Kanishk V. Ganga

Led advanced research on non-equilibrium radiative emission in hypersonic wake flows of low Earth orbit re-entry vehicles, advancing fundamental understanding of atmospheric re-entry dynamics.

Pioneered experimental methodology utilizing time-resolved spectroscopy to precisely measure non-equilibrium thermochemistry in hypersonic flows within a shock/expansion tunnel environment.

Engineered and fabricated advanced optical fibre coupling assemblies for wall-mounted spectroscopy, significantly enhancing the quality of experimental measurements.

Conducted comprehensive CFD and spectral analysis for NASA EAST expanding flow experiments, elucidating the impact of shock speed variation on radiative heating predictions for lunar return conditions.

Quantified limitations in scaling radiative heating predictions from ground tests, using CFD analysis of forebody vehicle streamlines to accurately model pressure history effects on thermochemical evolution.

Designed critical shock/expansion tunnel components, enabling planetary entry test conditions up to 20 km/s and contributing to setting the European shock speed record for impulse facilities.

Provided essential support to senior researchers and facility operators, ensuring efficient tunnel turnaround and maintenance during critical test campaigns.

Instructed final-year undergraduate students on CFD tools for supersonic nozzle flow, enhancing their practical understanding of aerospace principles.

Assessed undergraduate physics admissions tests for approximately 1000 applicants, delivering detailed feedback on problem-solving processes.

Evaluated approximately 50 high-value proposals, influencing £44M in funding decisions across two major National Space Innovation Fund programs, ensuring technical feasibility and strategic alignment.

Identified and championed disruptive early-stage concepts with high probability of success across diverse projects including launch vehicles, propulsion systems, and satellite communications.

Contributed to a 20% acceptance rate by delivering technical evaluations within stringent 2-month review cycles, providing constructive feedback and strategic recommendations.

Executed large eddy simulation CFD for reacting flows in high-pressure gas turbine experiments using pre-tabulated chemistry in OpenFOAM, enhancing simulation fidelity.

Enhanced in-house combustion solver by integrating sub-grid scale straining effects, achieving a 50% reduction in computational grid requirements while maintaining <5% temperature field accuracy.

Validated modified solver against literature, achieving improved predictions on coarser meshes through advanced straining corrections in downstream flame regions.

Co-authored a conference paper on grid size optimization for turbulent premixed combustion, translating key findings into technical documentation for industrial deployment by MHI and DLR.

Analyzed in-house Reaction Control System (RCS) thruster performance using OpenFOAM cold gas simulations, optimizing system efficiency.

Designed and implemented a centralized documentation system for cross-functional teams, reducing weekly meeting durations and enabling asynchronous collaboration across multiple time zones.

Researched and proposed innovative thruster architectures, including water-based Hall-effect configurations, to extend mission capabilities for future deployment.

Executed 1D particle-in-cell plasma simulations, demonstrating 1/B diffusion scaling in Hall-effect thrusters and challenging conventional 1/B2 scaling theory for 1D systems.

Identified off-diagonal pressure tensor terms as the dominant mechanism for anomalous electron transport, achieving 13% error reduction compared to classical predictions with fully-kinetic models.

Demonstrated negligible temporal derivatives in electron mobility, suggesting a steady-state transport mechanism distinct from oscillation-driven instabilities in Hall thruster fluid models.

Designed a deep learning network (cWGAN-GP) to increase the signal-to-noise ratio (SNR) of galactic spectra from the Dark Energy Spectroscopic Instrument database.

Validated the network on the Sloan Digital Sky Survey database, effectively learning features in low SNR regions across diverse galaxy types.

Achieved a 78% increase in the SNR of galactic spectra, successfully preserving features in previously high-noise regions.

Led a team of 8 in building an automated gamma-ray identification system achieving 100% accuracy in blind sample identification, with confidence levels between 3.4 - 11.0 σ across Irish Sea sediment samples.

Calibrated a high-purity germanium detector and developed a Python-based analysis pipeline with automated peak detection, creating a sample database that successfully matched unknown samples to known fingerprints.

Assisted in modelling detector absolute efficiency using GEANT4 Monte Carlo simulations for gamma-rays to validate measurements.