Predictive Modelling of the Fundamentals of Failure in Metals

Our lack of detailed understanding of the atomic scale mechanisms which lead to failure of metals through processes such as cracking, creep, embrittlement or fatigue is surprising, given the significant technological and economic impact that such understanding could generate. Examples of what could be achieved include designing stronger, lighter turbine blades for aeroplane engines, improved lightweight alloys for the automobile industry or improved radiation shields for the nuclear industry.

Progress to date has been limited partly because the current generation of continuum models for metal failure rely heavily on empirical methods. The overarching aim of this proposal is to develop new models to enable continuum-scale modelling of failure processes, in particular crack growth, by incorporating pre-computed first-principles information. Adding reliable probabilistic "error bars'' which incorporate the effects of model error, limited data, epistemic uncertainty and coarse-graining would help to address one of the major barriers holding back wider adoption of materials modelling in industry (cf. Innovate UK/KTN special interest group on Uncertainty Quantification and Management for High Value Manufacturing).

Realising these long-term aims first requires developing (i) accurate atomic scale models for `slow' failure processes in metals and (ii) a rigorous model reduction procedure to capture information lost during coarse graining, allowing complex microstructures to be modelled. This project addresses (i) in detail by developing new methodology to compute energy barriers with QM accuracy in systems large enough to capture stress concentration, with application to dislocation motion and crack growth in technologically relevant but still structurally simple single crystal model systems (nickel, aluminium and tungsten). Requirement (ii) will be explored via a case study to be further developed in future proposals.

The project is aligned with research areas in which the UK is a world leader: condensed matter (electronic structure), materials engineering (metals and alloys) and numerical analysis.

Academic impact. This project will achieve cross-disciplinary academic impact spanning the materials science, engineering and numerical analysis communities. The new modelling techniques generated will be widely disseminated and are expected to benefit the materials modelling community. The project is expected to benefit the PI by helping to consolidate his membership of this community as an independent researcher. As identified in more detail in 'Academic Beneficiaries' above, the prospects for adoption of new methodology are excellent as the applicant is well embedded in the materials simulation community and has strong links with the numerical analysis community through project collaborator Prof. Christoph Ortner (Warwick Mathematics). Knowledge gained through developing and applying the techniques will feed into algorithm development. Two-way interaction with local experimental researchers in Engineering and Physics at Warwick is expected to expand the impact beyond the modelling community, leading to interdisciplinary academic impact. Moreover, the novel methodology to be developed here is not limited to modelling failure processes in metals and could provide QM-based insight for other localised non-equilibrium processes.

Economic impact. The project is expected to generate fundamental new insights which in the long term will help rationalise and guide future design and processing developments. This is consistent with the applicant's long term goal of providing "bottom up" effective criteria for continuum finite element analysis codes, which is of great interest to industrial partners concerned by the current lack of truly predictive continuum models. Looking beyond the lifetime of this project, it is envisaged that technology transfer will allow accurate, predictive, fracture simulations without any user-adjustable parameters to be carried out directly by industrial partners. Incorporating probabilistic error bars into this framework so that uncertainties due to incomplete data and insufficient models could be computed from the information lost during coarse graining could eventually lead to significantly enhanced adoption of materials modelling in industry (e.g. in partnership with the Innovate UK/KTN special interest group on uncertainty quantification and management, of which the applicant is an active member).

Societal impact. Scarcity of resources and the tremendous energy requirements of traditional materials processing techniques raise ever-increasing sustainability concerns. Limitations on the fuel efficiency of jet engines and the difficulties of designing materials suitable for use as radiation shields for fusion power stations are just two examples reflecting the social and economic cost of our incomplete knowledge of the fundamentals of how metals fail. The need to understand/control complex chemistry to support and guide applications is just as urgent here as in other high-priority materials problems. The high costs of laboratory investigations mean that theory must come in support of experiment to produce new knowledge. However, the complex combination of local chemistry and long range stress fields characteristic of materials failure processes means that it has so far been impossible to address these problems with quantum mechanical precision. This project aims to develop new methodology that will help scientists and engineers to model these kinds of issues, in the long term helping to inform the design of improved materials where failure processes can be better controlled.