Session A22: Energy: General and Storage

Desalination Pretreatment Using Fully Insulated Solar Air Heater

The performance of solar desalination systems based on a  humidification-dehumidification approach is significantly enhanced by preheating the air coming into the evaporator.  To estimate the potential benefits, we present an analysis of integrating a novel solar air heater (SAH) with a humidification-dehumidification system. An analysis of a solar desalination technology based on direct-contact humidification-dehumidification desalination combined with a novel solar air heater (SAH) is presented. The combination of both units leads to intriguing applications, such as incorporating these systems into buildings. A model of the solar thermal system is developed, and their predictions are validated with experimental data.  The SAH model is combined with a previously validated model of a humidification-dehumidification. This allows us to estimate the performance of a combined SAH/humidification-dehumidification system under different environmental conditions and to evaluate the annual water treatment capacity at various sites around the world. Simulations suggest that a system with a 3.5 m² solar collector in arid regions (Middle-East, north of Africa, south-west of the USA, East of South America), has the potential to treat 30 tons of water and reduce the CO₂ emission by 150 kg annually.   

On the Role of Atmospheric Turbulence and Stability on Machine Learning Predictions of Wind Speed for Optimal Control of Renewable Microgrids

Effective and reliable control of wind turbines during the operation of microgrids requires accurate wind speed predictions. This study introduces an innovative approach integrating atmospheric turbulence and stability features into machine and deep learning models. Employing these features significantly improves traditional on-site wind speed prediction by employing high-frequency sensor data, leading to advancements in both primary and tertiary renewable microgrid controls. The performance metrics reveal the significant influence of incorporating turbulent fluxes and stability features into predictive models, leading to more efficient and sustainable operation of wind turbines. This research emphasizes the impactful role of machine learning algorithms, informed by high-frequency data, to contribute significantly towards optimizing renewable energy technologies and microgrid operations.   

Graphene Ink Gel: Versatile Applications in Electronics and Thermal Systems

Graphene ink gel is a remarkable gel consisting of dispersed graphene nanoparticles in a liquid medium, offering exceptional mechanical, thermal, and electrical properties. The gel's versatility and unique characteristics have drawn significant attention, finding applications in printing conductive patterns, flexible electronics, sensors, and energy storage devices. Utilizing techniques like inkjet printing, screen printing, or spray coating, the gel can be easily applied to diverse substrates, enabling the creation of customized and intricate structures.

Adjusting the concentration of graphene nanoparticles and the choice of liquid medium provides control over the gel's properties, making it a promising material in electronics, photonics, biomedical devices, and energy storage technologies.

While not directly related to fluid flow, graphene ink gel's high electrical and thermal conductivity, mechanical strength, and flexibility have found relevance in microfluidics and lab-on-a-chip devices. It also holds potential in enhancing heat transfer in thermal management applications and can be used in flexible and wearable devices for healthcare and diagnostics purposes. This paper explores the diverse applications and potential impact of graphene ink gel on fluid-related research and technologies.   

Performance of an energy-efficient multi-stack PEM fuel cell system with optimized oxygen excess ratio

The increasing concerns over global warming resulting from transportation systems have spurred the automotive industry to shift towards green technologies. In recent years, the consideration of hydrogen fuel cell vehicles has gained considerable importance due to their high energy density, fast refuelling, and zero local emissions. Road freight vehicles contribute significantly to overall emissions, making them a strategic target for sustainable technological progress. Hybrid multi-stack proton exchange membrane (PEM) fuel cell systems with a battery can strategically provide fast refuelling and high-power demand for this application. The gaseous supply to the fuel cell accounts for most of the parasitic power consumption of the balance of plant. This works explores the performance of an energy-efficient PEM fuel cell system with an optimized oxygen excess ratio (OER). At one extreme, oxygen starvation degrades the fuel cell. However, at the other extreme, the additional power required by the compressor will eventually reduce the system's net power. The increased fuel cell energy production and increased parasitic load of the compressor are both nonlinear. Thus, the energy-efficient OER must be carefully tuned to maximize the fuel cell system efficiency. The efficiency map will be presented along with the system (fuel cell, battery, compressor, humidifier, heat exchanger, DC/DC converter, etc.) simulation following the driving cycle.   

Fluid Physics Impacting the Vanadium Redox Flow Battery

Here we review the stress tensor, deviatoric stress tensor, Darcy’s Law, Newtonian and non-Newtonian motion, compressible and incompressible fluids, and the Navier-Stokes equation, to address issues with inserting the vanadium redox flow battery into commercial and residential environments. New developments to independently control electrolyte fluid flow into the negative and positive electrodes are presented.   

A homogenized model for multi-phase transport and reaction processes in electrochemical CO2 reduction at gas diffusion electrodes

The limited carbon- and energy-efficiency of electrochemical CO₂ reduction cells has prompted the development of multi-scale gas diffusion electrodes (GDEs) with embedded catalyst structures, which are intended to improve dissolution of gaseous reactants into the aqueous phase and accelerate the rate of favorable Faradaic reactions. Accurate and efficient modeling of the coupled reaction and transport processes in GDEs may substantially contribute to the optimization of CO₂ reduction cells, but considerable challenges arise due to the multi-scale, multi-phase, and morphologically complex nature of GDE and catalyst structures. In this work, we develop and numerically simulate a model that efficiently incorporates a wide range of physical richness in GDEs, enabling the prediction of system-level behavior while capturing pore-scale and embedded interfacial effects via homogenization. This model, coupled with the appropriate numerics, permits exploration of the high-dimensional parameter space associated with CO₂ reduction cells, offering insight into the optimal design of catalysts and GDEs.   

The Current from Non-Disintegrable Suspended Particles at a Rotating Disk Electrode: A theoretical and Experimental Study.

Understanding the current response at an electrode from suspended solid particles in an electrolyte-liquid solution is crucial for developing materials to be used in semi-solid electrodes for energy storage applications. In this talk, an analytical model is presented to predict and understand the current response from non-disintegrable solid particles at a rotating disk electrode. The current is shown to be limited by a combination of ion diffusion within the solid particle and the mean residence time of the particle at the rotating disk electrode. This results in a relationship between current and angular frequency of I ∝ ω^3/4, instead of the classical Levich theory prediction I ∝ ω^1/2.. Specifically, the current response of LTO microparticles suspended in a non-aqueous electrolyte of lithium hexafluorophosphate (LiPF6) in ethylene carbonate:diethyl carbonate (EC:DEC) was determined experimentally and compared favorably with predictions from the proposed analytical model using fitting parameters consistent with the experimental conditions.