Measurement and modelling of electrical, transport and phase-change phenomena and application to Vacuum Arc Remelting for Optimal Material Quality

为了节省燃料，飞机部件必须设计得尽可能轻，但为了保证我们的安全，它们必须完全可靠。如果一个特定的部件必须承受一定的力，那么为了安全地使它变小（以减轻重量），我们必须增加制造它的材料的强度。不幸的是，随着部件变得越来越小，随着材料强度的增加，它们对小裂缝、空隙或异物（“缺陷”）的存在变得非常敏感；即使只有万分之二毫米宽的裂缝也会导致部件过早失效。因此，提高这些材料的质量不仅使飞行更安全，而且还减少了燃料的使用量和产生的污染。这只是一个例子，但它也适用于发电站、化学工业、石油和天然气钻井平台的涡轮机。所有这些应用都使用真空电弧重熔（VAR）制造的材料。VAR利用电力以可控的方式缓慢融化和再凝固一大块圆柱形材料（一个“锭”，通常是几吨），从而显著提高其质量。其他重要的过程，如制造铝，有许多相似之处。在VAR过程中，大量的熔融金属存在于钢锭顶部的池中，其流动和凝固的方式极大地影响了最终产品的质量。用于加热金属的电流也会在金属内部产生磁场，这些磁场、电流本身和温度变化的组合会在液态金属内部产生力，导致复杂的运动模式。这些图案曾被认为是围绕熔池的中轴线对称的，但最近的研究表明，情况并非如此。不幸的是，目前还没有足够的数据来确定流偏离对称的程度，或者是什么导致了不对称，而且现有的过程模型也不够强大，无法使用这些信息。由于VAR过程中的高温，并且需要在密封的真空室中进行（因为它是真空电弧重熔），因此也很难测量发生了什么。然而，通过最近完成的一个项目，已经开发出可以放置在VAR设备外部的传感器，但仍然可以检测到电流在哪里流动。这些需要进一步开发，并且来自它们的数据与来自其他传感器（如摄像机和温度传感器）的数据相结合，并在计算机模型中使用，以提供更清晰的整体理解。一旦我们知道了电的作用，我们需要了解它是如何影响生产材料的质量的，同样是通过建模。我们还想知道是什么控制着电行为，这样我们就可以在必要的时候想出修改它的方法。通过这个项目，我们希望开发传感器，并将其应用于制造高级钢和镍合金的熔炉，并开发一种新型的计算机模型，该模型不假设钢锭顶部的行为是相同的，也不假设行为在任何时候都是相同的。该模型将特别具有预测金属如何固化的非常小的细节（称为金属的“微观结构”）的能力，这对于确定金属的性能有多好很重要。我们希望利用传感器和计算机模型来帮助使用VAR的工厂生产质量更好的产品。我们还想开发控制VAR的方法，以进一步提高产品质量。该模型对其他工艺和其他金属也非常有用；我们也相信，这种模型，它背后的科学，以及我们将开发的技术，对科学家研究许多其他类似问题也很有用，比如生物支架组织生长过程中的流动。

In order to save fuel, aircraft components have to be designed to have as little weight as possible, but to keep us safe they have to be completely reliable. If a particular component has to withstand a certain amount of force, then to safely make it smaller (to save weight) we have to increase the strength of the material it's made from. Unfortunately as components become smaller, and as the strength of materials increases, they become very sensitive to the presence of small cracks, voids or foreign objects ('defects'); even something 20 thousandths of a mm across can start a crack which causes a component to fail prematurely. So improving the quality of these materials not only makes flying safer but also reduces the amount of fuel used and the pollution produced. That's just one example, but it also applies to the turbines in power stations, the chemical industry, and oil and gas rigs. All of these applications use material made by Vacuum Arc Remelting (VAR). VAR uses electrical power to slowly melt and resolidify a large cylindrical block of material (an 'ingot', typically a few tonnes) in a controlled way which dramatically improves its quality. Other important processes, such as manufacturing aluminium, have many similarities.During VAR a large amount of molten metal is present in a pool at the top of the ingot, and the way in which this flows and solidifies greatly affects the quality of the final product. The electrical currents used to heat the metal also cause magnetic fields within it, and the combination of these fields, the current itself, and variations in temperature lead to forces within the liquid metal causing complicated patterns of motion. These patterns had been thought to be symmetric around the central axis of the molten pool, but recent work indicates that this is not the case. Unfortunately there is not yet enough data to decide how far the flow deviates from symmetry, or what causes the asymmetry, and existing process models are not powerful enough to use this information. Because of the high temperatures during VAR, and because it needs to happen inside a sealed vacuum chamber (as it's Vacuum arc remelting), it's also very difficult to measure what's happening. However through a recently-finished programme, sensors have been developed which can be placed outside of VAR equipment but still detect where the electrical current is flowing within. These need to be developed further, and the data from them combined with data from other sensors such as video cameras and temperature sensors and used within a computer model to give a clearer overall understanding. Once we know what's going on electrically, we need to understand how it affects the quality of the material produced, again using modelling. We also want to know what controls the electrical behaviour so that we can come up with ways to modify it if necessary.Through this programme we want to develop the sensors and apply them to furnaces which make advanced steel and nickel alloys, and to develop a new type of computer model that does not assume that the behaviour is the same all the way round the top of the ingot, and does not assume that the behaviour is the same at all times. The model will specially have the ability to predict very small details about how the metal solidifies (called the 'microstructure' of the metal) that are important for determining how well the metal will perform. We want to use the sensors and the computer model to help the factories which use VAR to make better quality products. We also want to develop ways of controlling VAR, to improve product quality even more. The model will also be very useful for other processes and other metals; we also believe that this kind of model, the science behind it, and the techniques we will have developed will also be useful to scientists studying many other analogous problems, such as flow during tissue growth in bioscaffolds.