Richard Barker

The new Euro 7 standard will be the first legislation to limit the emissions produced by an automotive brake system. This has caused brake manufacturers to seek radical solutions to reduce the emissions generated from conventional grey cast iron (GCI) friction brakes. The regenerative braking system of electric vehicles (EVs) will require the friction brakes to be used less frequently than for an internal combustion engine vehicle. This may lead to a build-up of corrosion products on the friction surfaces that may not only affect the performance and service life of the GCI friction brake but also increase wear particle emissions when braking. Plasma electrolytic oxidation (PEO) ceramic-coated aluminium alloy rotors could be an alternative solution to reduce the effects of corrosion, produce lower brake emissions and also improve the energy efficiency of the EV by reducing its unsprung mass. To understand the interrelation between brake rotor corrosion and particulate emissions, this study concentrates on quantifying wear particles from a PEO-coated Al6082 brake rotor, both before and after exposure to salt fog corrosion. The results are compared to those for a standard uncoated GCI rotor and for an aluminium metal matrix composite (MMC) rotor subject to the same braking and corrosion test cycles. It was found that the PEO brake rotor produced a higher steady-state coefficient of friction in both the uncorroded and corroded conditions than either the GCI or MMC rotor, but emitted fewer wear particles in the uncorroded state, apart from at the highest brake line pressure. In the corroded state, the PEO rotor produced far lower emissions than either the corroded GCI or MMC rotors across all brake line pressures.

Mineral scaling is a major cause of heat transfer deterioration in water-based thermal systems, yet existing laboratory methods often fail to provide controlled hydrodynamics, constant supersaturation, and quantitative thermal performance measurements under evaporative conditions. This study presents the development and validation of a novel once-through Flow Evaporation Scaling Cell (FESC) for the quantitative evaluation of crystallization fouling on heated surfaces. The FESC combines controlled laminar flow, localised evaporation, continuous temperature monitoring, and flat coupon geometry to determine fouling resistance, induction time, and deposited scale mass under well-defined thermal and hydrodynamic conditions. The methodology was validated using ferritic stainless steel, electroless nickel, and a nickel-polymer composite coating exposed to two calcium carbonate-forming brines representing moderate and aggressive scaling conditions. The FESC demonstrated good repeatability, with relative standard deviations of 4.3% for induction time and 2.9% for asymptotic fouling resistance. Surface-dependent differences in scale morphology, polymorph, fouling resistance, and deposited mass were successfully resolved, confirming the sensitivity of the methodology. Electroless nickel showed the best anti-scaling performance, extending induction time by up to fourfold, reducing deposited calcium mass by approximately 83%, and lowering asymptotic fouling resistance by around 50% relative to stainless steel under aggressive conditions. The proposed FESC provides a reproducible and quantitative platform for evaluating anti-scaling surfaces, coatings, water chemistries, and operating conditions relevant to heat exchangers and water heating systems.

The University of Leeds has developed a high-fidelity mechanistic CO2 corrosion model that simulates the underlying bulk equilibrium, mass transport, and electrochemical processes using multiphysics simulation software. This high-fidelity model (the Leeds Model) provides accurate forecasting of corrosion rate (CR), surface pH, and the precipitation tendency of protective corrosion products within mild steel pipelines. However, there are several barriers hindering the effective use of the outputs from this model, including the requirement for trained users, licensing costs, and the timescale and complexity of achieving satisfactory convergence when performing extended run-time, large-scale parametric sweeps. In this work, we investigate the use of machine learning (ML)-based surrogate modeling to combat these limitations, to develop a surrogate model to emulate Leeds Model predictions in a much more convenient format. Six ML models were trained on over 160,000 CO2 CR predictions made by the Leeds Model, each with hyperparameters optimised accordingly. Of the models examined, the Deep Neural Network (DNN) and extra trees regressor exhibit most accurate test set predictions, achieving a MAPE of 0.44% and 0.39%, respectively. Further trade-offs between these two surrogates are discussed, highlighting significant improvements in simulation time, computational cost, and usability compared to the original model. During analysis, 47 anomalous datapoints are discovered within the training dataset, caused by poor convergence in the Leeds Model. DNN predictions are shown to be insensitive to poorly converged datapoints and prove effective in detecting convergence issues in the Leeds Model’s CR predictions.

The corrosion behaviour of deep pit-like features on X65 carbon steel under silica sand deposits in the presence of chemical corrosion inhibitors has been evaluated using a bespoke localised corrosion test cell. Two commercial chemical inhibitor components, 2-mercaptoethanol (2ME) and an imidazoline, were evaluated individually, and as a blend, via electrochemical measurements, including electrochemical impedance spectroscopy and zero resistance ammetry to assess galvanic interaction. Corrosion rates were measured at two recession depths under <8 mm thick silica deposits in a carbon dioxide (CO2)-saturated environment and compared with general corrosion conditions (absence of deposits). Inhibitor efficiency followed the trend imidazoline < 2ME ≤ blend, with all inhibitors showing <80% inhibitor efficiency in general corrosion scenarios. In the presence of deposits, the efficiency of imidazoline and the blend diminished almost entirely. The performance of 2ME decreased significantly from 91% at 0 mm specimen recession to 0% at 9 mm recession. During galvanic coupling, the inhibitor addition caused a reversal in current flow with the under-deposit specimen acting as a net anode irrespective of inhibitor chemistry.

Corrosion products that form on carbon steel in sweet (CO₂) environments can protect the steel by retarding interactions between the substrate steel and a corrosive electrolyte. Such protective films could become damaged at local sites leading to potential differences and the emergence of galvanic currents with the consequence of pitting in certain envfironments. The large protected surface (with a higher positive potential) becomes a cathode while the tiny exposed surface (with a lower positive potential) becomes the anode. Films formed in a Ca²⁺‐rich environment often comprise mixed calcium and iron carbonates in the form, Ca_xFe_(1−x)CO₃. This work utilized an artificial pit to investigate pitting under Ca_xFe_(1−x)CO₃film conditions. Mixed carbonate films were grown on cathode discs (25‐mm diameter) for 96 h at pH 7.5, 50°C. Freshly polished anode pins were screwed onto the cathode disc and the galvanic currents were measured for 24 h. The anode/cathode area ratios were varied by using pins of 1, 2, and 3 mm diameter. Scanning electron microscope, X‐ray powder diffraction, and energy‐dispersive X‐ray were employed to examine the electrode surfaces after the test while surface profilometry was conducted to obtain pit profiles on the anode pins. Galvanic current increased with the addition of 682 ppm Ca²⁺to the solution while pseudopassivation occurred under mixed calcium–iron carbonate films. The galvanic current was observed to drop as corrosion products formed on the anode electrode. Profilometry revealed the emergence of pits on the anode electrode. The pitting threshold was commensurate with the magnitude of the galvanic current density recorded.