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Royal Academy of Engineering Research Fellow

BSc (Math) National Autonomous University of Mexico
BSc (Eng) Panamerican University 
PhD Delft University of Technology

My research aims at optimising and designing novel materials for structural applications using modelling and experimentation. Engineering applications require materials to display a balance of mechanical and environmental properties, which can only be achieved by tailoring complex microstructures during component manufacturing. I am interested in studying a range of engineering alloys such as steel, superalloys, and titanium alloys, as well as multifunctional materials such as shape memory alloys and magnets. 

Advanced Material Processing 

The Achilles heel of any new material is its capacity to be made outside laboratory conditions. Advanced materials are designed with greater microstructural complexity demanding new predictive computational tools to optimise in-service performance and prevent process-related issues, e.g. cracks during rapid solidification or during non-isothermal forging. I endeavour in understanding the origins of defects and damage during advanced material processing using data science, physics-based modelling and correlative characterisation to design better material processing routes and ensure that new materials can be produced under industrial conditions. 

Engineering shear and martensitic transformations in Advanced Metallics

Materials using shear transformations are widely used in high-strength/multifunctional structures, e.g. steels and shape memory alloys, and they are becoming more important in manufacturing, e.g. during additive manufacturing. A classic example is martensite in iron, its strength can be as high as ~2.5 GPa by simply varying the carbon content and quenching the alloy. However, in spite of the importance of these transformations in Materials Science, our understanding remains mostly material-specific and very few predictive models are currently available. A reason for this is that the resulting microstructure is very complex, e.g. it consists of a very high density of crystal defects, second phases and elemental redistribution at the nanoscale. I work on deriving new physically-based models that account for these features and are able to link material’s initial structure and chemical composition with the resulting microstructure and associated mechanical/functional properties. 

Computational Material Design: Processing-oriented material optimisation 

Advanced materials are designed to be more resilient but the trade-off and bottle-neck is that they are inherently more difficult to manufacture into components. They often require the use of tailored manufacturing routes involving as much effort as the material design process itself. I combine machine learning, physics-based modelling and material characterisation to design materials with improved mechanical and environmental performance whilst ensuring their appropriate manufacturability. For instance, we have been using meso-scale modelling, deep learning and nano-characterisation to design better and more resource-efficient steels and superalloys for aerospace and automotive applications. 

Hydrogen in metals: preventing against embrittlement and environmental degradation

Preventing Hydrogen related degradation is a grand challenge in Materials Science and anticipating its behaviour is critical to the viability of many materials used in safety-critical industries such as energy, nuclear and automotive. Detection of H inside alloys is extremely challenging and computational models able to explain the physical mechanisms of hydrogen diffusion and embrittlement are highly desirable. We develop novel modelling strategies -based on combining atomistic, meso-scale and macro-scale methods- that predict how H diffuses and interacts with crystal defects, including nano-precipitates, dislocations, grain boundaries, and second phases. This is to anticipate material’s response to H embrittlement and suggest better manufacturing routes to minimise alloys’ susceptibility to H-related cracking.

(a) Modelling results predicting how (left) the stacking fault energy controls the evolution rate of deformation-induced martensite and twinning in austenitic steels and (right) their associated effects in steel’s mechanical response.

(b) Results of new model to predict hydrogen diffusion in multi-phase alloys applied to a steel sample with 50% of austenite islands within a ferritic matrix. Images are normalised maps of hydrogen concentration in ferrite (right) and austenite (left) at 350 oC during thermal desorption spectroscopy; H diffusion takes place from left to right of the image.

  • A. Turk, G. Joshi, M. Gintalas, M. Callisti, T. Ungár, P.E.J. Rivera-Díaz-del-Castillo, E.I. Galindo-Nava, "Quantification of hydrogen trapping in multiphase steels: Part I - Point traps in martensite", Acta Materialia 194 (2020) 118-133
  •  A. Turk, D. Bombač, S. Pu, P.E.J. Rivera-Díaz-del-Castillo, E.I. Galindo-Nava, "Modelling and characterisation of hydrogen trapping parameters in multi-phase steels: Part II - Effect of austenite morphology", Acta Materialia (2020) in press
  • F. León-Cazares, F. Monni, T .Jackson, E.I. Galindo-Nava, C.M.F. Rae, "Stress response and microstructural evolution of nickel-based superalloys during low cycle fatigue: physics-based modelling of cyclic hardening and softening", International Journal of Plasticity 128 (2020) 102682 
  • E.I. Galindo-Nava, "On the prediction of martensite formation in metals", Scripta Materialia 138 (2017) 6-11 
  • E.I. Galindo-Nava, P.E.J. Rivera-Díaz-del-Castillo, "Understanding martensite and twin formation in austenitic steels: A model describing TRIP and TWIP effects", Acta Materialia 128 (2017) 120-134 
  • E.I. Galindo-Nava, L.D. Connor, C.M.F. Rae, "On the prediction of the yield stress of unimodal and multimodal γ’ Nickel-base superalloys", Acta Materialia 98 (2015) 377-390