DrRhea Liem

Research interests
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- Multidisciplinary design and optimisation (MDO)
- Aircraft design
- Flight mission analysis
- Air transportation modelling and optimisation
- Data-enhanced modelling
- Machine learning and data analytics
- Computational modelling for complex systems

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*** HIGHLIGHTS ***
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This is an exciting time for aerospace engineering, what with remarkable new systems and capabilities of airborne and spaceborne vehicles being introduced in rapid successions. New ideas and concepts such as unconventional aircraft configuration, silent and fuel-efficient aircraft, advanced air mobility, alternative fuel, novel propulsion systems, and space travels, to name a few, have become increasingly more familiar even to laypeople. The next few decades will bring even more remarkable development beyond what we can envision today. Computational science, which has been vital in enabling the design and analysis of complex aerospace systems, will play an important role in this development. Computational techniques allow researchers and practitioners to model and examine phenomena that are too complex, costly, and hazardous for experimentation, even when past experience and data are scarce, thereby addressing problems previously deemed intractable. Computational science also makes it possible to analyse the interdependence of processes across disciplinary boundaries, encompassing techno-socio-economic disciplines, via multidisciplinary and systems engineering approaches. However, how to effectively use computational methods to represent and solve real-world problems, where models might be imperfect and data limited and noisy, remains an open challenge. This question motivates, drives, and shapes my research, and I am excited to see what my research group and I can contribute.


Research motivations -- real-world problems
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- To design aircraft that can perform optimally across the entire flight operating envelope
- To design physically-flyable operations with realistic fuel and noise considerations

Aerospace computation with MDO
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- Approaches design as a systematic decision-making processes
- Enables analysing the interdependence of processes and designs across disciplinary boundaries, encompassing techno-socio-economic disciplines
- Requires derivations of realistic models and problem formulations

Hybrid modelling -- learning from data through the lens of physics-based models
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- Combines the objectivity of data and the interpretability of physics-based models
- Infusing data into physics-based models yields more realistic results

Data-enhanced fuel assessment model development
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- To support policy analysis
- To support airline's fuel budgeting
- To support flight path optimisation
- To support detailed aircraft design process
- ...