Decomposition of Flow Structures in Stirred Reactors and Implications for Mixing Enhancement

Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics

Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental haemodynamic models. In this work, we focus on the use of Reduced Order Models (ROMs) to reconstruct velocity fields in a patient-specific dissected aorta, with the objective being to compare the ROMs obtained from Robust Proper Orthogonal Decomposition (RPOD) to those obtained from the traditional Proper Orthogonal Decomposition (POD). POD and RPOD are applied to in vitro, haemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions. In this work, PIV and CFD results act as surrogates for clinical haemodynamic data e.g. MR, helping to demonstrate the potential use of ROMS in real clinical scenarios. The flow is reconstructed using different numbers of POD modes and the flow features obtained throughout the cardiac cycle are compared to the original Full Order Models (FOMs). Robust Principal Component Analysis (RPCA), the first step of RPOD, has been found to enhance the quality of PIV data, allowing POD to capture most of the kinetic energy of the flow in just two modes similar to the numerical data that are free from measurement noise. The reconstruction errors differ along the cardiac cycle with diastolic flows requiring more modes for accurate reconstruction. In general, modes 1-10 are found sufficient to represent the flow field. The results demonstrate that the coherent structures that characterise this aortic dissection flow are described by the first few POD modes suggesting that it is possible to represent the macroscale behaviour of aortic flow in a low-dimensional space; thus significantly simplifying the problem, and allowing for more computationally efficient flow simulations or machine learning based flow predictions that can pave the way for translation of such models to the clinic.

Advances in human mesenchymal stromal cell-based therapies - Towards an integrated biological and engineering approach

Recent advances of stem cell-based therapies in clinical trials have raised the need for large-scale manufacturing platforms that can supply clinically relevant doses to meet an increasing demand. Promising results have been reported using stirred-tank bioreactors, where human Mesenchymal Stromal Cells (hMSCs) were cultured in suspension on microcarriers (MCs), although the formation of microcarrier-cell-aggregates might still limit mass transfer and determine a heterogeneous distribution of hMSCs. A variety of MCs, bioreactor-impeller configurations, and agitation conditions have been established in an attempt to overcome the trade-off of ensuring good suspension while keeping the stresses to a minimum. While understanding and controlling the fluid flow environment of bioreactors has been initially under-appreciated, it has recently gained in popularity in the mission of providing ideal culture environments across different scales. This review article aims to provide a comprehensive overview of how rigorous engineering characterisation studies improved the outcome of biological process development and scale-up efforts. Reconciling these two disciplines is crucial to propose tailored bioprocessing solutions that can provide improved growth environments across a range of scales for the allogeneic cell therapies of the future.