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Due to the characteristics of flow batteries, this technology is ideally suited for low-cost storage in the range of a few hours and thus for load balancing as stationary storage in grids with high amounts of renewable energy [1]. Today, a large number of different active materials for flow batteries are known, although only a few have been commercialised [2]. Basically, the energy supply and thus also the required storage should be sustainable, i.e. not cause resource problems and not be harmful to humans and the environment. A potential for a huge range of possibilities is offered by organic active materials, which should be used especially in aqueous solutions. Due to the immense possibilities, classical synthesis and testing is extremely lengthy and costly. An alternative can be model-based high-throughput screening, where by simulating the properties of active materials in the electrolyte and the battery itself, computer-based simulations can be used to conduct the search. The SONAR project is an EU-funded project in which 7 different institutions from the EU, Switzerland and Australia are developing a high-throughput screening method capable of finding new active materials for redox flow batteries. The principle is a serial coupling of different size scales, combined with molecule generation and machine learning. The chemical structure of a candidate is generated by a molecule generator and then its atomistic properties, kinetics, side reactions and cell properties are iteratively calculated with exclusion criteria. In this talk we will give an overview of 2 years of research in this project in the areas of machine learning for high throughput screening, DFT based quantum mechanics modelling, kinetics Monte Carlo methods for meso-scale, 0D cell modelling, 3D cell modelling, stack modelling and techno-economics.

The clean energy transition is imperative. Fossil fuels unsustainably supply the vast majority of global energy consumption; associated emissions exacerbate climate change and are immediately hazardous to health. Escalating energy demands only compound the issue. This thesis aims to support the transition by optimising the performance of manifold microchannel heat sinks for cooling highly concentrated photovoltaics. The proposed innovations reduce thermal resistance and parasitic pumping losses, which also serves to curb the consumption of power-hungry data centres.The research findings coalesce into two heat sink designs; one which is practical now and another which is applicable in the future. The open modification removes ineffective nozzle constriction to reduce pressure drop by 25 % without compromising the low thermal resistance of 0.120 cm2K/W within a thin 2 mm profile. Additionally, the introduction of mini-baffle pins reduces channel flow variation from 17 % to less than 1 % with only an 8 % increase in pressure drop. These features may be immediately integrated into existing manifold microchannel heat sinks using established microfabrication techniques. Innovative microchannel topologies, also developed in this thesis, reduce the pressure drop of straight rectangular channels by 79.2 % while also reducing thermal resistance by 22.3 %. Boiling flow through an additively manufactured manifold microchannel is shown to decrease the thermal resistance by 73 %, compared to single-phase flow at an equivalent pumping power. These findings support the next steps for higher heat flux cooling, as topology optimisation and additive manufacturing technologies advance.

Efficient and effective energy storage technologies are a key element of digital power networks. Batteries offer versatile capabilities, and flow batteries are well suited to large-scale applications due to inherent technical advantages. Their efficiency, capacity and power density, however, is hindered by mass transport limitations. Availability of reactants must be maintained to reduce parasitic overpotentials and maximise electrolyte utilisation. This is a coupled mechanical-electrochemical problem as pressure differentials and associated losses from pumping power must also be considered. This thesis optimises flow battery system performance using computer aided design of cell architecture innovations. Flow-through vanadium redox flow battery cells charging at high state of charge were simulated in COMSOL with two- and three-dimensional multiphysics models developed and validated against published data. Trapezoidal and annular sector shapes, where cell width is reduced towards the outlet, were compared to conventional rectangular geometry. These shapes were employed to increase electrolyte velocity from inlet to outlet, thus improving the delivery of active species as reactants are depleted. The radial design raised the minimum reactant concentration in the cell by 66%, and improved flow uniformity when compared to the trapezoidal design, at the cost of an increased pressure differential. Wedge-shaped cell designs, where the thickness is reduced instead of the width, were then simulated with variable porous electrode compression. Results showed 1% improvement to operating cell voltage. Laboratory experiments demonstrated a 15% higher energy efficiency and extended usable capacity with no parasitic pressure drop increase using this design.Static mixers were then used to address concentration gradients. The redistributing of reactants within the flow frame showed a 60% improvement in minimum V3+ concentration and a corresponding 1% cell voltage improvement by reduc

Chemotherapy induced alopecia (CIA) compounds the physical and psychological burden suffered by cancer patients every day. This thesis proposed, developed, and investigated a novel approach to CIA prevention using a flexible network comprised of thermoelectric cooling modules (TECs) combined with free convection heat sinks for precise and controlled cryotherapy. A human head was modelled numerically with COMSOL Multiphysics to predict the scalp’s reaction to thermoelectric cooling for the first time in the published literature. The head model was dimensioned using average anatomical measurements for a middle-aged female, since CIA most commonly affects women undergoing chemotherapy for breast cancer, contributing to patients’ significant mental health burden. Under a dry-hair scenario, it was found that approximately 26.5 W of thermal energy needed to be extracted from the scalp to achieve the clinically significant follicle temperature of 19°C. This was achieved using 38 thermoelectric modules each operating at a cooling duty of 0.7 W. The cooling requirements were reduced further by tuning the conductivity of the hair layer through wetting, which has been often discussed in clinical studies but never before investigated directly. Dissipation of the 2.15 W of waste heat generated by the thermoelectric coolers, was achieved through additively manufactured free convection heat sinks. A high precision free convection test apparatus was built and installed in a thermally quiescent chamber to investigate the performance of various designs. Experimental data was used to validate conjugate heat transfer models that enabled parametric studies of several critical design elements. The best design featured 50 mm tall tapered pins, and a pin-less central cavity that maximised fluid-solid interactions. This heat sink’s performance, as measured by its thermal resistance (Rth), was measured to be 11.7 K.W-1. This improved significantly by boosting the heat sink’s surface emissivi

Solar and wind power have become the dominant renewable energy technologies around the world. Hybrid PV and wind plants are gaining much interest due to their ability to take advantage of different weather systems at different times of the day. A comprehensive analysis has been performed to investigate the complementary features of solar and wind resources for a hybrid PV and wind power plant. Four different forecasting methods were applied for both solar PV and wind forecasting, including persistence, Elman neural network (ENN), wavelet neural network and autoregressive integrated moving average (ARIMA). To reduce the uncertainty associated with forecasting, battery energy storage systems (BESS) were optimised to address the problems arising from the variability in renewable energy generation.In this study, the BESS size was first determined using an analytical method with financial criteria. An economic optimisation model was developed to minimise the total operation cost of the hybrid system and attain an optimum size for the battery. To further understand the factors that impact battery sizes underneath the optimisation model, forecasting errors were decomposed in both the time and frequency domains revealing the distinct impacts of time and frequency domain components on battery sizing. Another key finding is the discovery that the required battery size favours biased forecasting when the battery model considers round-trip efficiency. This study also optimised the scheduling of the BESS using forecasting information. A mixed receding horizon control (RHC) strategy was presented to minimise the total operation cost of the entire system, including the profits from selling electricity, the costs from purchasing ancillary services for undersupply and oversupply, and the operation and maintenance costs of each component. The strategy made full use of both long-term and short-term forecasting information to improve optimisation performance. The simulation demonstrate