Dr Rajesh Ransing

Research Highlights

1. Ransing, R. (2022). Thermal irreversibility demystified. International Journal of Numerical Methods for Heat and Fluid Flow, 33(2)

   https://doi.org/10.1108/hff-02-2022-0079, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60760
2. Mahmoodi, P., Ransing, R., & Friswell, M. (2016). A novel mathematical formulation for predicting symmetric passive bipedal walking motion with unbalanced masses. Applied Mathematical Modelling, 40(5-6), 3895-3906.

   https://doi.org/10.1016/j.apm.2015.10.051, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa26355
3. Giannetti, C. & Ransing, R. (2016). Risk based uncertainty quantification to improve robustness of manufacturing operations. Computers & Industrial Engineering, 101, 70-80.

   https://doi.org/10.1016/j.cie.2016.08.002, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa29528
4. Ransing, R., Batbooti, R., & Giannetti, C. (2016). A quality correlation algorithm for tolerance synthesis in manufacturing operations. Computers & Industrial Engineering, 93, 1-11.

   https://doi.org/10.1016/j.cie.2015.12.008, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa25053
5. Mahmoodi, P., Aristodemou, S., Ransing, R., Owen, N., & Friswell, M. (2016). Prosthetic foot design optimisation based on roll-over shape and ground reaction force characteristics. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

   https://doi.org/10.1177/0954406216643110, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa26762
6. Batbooti, R., Ransing, R., Ransing, M., & Ransing, R. (2016). A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis. Computers & Industrial Engineering

   https://doi.org/10.1016/j.cie.2016.09.002, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa29741
7. Hafezi, F., Ransing, R., Lewis, R., & Ransing, R. (2016). The calculation of drag on nano-cylinders. International Journal for Numerical Methods in Engineering, 111(11)

   https://doi.org/10.1002/nme.5489, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa31407
8. Charles, W., Mahmoodi, P., Ransing, R., Sazonov, I., & Friswell, M. (2016). Comparison of point foot, collisional and smooth rolling contact models on the bifurcations and stability of bipedal walking. European Journal of Computational Mechanics, 25(3), 273-293.

   https://doi.org/10.1080/17797179.2016.1191122, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa31527

   http://www.tandfonline.com/doi/full/10.1080/17797179.2016.1191122
9. Arjunwadkar, S., Ransing, M., & Ransing, R. (2015). Seven Steps to Energy Efficiency for Foundries. Foundry Management and Technology, 143(3), 24-29.

   SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24998

   http://foundrymag.com/energy-efficiency
10. Ransing, M. & Ransing, R. (2014). “If Only My Foundry Knew What it Knows ...”: A 7Epsilon Perspective on Root Cause Analysis and Corrective Action Plans for ISO9001:2008. In Proceedings of the 61nd Annual Technical Conference on Investment Casting, Covington, KY, USA, October 5-8, 2014

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24537

    https://www.investmentcasting.org/paper_detail.asp?PaperID=999
11. R., S., M., N., R., W., & Ransing, R. (1995). ICADA: Intelligent computer aided defect analysis for castings. Journal of Intelligent Manufacturing, 6(1), 29-40.

    https://doi.org/10.1007/bf00123674, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24536
12. Ransing, R., Lewis, R., & Ransing, R. (1997). Optimal design of die coating thickness using the Lewis-Ransing correlation. , 9

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24535

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0042786401&partnerID=MN8TOARS
13. R., W., R.S., R., & Ransing, R. (1997). A semantically constrained Bayesian network for manufacturing diagnosis. International Journal of Production Research, 35(8), 2171-2188.

    https://doi.org/10.1080/002075497194796, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24534
14. R., S., R.W., L., & Ransing, R. (1997). A semantically constrained neural network for manufacturing diagnosis. International Journal of Production Research, 35(9), 2639-2660.

    https://doi.org/10.1080/002075497194705, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24533
15. Lewis, R., Ransing, R., & Ransing, R. (1998). Correlation to describe interfacial heat transfer during solidification simulation and its use in the optimal feeding design of castings. , 29

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24532

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0032046050&partnerID=MN8TOARS
16. Lewis, R., Cross, J., Gethin, D., Ransing, R., Manzari, M., Rahman, N., & Ransing, R. (1998). Recent developments in heat transfer and porous media.

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24531

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0032275225&partnerID=MN8TOARS
17. Ransing, R., Lewis, R., & Ransing, R. (1998). Recognising patterns in rejection data for diagnosis. , 10

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24530

    http://www.scopus.com/inward/record.url?eid=2-s2.0-24044448329&partnerID=MN8TOARS
18. R., W., R., S., & Ransing, R. (1998). A correlation to describe interfacial heat transfer during solidification simulation and its use in the optimal feeding design of castings. Metallurgical and Materials Transactions B, 29(2), 437-448.

    https://doi.org/10.1007/s11663-998-0122-y, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24529
19. Ransing, R., Lewis, R., Gethin, D., & Ransing, R. (1999). Lewis-Ransing correlation to optimally design the metal-mould heat transfer. , 9

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24528

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0040631470&partnerID=MN8TOARS
20. Ransing, R., Gethin, D., Lewis, R., Rowe, R., Roberts, R., & Ransing, R. (1999). Modelling and simulation of the compaction of pharmaceutical powders. , 51

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24527

    http://www.scopus.com/inward/record.url?eid=2-s2.0-4544244255&partnerID=MN8TOARS
21. R.S., R., R.W., L., D.T., G., & Ransing, R. (1999). Lewis‐Ransing correlation to optimally design the metal‐mould heat transfer. International Journal of Numerical Methods for Heat & Fluid Flow, 9(3), 318-334.

    https://doi.org/10.1108/09615539910260167, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24526
22. Lewis, R., Manzari, M., Ransing, R., Gethin, D., & Ransing, R. (2000). Casting shape optimization via process modelling. , 21

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24525

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0033626910&partnerID=MN8TOARS
23. R.W., L., R.S., R., & Ransing, R. (2000). The optimal design of interfacial heat transfer coefficients via a thermal stress model. Finite Elements in Analysis and Design, 34(2), 193-209.

    https://doi.org/10.1016/s0168-874x(99)00038-4, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24524
24. R.W, L., M.T, M., R.S, R., D.T, G., & Ransing, R. (2000). Casting shape optimisation via process modelling. Materials & Design, 21(4), 381-386.

    https://doi.org/10.1016/s0261-3069(99)00079-5, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24523
25. R.S, R., D.T, G., A.R, K., P, M., R.W, L., & Ransing, R. (2000). Powder compaction modelling via the discrete and finite element method. Materials & Design, 21(4), 263-269.

    https://doi.org/10.1016/s0261-3069(99)00081-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24522
26. D.T, G., R.S, R., R.W, L., M, D., A.J.L, C., & Ransing, R. (2001). Numerical comparison of a deformable discrete element model and an equivalent continuum analysis for the compaction of ductile porous material. Computers & Structures, 79(13), 1287-1294.

    https://doi.org/10.1016/s0045-7949(01)00015-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24521
27. Lewis, R., Ransing, M., Ransing, R., & Ransing, R. (2002). An approach for casting defect analysis employing finite element design optimisation, media axis transformation and neural networks. , 15

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24520

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0036456188&partnerID=MN8TOARS
28. Cox, I., Lewis, R., Ransing, R., Laszczewski, H., Berni, G., & Ransing, R. (2002). Application of neural computing in basic oxygen steelmaking. Journal of Materials Processing Technology, 120(1-3), 310-315.

    https://doi.org/10.1016/S0924-0136(01)01136-0, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24519

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0037080258&partnerID=MN8TOARS
29. X., S., R., W., D., T., R., S., R., C., & Ransing, R. (2002). Discrete-Finite Element Modelling of Pharmaceutical Powder Compaction: A Two-Stage Contact Detection Algorithm for Non-Spherical Particles. In Discrete Element MethodsAmerican Society of Civil Engineers.

    https://doi.org/10.1061/40647(259)14, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24518
30. Gethin, D., Lewis, R., Ransing, R., & Ransing, R. (2003). A discrete deformable element approach for the compaction of powder systems. Modelling and Simulation in Materials Science and Engineering, 11(1), 101-114.

    https://doi.org/10.1088/0965-0393/11/1/308, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24517

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0037250837&partnerID=MN8TOARS
31. Malan, A., Lewis, R., Ransing, R., & Ransing, R. (2003). Modeling Coupled Heat and Mass Transfer in Drying Capillary Hygroscopic and Non-hygroscopic Porous Materials.

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24516

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0142209292&partnerID=MN8TOARS
32. Ransing, R., Ransing, M., Lewis, R., & Ransing, R. (2003). On the limitations of neural network techniques for analysing cause and effect relationships in manufacturing processes - A case study

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24515

    http://www.scopus.com/inward/record.url?eid=2-s2.0-2942677405&partnerID=MN8TOARS
33. Ransing, R., Pao, W., Lewis, R., & Ransing, R. (2003). Solidification and thermal stress simulation using medial objects.

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24514

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0142240290&partnerID=MN8TOARS
34. Ransing, R., Ransing, M., Lewis, R., & Ransing, R. (2003). The importance of an adaptive gain variation method in back-propagation neural network learning algorithms

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24513

    http://www.scopus.com/inward/record.url?eid=2-s2.0-2942696652&partnerID=MN8TOARS
35. Javier, B., Rajesh, R., & Ransing, R. (2003). 10th anniversary conference of ACME-UK. Communications in Numerical Methods in Engineering, 19(9), 657-657.

    https://doi.org/10.1002/cnm.624, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24512
36. Lewis, R., Postek, E., Ransing, R., Han, Z., Gethin, D., & Ransing, R. (2004). A finite element model of the squeeze casting process.

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24511

    http://www.scopus.com/inward/record.url?eid=2-s2.0-84893519303&partnerID=MN8TOARS
37. Harris, A., Hargreaves, A., Southworth, G., & Ransing, R. (2005). Editorial. School Leadership & Management, 25(4), 317-319.

    https://doi.org/10.1080/13634230500197108, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24510

    http://www.scopus.com/inward/record.url?eid=2-s2.0-0842326588&partnerID=MN8TOARS
38. Ransing, R. & Ransing, R. (2004). International Journal of Numerical Methods of Heat and Fluid Flow: Editorial. , 14

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24509

    http://www.scopus.com/inward/record.url?eid=2-s2.0-1842483471&partnerID=MN8TOARS
39. W.K.S, P., R.S, R., R.W, L., C, L., & Ransing, R. (2004). A medial-axes-based interpolation method for solidification simulation. Finite Elements in Analysis and Design, 40(5-6), 577-593.

    https://doi.org/10.1016/s0168-874x(03)00097-0, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24508
40. R.W., L., R.S., R., W.K.S., P., K., K., J., B., & Ransing, R. (2004). Alternative techniques for casting process simulation. International Journal of Numerical Methods for Heat & Fluid Flow, 14(2), 145-166.

    https://doi.org/10.1108/09615530410513782, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24507
41. Ransing, R. & Ransing, R. (2005). 'Thinking' software set to revolutionise defect analysis

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24506

    http://www.scopus.com/inward/record.url?eid=2-s2.0-18844425207&partnerID=MN8TOARS
42. Ransing, R., Pao, W., Lin, C., Sood, M., Lewis, R., & Ransing, R. (2005). Enhanced medial axis interpolation algorithm and its application to hotspot prediction in a mould–casting assembly. International Journal of Cast Metals Research, 18(1), 1-12.

    https://doi.org/10.1179/136404604225022766, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24505

    http://www.scopus.com/inward/record.url?eid=2-s2.0-27444442963&partnerID=MN8TOARS
43. Postek, E., Lewis, R., Gethin, D., Ransing, R., & Ransing, R. (2005). Influence of initial stresses on the cast behaviour during squeeze forming processes. Journal of Materials Processing Technology, 159(3), 338-346.

    https://doi.org/10.1016/j.jmatprotec.2004.05.022, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24504

    http://www.scopus.com/inward/record.url?eid=2-s2.0-11344253035&partnerID=MN8TOARS
44. Ransing, R., Sood, M., & Ransing, R. (2005). Review of optimization methods for casting simulation.

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24503

    http://www.scopus.com/inward/record.url?eid=2-s2.0-32544461286&partnerID=MN8TOARS
45. Ahmad, R., Gethin, D., Lewis, R., Ransing, R., Postek, E., & Ransing, R. (2005). Thermal optimisation of the squeeze-forming process using genetic algorithms.

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24502

    http://www.scopus.com/inward/record.url?eid=2-s2.0-80053436937&partnerID=MN8TOARS
46. R., S., M., P., & Ransing, R. (2006). Optimization in castings—An overview of relevant computational technologies and future challenges. Metallurgical and Materials Transactions B, 37(6), 905-911.

    https://doi.org/10.1007/bf02735012, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24501
47. Ransing, R. & Ransing, R. (2006). Software solutions: Recall with X1Recall.com

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24500

    http://www.scopus.com/inward/record.url?eid=2-s2.0-33644511088&partnerID=MN8TOARS
48. Nazri, N., Meghana, R., Rajesh, R., & Ransing, R. (2006). An Improved Learning Algorithm Based on The Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method For Back Propagation Neural Networks. In Sixth International Conference on Intelligent Systems Design and ApplicationsIEEE.

    https://doi.org/10.1109/isda.2006.95, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24499
49. Nawi, N., Ransing, M., Ransing, R., & Ransing, R. (2007). Improving the gradient based search direction to enhance training efficiency of back propagation based neural network algorithms.

    https://doi.org/10.1007/978-1-84628-663-6-4, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24498

    http://www.scopus.com/inward/record.url?eid=2-s2.0-84881435971&partnerID=MN8TOARS
50. Nazri, M., Meghana, R., Rajesh, S., & Ransing, R. (2007). Research and Development in Intelligent Systems XXIII

    https://doi.org/10.1007/978-1-84628-663-6_4, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24497
51. Ransing, R., Ransing, M., & Ransing, R. (2007). Innovations in machine learning and defect diagnostics

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24496

    http://www.scopus.com/inward/record.url?eid=2-s2.0-34250874412&partnerID=MN8TOARS
52. Nazri, M., Rajesh, S., Meghana, R., & Ransing, R. (2008). A New Method to Improve the Gradient Based Search Direction to Enhance the Computational Efficiency of Back Propagation Based Neural Network Algorithms. In 2008 Second Asia International Conference on Modelling & Simulation (AMS)IEEE.

    https://doi.org/10.1109/ams.2008.70, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24495
53. Nawi, N., Ransing, R., Salleh, M., Ghazali, R., Hamid, N., & Ransing, R. (2010). Database Theory and Application, Bio-Science and Bio-Technology

    https://doi.org/10.1007/978-3-642-17622-7_18, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24494

    http://www.scopus.com/inward/record.url?eid=2-s2.0-78650769143&partnerID=MN8TOARS
54. Ransing, R. & Ransing, R. (2010). Back to basics: Process optimisation using in-process data

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24493

    http://www.scopus.com/inward/record.url?eid=2-s2.0-78650875982&partnerID=MN8TOARS
55. Ransing, R. & Ransing, R. (2010). Challenges in investment casting process control

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24492

    http://www.scopus.com/inward/record.url?eid=2-s2.0-77954331787&partnerID=MN8TOARS
56. Paine, A., Ransing, R., Gethin, D., Sims, G., Richley, J., Boissevain, L., Lewis, M., & Ransing, R. (2013). Challenges faced by a small ship yard in integrating computer aided design and production processes - A real life case study.

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24491

    http://www.scopus.com/inward/record.url?eid=2-s2.0-84903637677&partnerID=MN8TOARS
57. Roshan , H., Giannetti, C., Ransing, M., & Ransing, R. (2014). “If only my foundry knew what it knows …”: A 7Epsilon perspective on root cause analysis and corrective action plans for ISO9001:2008. In World Foundry Congress 2014

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24479
58. Giannetti, C., Ransing, M., Ransing, R., Bould, D., Gethin, D., & Sienz, J. (2015). Organisational Knowledge Management for Defect Reduction and Sustainable Development in Foundries. International Journal of Knowledge and Systems Science, 6(3), 18-37.

    https://doi.org/10.4018/ijkss.2015070102, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa21931
59. Giannetti, C., Ransing, R., Ransing, M., Bould, D., Gethin, D., & Sienz, J. (2014). A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data. Computers & Industrial Engineering, 72, 217-229.

    https://doi.org/10.1016/j.cie.2014.03.017, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa21495
60. Ransing, R., Giannetti, C., Ransing, M., James, M., Ransing, R., & Giannetti, C. (2013). A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects. Computers in Industry, 64(5), 514-523.

    https://doi.org/10.1016/j.compind.2013.02.009, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa14574
61. Ransing, R., Sood, M., Pao, W., & Ransing, R. (2005). Computer implementation of Heuvers' circle method for thermal optimisation in castings. , 119-126.

    https://doi.org/10.1179/136404605225022910, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa1946
62. Mahmoodi, P., Ransing, R., Friswell, M., Friswell, M., & Ransing, R. (2013). Modelling the effect of ‘heel to toe’ roll-over contact on the walking dynamics of passive biped robots. Applied Mathematical Modelling

    https://doi.org/10.1016/j.apm.2013.02.048, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa14575
63. Hafezi, F., Ransing, R., & Ransing, R. (2013). Computational modelling of fluid structure interaction at nano-scale boundaries with modified Maxwellian velocity distribution. Applied Mathematical Modelling

    https://doi.org/10.1016/j.apm.2013.04.013, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa14782
64. Ransing, R., Lewis, R., & Gethin, D. (2004). Using a deformable discrete-element technique to model the compaction behaviour of mixed ductile and brittle particulate systems. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 362(1822), 1867-1884.

    https://doi.org/10.1098/rsta.2004.1421, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa1963
65. Ransing, R., Savino, S., Lewis, R., & Ransing, R. (2005). Numerical optimisation of tilt casting process. , 109-118.

    https://doi.org/10.1179/136404605225022901, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa1964
66. Ransing, R. (2009). An innovative extrusion die layout design approach for single-hole dies. Journal of Materials Processing Technology

    https://doi.org/10.1016/j.jmatprotec.2008.07.042, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa5507
67. Dyson, P., Ransing, R., Williams, P., & Williams, P. (2008). Fluid Properties at Nano/Meso Scale Wiley.

    https://doi.org/10.1002/9780470697382, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa5508
68. Ransing, R. (2011). Enhancing undergraduate student skills to meet research challenges: Case Studies and Examples. The Journal of Engineering Education, XII special issue – Research in State Engineering Colleges in – A Road Map, XXV(1-2)

    SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24897

All Research

• Deep Learning in Computational Fluid Dynamics (current)

  PhD

  Other supervisor: Prof Mike Jennings

• Evolution of computational tools and experimental methods for the development of passive prosthetic feet. (awarded 2022)

  EngD

  Other supervisor: Dr Nicholas Owen

• An investigation into the sterilisation capabilities of biopolymers and the introduction of an organisational knowledge management methodology for the medical device industry. (awarded 2022)

  MSc

  Other supervisor: Dr Will Harrison

• EG-261 Thermodynamics 2

  The module aims to introduce two main concepts: (i) thermodynamic efficiency and (ii) irreversibility applied to a variety of energy conversion processes involving heat to work transfer and vice a versa. Engineering examples will include heat, work and/or mass transfer associated with a number of steady/unsteady flow systems.

• EG-M93 Metallurgy and Alloy Design

  The module will be presented with real life scenario of improving mechanical properties of a cast component by providing suggestions for optimising its alloy composition. The module will cover the necessary knowledge required to understand the metallurgical concepts and terminology used in peer-reviewed scientific papers so that students can not only review them but evaluate results and use the information to make informed decisions in the context of alloy design.