging; in vitro study; velocity; Pulsatile Flow; Blood Flow Velocity; MACHINERY; cardiovascular system; anatomy; Image segmentation; Image segmentation; Flow of fluids; steady state; flow rate; Mean square error; Denoising; clinical application; intracranial aneurysm; Molecular physics; Regions of interest; Laminar flow; saccular aneurysm; Optical resolving power; spatiotemporal analysis; particle image velocimetry; Image registration; data assimilation; heart cycle; State-of-the-art methods; Fluid flow; super-resolution; PHANTOMS; Deep neural networks; deep neural network; statistical bias; Deep learning; spatio-temporal resolution; Phase contrast magnetic resonance imaging; Root mean square errors; four-dimensional imaging; 4D-flow MRI; PI-DNN; High-resolution flow images; Human vascular systems},
	year = {2020},
	eissn = {1872-7565},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@inproceedings{MTMT:31911957,
	title = {Improved flow prediction in intracranial aneurysms using data assimilation},
	url = {https://m2.mtmt.hu/api/publication/31911957},
	author = {Schulz, F. and Roloff, C. and Stucht, D. and Thevenin, D. and Speck, O. and Janiga, Gábor},
	booktitle = {3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019},
	doi = {10.7712/120219.6365.18884},
	unique-id = {31911957},
	abstract = {Rupture of intracranial aneurysms often leads to irreversible disabilities or even death. The investigation of hemodynamics increases the understanding of cardiovascular diseases, this gain of knowledge can support physicians in outcome prediction and therapy planning. Hemodynamic simulations are restricted by modeling assumptions and uncertain initial conditions, whereas PC-MRI data is affected by measurement noise and artifacts. To overcome the limitations of both techniques, the current study uses a Localization Ensemble Transform Kalman Filter (LETKF) to incorporate uncertain Phase-Contrast MRI data into an ensemble of numerical simulations. The analysis output provides an improved state estimate of the threedimensional blood flow field. Benchmark measurements are carried out in a silicone phantom model of an idealized aneurysm under user-specific inflow conditions. Validation is ensured with high-resolution Particle Imaging Velocimetry (PIV) obtained from a vertical slice in the center of the same geometry. Results show that even velocity peaks smaller than the PC-MRI resolution can be reconstructed using the employed approach. The root mean square error (RMSE) of the analysis state estimate is reduced by 27 % to 89 % in comparison to interpolation of the PC-MRI data onto the PIV grid resolution. © 2019 Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. All rights reserved.},
	keywords = {Hemodynamics; Hemodynamics; Silicones; uncertainty analysis; state estimation; disease control; Mean square error; intracranial aneurysm; Computational fluid dynamics; INTRACRANIAL ANEURYSMS; data assimilation; data assimilation; CFD; Particle imaging velocimetry; outcome prediction; Cardio-vascular disease; Root mean square errors; PC-MRI; Benchmark measurements; Uncertain initial condition},
	year = {2019},
	pages = {629-639},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@CONFERENCE{MTMT:31911955,
	title = {Optimization of a winglet for improving the performance of an H-Darrieus turbine using CFD},
	url = {https://m2.mtmt.hu/api/publication/31911955},
	author = {Daróczy, L. and Janiga, Gábor and Thévenin, D.},
	booktitle = {16th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, ISROMAC 2016},
	unique-id = {31911955},
	abstract = {The importance of wind energy has progressed rapidly in the last years. Although Horizontal Axis Wind Turbines (HAWT) are most well-spread, there is an increasing interest in Vertical Axis Wind Turbines (VAWT), especially in the H-Darrieus concept, as these rotors are omni-directional and affordable. However, the physics of these rotors is more complex; they can only be analyzed using transient CFD simulations. Due to the finite aspect ratio of the rotors, a wingtip vortex is created, which generates losses. Optimizing the wingtip geometry could be advantageous for increasing the efficiency of the rotors: this can only be achieved with three-dimensional turbulent transient simulations. For the optimization of winglets, the whole process (mesh generation, CFD computation, post-processing) has to be automated. This is achieved using the OPtimization Algorithm Library++ (OPAL++), a custom C++ code for the description of blended and canted winglets, coupled with a CD-Adapco StarCCM+ Java script for the automatization of the mesh generation and CFD computations. To check the viability of the present concept, two parameters have been varied in the simulations. As shown in what follows, an efficient automatic optimization of wind turbine wingtips can be implemented in this manner. © Open Archives of the 16th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, ISROMAC 2016. All rights reserved.},
	keywords = {Optimization; Optimization; Wind energy; Wind turbines; Wind power; Mesh generation; Transport properties; Aspect ratio; Computational fluid dynamics; Optimization algorithms; Horizontal wells; Rotating machinery; CFD; C++ (programming language); Vertical axis wind turbines; Horizontal axis wind turbines; Darrieus turbine; Automatic optimization; transient simulation; Winglet; Winglet; Finite aspect ratio},
	year = {2019},
	pages = {149383},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640}
}

@article{MTMT:30675736,
	title = {Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)—phase II: rupture risk assessment},
	url = {https://m2.mtmt.hu/api/publication/30675736},
	author = {Berg, Philipp and Voß, Samuel and Janiga, Gábor and Saalfeld, Sylvia and Bergersen, Aslak W. and Valen-Sendstad, Kristian and Bruening, Jan and Goubergrits, Leonid and Spuler, Andreas and Chiu, Tin Lok and Tsang, Anderson Chun On and Copelli, Gabriele and Csippa, Benjamin and Paál, György and Závodszky, Gábor and Detmer, Felicitas J. and Chung, Bong J. and Cebral, Juan R. and Fujimura, Soichiro and Takao, Hiroyuki and Karmonik, Christof and Elias, Saba and Cancelliere, Nicole M. and Najafi, Mehdi and Steinman, David A. and Pereira, Vitor M. and Piskin, Senol and Finol, Ender A. and Pravdivtseva, Mariya and Velvaluri, Prasanth and Rajabzadeh-Oghaz, Hamidreza and Paliwal, Nikhil and Meng, Hui and Seshadhri, Santhosh and Venguru, Sreenivas and Shojima, Masaaki and Sindeev, Sergey and Frolov, Sergey and Qian, Yi and Wu, Yu-An and Carlson, Kent D. and Kallmes, David F. and Dragomir-Daescu, Dan and Beuing, Oliver},
	doi = {10.1007/s11548-019-01986-2},
	journal-iso = {INT J COMPUT ASSIST RADIOL SURG},
	journal = {INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY},
	volume = {14},
	unique-id = {30675736},
	issn = {1861-6410},
	abstract = {Assessing the rupture probability of intracranial aneurysms (IAs) remains challenging. Therefore, hemodynamic simulations are increasingly applied toward supporting physicians during treatment planning. However, due to several assumptions, the clinical acceptance of these methods remains limited.},
	year = {2019},
	eissn = {1861-6429},
	pages = {1795-1804},
	orcid-numbers = {Janiga, Gábor/0000-0002-4560-9640; Csippa, Benjamin/0000-0002-6072-9712; Paál, György/0000-0003-1426-2215; Závodszky, Gábor/0000-0003-0150-0229}
}