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，飞机部件的设计必须尽可能轻，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更小(为了减轻重量)，我们必须增加它所用材料的强度。不幸的是，随着部件变得更小，随着材料强度的增加，它们对小裂纹、空洞或异物(缺陷)的存在变得非常敏感；即使是直径为万分之两万毫米的东西也可能会产生裂纹，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、化学工业和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)制成的材料。VAR使用电力以受控的方式缓慢熔化和重新凝固一大块圆柱形材料(一块“钢锭”，通常为几吨)，从而显著提高其质量。其他重要的过程，如铝的制造，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熔池中，而这些熔体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。这些图案一直被认为是围绕熔池的中心轴对称的，但最近的研究表明情况并非如此。不幸的是，还没有足够的数据来确定流程偏离对称的程度，或者是什么导致了不对称，而且现有的流程模型也不够强大，无法使用这些信息。由于VAR过程中的高温，以及因为它需要在密封的真空室中发生(因为它是真空弧重熔)，所以也很难测量正在发生的事情。然而，通过最近完成的一项计划，已经开发出了传感器，这种传感器可以放置在VAR设备之外，但仍然可以检测电流在其中流动的位置。这些数据需要进一步开发，并将它们的数据与其他传感器(如摄像机和温度传感器)的数据结合在一起，并在计算机模型中使用，以提供更清晰的整体理解。一旦我们知道了电学上发生了什么，我们就需要了解它是如何影响所生产的材料的质量的，同样是使用建模。我们还想知道是什么控制了电学行为，这样我们就可以想出必要的方法来修改它。通过这个程序，我们想要开发传感器，并将它们应用到制造先进钢和镍合金的熔炉中，并开发一种新型的计算机模型，它不会假设钢锭顶部的所有行为都是相同的，也不会假设所有时间都是相同的。该模型将特别能够预测金属如何凝固的非常小的细节(称为金属的“微结构”)，这些细节对于确定金属的性能将有多好非常重要。我们希望使用传感器和计算机模型来帮助使用VAR的工厂生产更高质量的产品。我们还想开发控制VAR的方法，以进一步提高产品质量。该模型也将对其他过程和其他金属非常有用；我们也相信，这种模型、其背后的科学以及我们将开发的技术也将对研究许多其他类似问题的科学家有用，例如生物支架中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.