Abstract: A molybdenum based alloy composition including between 8.5% and 9.5% hafnium and 0.15% and 0.

Abstract: A molybdenum based alloy composition including between 15% and 20% niobium and 0.05% and 0.25% carbon.

Abstract: An alloy composition consisting essentially of, by atomic percent, between 6.5 and 8% of elements selected from the group consisting of aluminium, titanium, tantalum and niobium, between 37 and 47% of elements selected from the group consisting of chromium, cobalt, iron, molybdenum and tungsten, and between 1.8 and 3.8% of elements selected from the group consisting of tungsten, molybdenum, niobium and tantalum, and, optionally, by weight percent, up to 0.04% carbon, up to 0.07% boron, and up to 0.1% zirconium, the balance being nickel and incidental impurities.

Abstract: A nickel-base alloy having the following composition (in weight percent unless otherwise stated): Cr 10.5-15.0; Co 1.7-8.8; Fe 0-5.9; Si 0-0.65; Mn 0-0.65; Mo 0.3-2.3; W 2.3-4.4; Al 2.7-4.1; Nb 1.0-4.2; Ti 1.0-3.0; Ta 2.0-5.0; Hf 0.0-0.6; C 0.02-0.06; B 0.015-0.035; Zr 0.035-0.11; S<20 ppm; P<60 ppm; the balance being Ni and incidental impurities. The alloy has an improved combination of properties (principally resistance to surface environmental damage and dwell fatigue crack growth) compared with known alloys, and is intended to operate for prolonged periods of time above 700° C., and up to peak temperatures of 800° C.

Abstract: A molybdenum based alloy composition including between 15% and 20% niobium and 0.05% and 0.

Abstract: A method of designing a material by optimising values for design variables includes the steps of: (i) providing one or more property models that produce a prediction for a value of a respective property of the material as a function of the design variables and produce a value for the uncertainty in the prediction, (ii) setting a specification target for a desired value for each property and a probability for that specification target to be met or exceeded, (iii) determining a probabilistic target for a value for each property, the probabilistic target being based on the specification target and the probability, and defining a merit index factor based on the degree to which a given prediction satisfies the probabilistic target, (iv) constructing an overall merit factor from the individual merit index factors of the properties, and (v) determining a set of optimal design variables that optimise the overall merit factor.