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The ladle furnace plays a critical role in the secondary steelmaking stage, where many processes take place in the ladle such as steel property and temperature homogenization, inclusion removal, degassing, and desulfurization. Although many research has been conducted to study these aspects, due to the complicated heat and mass transfer process inside the ladle, many details about the physical process are still not quite clear. For example, the efficacy of plug/injector designs in turbulent mixing of molten steel were not fully understood. Due to its complex three dimensional flow phenomena inside the ladle, previous two dimensional flow measurement of water ladle models provided little insight into understanding the three dimensional flow phenomenon of turbulent mixing. Therefore, to achieve a better understanding on the efficacy of plug/injector designs in turbulent mixing, we implemented an advanced volumetric flow measurement instrument of Shake-the-Box system to measure the three-dimensional flow field inside a water ladle model. Totally, three different plug/injector designs were tested under two different flow rates (8 LPM and 11.5 LPM) of gas injection within a volumetric flow measurement region of 4.8 cm × 4.8 cm × 2.4 cm. The flow measurement results suggest the double slits injector produces the highest turbulence kinetic energy comparing the three injectors. 

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating “avatars” (herein defined as an extension of “digital twins”) of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells. 

Complex analyses involving multiple, dependent random quantities often lead to graphical models—a set of nodes denoting variables of interest, and corresponding edges denoting statistical interactions between nodes. To develop statistical analyses for graphical data, especially towards generative modeling, one needs mathematical representations and metrics for matching and comparing graphs, and subsequent tools, such as geodesics, means, and covariances. This paper utilizes a quotient structure to develop efficient algorithms for computing these quantities, leading to useful statistical tools, including principal component analysis, statistical testing, and modeling. We demonstrate the efficacy of this framework using datasets taken from several problem areas, including letters, biochemical structures, and social networks.