An experimental and CFD study of liquid jet injection into a partially baffled mixing vessel: a contribution to process safety by improving the quenching of runaway reactions

Thermal runaway remains a problem in the process industries with poor or inadequate mixing contributing significantly to these incidents. An efficient way to quench such an uncontrolled chemical reaction is via the injection of a liquid jet containing a small quantity of a very active inhibiting agent (often called a stopper) that must be mixed into the bulk of the fluid to quench the reaction. The hazards associated with such runaway events mean that a validated computational fluid dynamics (CFD) model would be an extremely useful tool. In this paper, the injection of a jet at the flat free surface of a partially baffled agitated vessel has been studied both experimentally and numerically. The dependence of the jet trajectory on the injection parameters has been simulated using a single-phase flow CFD model together with Lagrangian particle tracking. The comparison of the numerical predictions with experimental data for the jet trajectories shows very good agreement. The analysis of the transport of a passive scalar carried by the fluid jet and thus into the bulk, together with the use of a new global mixing criterion adapted for safety issues, revealed the optimum injection conditions to maximise the mixing benefits of the bulk flow pattern

Investigation of bone resorption within a cortical basic multicellular unit using a lattice-based computational model

In this paper we develop a lattice-based computational model focused on bone resorption by osteoclasts in a single cortical basic multicellular unit (BMU). Our model takes into account the interaction of osteoclasts with the bone matrix, the interaction of osteoclasts with each other, the generation of osteoclasts from a growing blood vessel, and the renewal of osteoclast nuclei by cell fusion. All these features are shown to strongly influence the geometrical properties of the developing resorption cavity including its size, shape and progression rate, and are also shown to influence the distribution, resorption pattern and trajectories of individual osteoclasts within the BMU. We demonstrate that for certain parameter combinations, resorption cavity shapes can be recovered from the computational model that closely resemble resorption cavity shapes observed from microCT imaging of human cortical bone.Comment: 17 pages, 11 figures, 1 table. Revised version: paper entirely rewritten for a more biology-oriented readership. Technical points of model description now in Appendix. Addition of two new figures (Fig. 5 and Fig. 9) and removal of former Fig.

Jet injection studies for partially baffled mixing reactors: a general correlation for the jet trajectory and jet penetration depth

This paper is devoted to the analysis of the jet trajectories, obtained using computational fluid dynamics (CFD), at two different scales (laboratory and industrial) with application to quenching of runaway reactions. One of the goals was to describe how the jet penetrates the fluid in the stirred vessel and to build an easy to use correlation for research and industrial purposes. A model of the jet trajectory based on the analogy with a jet in a cross-flow has been used to predict the jet trajectory at the pilot and industrial scales. The correlation, built using a statistical analysis, has shown that the jet in a cross-flow model performs very well to describe the jet trajectories. A very interesting conclusion is that the correlation constants were found to be independent of scale. Finally, the authors proposed a definition of the penetration depth and use it in its dimensionless form to predict how the jet penetrates in the industrial vessel with the current injection conditions

Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incom- plete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop, MICCAI 201

Asymmetries arising from the space-filling nature of vascular networks

Cardiovascular networks span the body by branching across many generations of vessels. The resulting structure delivers blood over long distances to supply all cells with oxygen via the relatively short-range process of diffusion at the capillary level. The structural features of the network that accomplish this density and ubiquity of capillaries are often called space-filling. There are multiple strategies to fill a space, but some strategies do not lead to biologically adaptive structures by requiring too much construction material or space, delivering resources too slowly, or using too much power to move blood through the system. We empirically measure the structure of real networks (18 humans and 1 mouse) and compare these observations with predictions of model networks that are space-filling and constrained by a few guiding biological principles. We devise a numerical method that enables the investigation of space-filling strategies and determination of which biological principles influence network structure. Optimization for only a single principle creates unrealistic networks that represent an extreme limit of the possible structures that could be observed in nature. We first study these extreme limits for two competing principles, minimal total material and minimal path lengths. We combine these two principles and enforce various thresholds for balance in the network hierarchy, which provides a novel approach that highlights the trade-offs faced by biological networks and yields predictions that better match our empirical data.Comment: 17 pages, 15 figure