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。如果一个特定的组件必须承受一定的力，那么为了安全地使它更小（以减轻重量），我们必须增加其制造材料的强度。不幸的是，随着组件变得越来越小，以及材料强度的增加，它们对小裂缝、空隙或异物（“缺陷”）的存在变得非常敏感;即使是千分之二十毫米的东西也会引发裂缝，导致组件过早失效。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，化学工业和石油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并重新凝固大块材料（“铸锭”，通常为几吨），从而显著提高其质量。其他重要工艺，如铝的生产，也有许多相似之处。在VAR过程中，大量熔融金属存在于铸锭顶部的熔池中，其流动和凝固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、电流本身以及温度变化的组合会在液态金属内部产生力，从而导致复杂的运动模式。这些图案被认为是对称的中心轴周围的熔池，但最近的工作表明，这是不是这样的。不幸的是，目前还没有足够的数据来确定流动偏离对称性的程度，或者是什么导致了不对称性，并且现有的过程模型还没有强大到足以使用这些信息。由于VAR过程中的高温，并且由于它需要在密封的真空室内发生（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，但仍然可以检测电流在其中流动的位置。这些需要进一步开发，并将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）的数据结合起来，并在计算机模型中使用，以提供更清晰的整体理解。一旦我们知道了电气方面的情况，我们就需要了解它如何影响所生产材料的质量，再次使用建模。我们还想知道是什么控制着电行为，以便我们在必要时提出修改它的方法。通过这个项目，我们想开发传感器，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，这种模型不假设钢锭顶部周围的行为都是一样的，并且不假设行为在所有时间都是相同的。该模型将特别能够预测有关金属如何凝固的非常小的细节（称为金属的“微观结构”），这些细节对于确定金属的性能非常重要。我们希望使用传感器和计算机模型来帮助使用VAR的工厂生产更优质的产品。我们还希望开发控制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，它背后的科学，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期间的流动。

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.