Publications

Demand-responsive transportation (DRT) is emerging as a mode that can enhance public transit and reduce reliance on private vehicles. Achieving wider social equity and environmental benefits goals requires DRT services to serve a wider geographic and demographic area while still maintaining commercial viability. One solution is for DRT to attract a broader range of ‘choice’ users, including private car owners. Technology adoption theory proposes several potentially significant factors, but to date, limited attention has been paid to the psychological motivators and barriers to DRT adoption among this large market of car users. This study seeks to fill that gap by analysing the demographic and behavioural characteristics of individuals with and without private car access who already use DRT. We paired two attitudinal and behavioural datasets together: (i) digitised travel survey data (socio-demographics, travel reason, alternative modes, reason for switching to DRT) collected from 6,438 DRT trips across 23 microtransit and paratransit services in the USA and Canada; and (ii) a high-resolution dataset on the exact trip data (time of travel, trip duration, trip frequency) associated with each survey response. Our analysis revealed that people using DRT who otherwise have access to a car are younger, wealthier, more likely to be male, and less likely to have disabilities. They primarily use DRT for non-discretionary travel, such as healthcare, work, and education, suggesting DRT shifts trips away from private cars rather than inducing new journeys. Regression analysis shows access to a vehicle is a stronger predictor of trip length than demographic attributes of age, ethnicity and disability (but not income). These findings underscore DRT’s potential to reduce road congestion and environmental pollution, but increased adoption by car users raises a potential social equity dilemma: if DRT systems evolve to accommodate more privileged ‘choice’ user demographics, marginalised groups risk being underserved, exacerbating existing transit challenges. As DRT expands, planning to serve dual markets will be essential to ensure it serves those with the greatest need while attracting and satisfying a diverse user base to achieve longer-term societal benefits. Transparent data sharing and user-focused research are critical to refining DRT strategies that balance competing priorities.

Hydrogen is emerging as a compelling energy carrier for future transportation due to its potential to enable fully decarbonised operation and near-zero tailpipe pollutant emissions. Realising this potential in reciprocating internal combustion engines requires a detailed understanding of the complex interactions governing hydrogen combustion and emissions formation. In this context, physics-based reduced-order emission predictive modelling offers a powerful means to accelerate the development and optimisation of hydrogen-fuelled engines by enabling rapid evaluation of operating strategies without the need for extensive experimental campaigns. This study investigates the simulation of nitrogen oxides (NOx) and unburned hydrogen (uH2) emissions from a 0.5L spark-ignition direct injection single-cylinder research engine within a 1D-0D simulation approach. For NOx prediction, a simplified kinetic mechanism is coupled with both a 0D two-zone combustion model and a thermal multi-zone in-cylinder representation, enabling assessment of the need to account for temperature stratification for accurate prediction. For uH₂ emissions, phenomenological sub-models describing flame wall quenching and top-land crevice mechanisms are implemented and calibrated to capture the dominant sources of hydrogen escape during combustion.

Hydrogen direct injection involves shock-containing, under-expanded jets for which Unsteady Reynolds–Averaged Navier–Stokes (URANS) predictions of penetration and mixture preparation are difficult to attribute to turbulence closure alone, because experimental, diagnostic, boundary-condition and numerical uncertainties can obscure model-form effects. This study assesses closure-model effects for a non-reacting, engine-representative high-pressure round (Formula presented.) jet through a controlled Large-Eddy Simulation (LES)–URANS numerical experiment with matched geometry, initial and boundary conditions, numerics and meshing strategy. A ten-realisation LES database, filtered within the dissipative range, is ensemble averaged using a Spherical Intersected Volume (SIV) operator to obtain statistically converged axisymmetric reference fields at approximately 3–5 times lower sampling cost than conventional ensemble sampling. Matched a-posteriori URANS calculations with STandarD (STD), Re-Normalisation Group (RNG), Rapid-Distortion RNG (RDRNG) and Generalised RNG (GRNG) k–ε closures quantify the coupled predictive response, while complementary a-priori frozen-field analyses isolate intrinsic momentum- and species-closure behaviour. With non-closure choices controlled, all tested URANS closures reproduce injection-phase penetration with low relative root-mean-square error of 2.56– (Formula presented.), indicating that larger URANS–experiment penetration discrepancies reported for similar configurations may include substantial non-closure contributions. Residual model-form errors are concentrated in mixture preparation: transport budgets identify turbulent scalar flux, rather than turbulent momentum transport, as the primary initiator of mass-fraction error, and error-propagation analysis shows that these scalar errors propagate through density, pressure and velocity coupling. Accordingly, the inferred LES-consistent turbulent diffusivity is space-, time- and model-dependent, with the corresponding (Formula presented.) spanning approximately 0.1–2, showing that a constant value cannot recover scalar transport consistently. The standard k–ε model gives the highest overall accuracy for the isolated round jet, whereas GRNG provides the most balanced RNG-family response and is therefore the more defensible compromise for engine-relevant applications requiring strain suppression. These results support closure-consistent, dynamically varying (Formula presented.) formulations, together with higher-fidelity realisable stress closures, pressure- and density-aware adaptive mesh refinement, and RNG-family extensions for future high-pressure gaseous-injection modelling.

Despite significant advancements in hydrogen storage technology, a challenge remains to provide affordable hydrogen refuelling infrastructure for light duty road vehicles. This paper suggests a new method using swappable hydrogen tanks for distributing and dispensing fuel which will reduce the upfront cost of hydrogen refuelling stations, reduce the energy consumed to dispense high-pressure hydrogen and increase the efficiency of distribution over standard tube trailers. In the analysis, current systems for transporting and dispensing gaseous hydrogen fuel account for a significant amount of energy consumed calculated at ~23 MJ kg-1 for the given scenario. The swappable delivery system only consumes ~9 MJ kg-1, because compressing hydrogen at the point of production has removed the need for station gas handling plant and allows for a greater transport efficiency. This represents a significant 60 % saving and presents a case for an alternative refuelling method to address the issues faced by hydrogen vehicles.