Purpose Live non-invasive monitoring of biomarkers is of great importance for the medical community. Moreover, some studies suggest that there is a substantial business gap in the development of mass-production commercial sweat-analysing wearables with great revenue potential. The objective of this work is to quantify the concentration of biomarkers that reaches the area of the garment where a sensor is positioned to advance the development of commercial sweat-analysing garments. Design/methodology/approach Computational analysis of the microfluidic transport of biomarkers within eccrine sweat glands provides a powerful way to explore the potential for quantitative measurements of biomarkers that can be related to the health and/or the physical activity parameters of an individual. The numerical modelling of sweat glands and the interaction of sweat with a textile layer remain however rather unexplored. This work presents a simulation of the production of sweat in the eccrine gland, reabsorption from the dermal duct into the surrounding skin and diffusion within an overlying garment. Findings The model represents satisfactorily the relationship between the biomarker concentration and the flow rate of sweat. The biomarker distribution across an overlying garment has also been calculated and subsequently compared to the minimum amount detectable by a sensor previously reported in the literature. The model can thus be utilized to check whether or not a given sensor can detect the minimum biomarker concentration threshold accumulated on a particular type of garment. Originality/value The present work presents to the best of our knowledge, the earliest numerical models of the sweat gland carried out so far. The model describes the flow of human sweat along the sweat duct and on to an overlying piece of garment. The model considers complex phenomena, such as reabsorption of sweat into the skin layers surrounding the duct, and the structure of the fibres composing the garment. Biomarker concentration maps are obtained to check whether sensors can detect the threshold concentration that triggers an electric signal. This model finds application in the development of smart textiles.
Distributed Energy Systems (DES) are set to play a vital role in achieving emission targets and meeting higher global energy demand by 2050. However, implementing these systems has been challenging, particularly due to uncertainties in local energy demand and renewable energy generation, which imply uncertain operational costs. In this work we are implementing a Mixed-Integer Linear Programming (MILP) model for the operation of a DES, and analysing impacts of uncertainties in electricity demand, heating demand and solar irradiance on the main model output, the total daily operational cost, using Global Sensitivity Analysis (GSA). Representative data from a case study involving nine residential areas at the University of Surrey are used to test the model for the winter season. Distribution models for uncertain variables, obtained through statistical analysis of raw data, are presented. Design results show reduced costs and emissions, whilst GSA results show that heating demand has the largest influence on the variance of total daily operational cost. Challenges and design limitations are also discussed. Overall, the methodology can be easily applied to improve DES design and operation.
Distributed Energy Systems (DES) can play a vital role as the energy sector faces unprecedented changes to reduce carbon emissions by increasing renewable and low-carbon energy generation. However, current operational DES models do not adequately reflect the influence of uncertain inputs on operational outputs, resulting in poor planning and performance. This paper details a methodology to analyse the effects of uncertain model inputs on the primary output, the total daily cost, of an operational model of a DES. Global Sensitivity Analysis (GSA) is used to quantify these effects, both individually and through interactions, on the variability of the output. A Mixed-Integer Linear Programming model for the DES design is presented, followed by the operational model, which incorporates Rolling Horizon Model Predictive Control. A subset of model inputs, which include electricity and heating demand, and solar irradiance, is treated as uncertain using data from a case study. Results show reductions of minimum 25% in the total annualised cost compared to a traditional design that purchases electricity from the centralised grid and meets heating demand using boilers. In terms of carbon emissions, the savings are much smaller, although the dependency on the national grid is drastically reduced. Limitations and suggestions for improving the overall DES design and operation are also discussed in detail, highlighting the importance of incorporating GSA into the DES framework.
Optimisation-based models are often employed for the design and operation of distributed energy systems (DES). A two-stage approach often involves the optimisation of the design of a distributed energy system for a specified location or scale, and the subsequent optimisation of the operational model based on the structure recommended by the design model. The structure includes what types of generation and storage technologies should be used in the operation, related capacities and sizes, and potential locations. Often, both design and operational models are deterministic in nature, as either past or fictitious data is fed into the models to minimise an objective function such as the total cost or environmental impact due to carbon emissions. Consequently, the operational models encounter challenges when real-time data is fed, as time-variant input variables such as electricity demand, heating demand and solar insolation can be deemed uncertain. These variables could have unexpected and significant impacts on the total costs involved with the operation of distributed energy systems, leading to sub-optimality or even infeasibilities. Identifying these input variables, quantifying their uncertainties (which are then described in the models), and evaluating the influence of these variables on the outputs can lead to the design of more robust models. Such models can then be used to design and operate optimal distributed energy systems.
This paper presents a novel methodology for using global sensitivity analysis (GSA) on an operational optimisation-based model of a distributed energy system. The operational model also utilises Model Predictive Control (MPC) rolling horizon concepts (as done by [1]) to determine hourly total operational costs. The paper also addresses how some challenges and limitations encountered in the operational model can be attributed to the deterministic design model on which the structure of the operational model has been based. Furthermore, the research explores how the design can be improved to support more robust operation. Another novel aspect of this paper highlights the use of the optimisation tool GAMS alongside SobolGSA, a global sensitivity analysis software [2]. This software uses the variance-based Sobol method to generate N samples and perform global sensitivity analysis, allowing users to understand how variations in the inputs can influence the outputs, whilst accounting for the different combinations of the uncertain parameters without varying one uncertain parameter at a time.
The energy production landscape is reshaped by distributed energy resources (DERs) – photovoltaic (PV) panels, combined heat and power (CHP), wind turbines (WT), fuel cells or battery storage systems, to name just a few [1]. Microgrids, collections of units or DERs that are locally controlled, close to the consumption point and cooperating with each other and the centralised grid [2], allow for the reduction in energy losses compared to traditional generation due to the close proximity to end users. Due to its volatility, the integration of this non-controllable generation poses severe challenges to the current energy system and ensuring a reliable balance of energy becomes an increasingly demanding task [3]. The optimal design and scheduling of the DERs and subsequent microgrid is of high importance in order to increase the reliability and determine their effectiveness in reducing losses, emissions and costs compared to conventional generation so that they may be implemented at faster rates to reduce global emissions and fossil fuel usage. In distributed energy systems, individual users typically have flexible tariffs while they also have the capability not only to use, but also to store and trade electric power. Direct transactions schemes can save money for end users, generate revenues for producers, reduce transmission losses and promote the use of renewable energy [4]. But it must be a robust, efficient and low-cost trading system to handle the rapid changes of information and value in the system.
The blockchain technology can fulfil these requirements by enabling the implementation of optimal energy management strategies through distributed databases. Since its introduction as the underlying technology of Bitcoin, the blockchain technology has emerged from its use as a verification mechanism for cryptocurrencies and heads to a broader field of applications. Blockchain-based systems are basically a combination of a distributed ledger, a decentralised consensus mechanism, and cryptographic security measures [5]. More precisely, it allows the resolution of conflicts and dismantles information asymmetries by providing transparent and valid records of past transactions that cannot be altered retrospectively [6]. With the help of specific algorithms and applications, multiple operations can be performed automatically on the blockchain, using this information together with information from the Internet or the real world (e.g. on whether, energy pricing, etc.). Furthermore, smart contracts can be implemented between the nodes of the microgrid.
This paper introduces a model for the implementation of a blockchain and smart contracts into the scheduling of a residential DER network. The blockchain is implemented in terms of energy rather than voltages [7], to allow for the decentralised operation of the microgrid without a centralised microgrid aggregator. Thus, the model will minimise only the operational cost. Furthermore, the DER network model is improved by the addition of more detailed transmission losses and costs within the microgrid and between the microgrid and the national grid. The resulted energy flows are stored and information on the availability/demand are exchanged between the network nodes. To appropriately compensate the DER operators in the microgrid for their services and to charge the consumers for withdrawals, nodal clearing prices are determined and implemented through smart contracts. The resulting MILP model minimises the overall investment and operating costs of the system.
Inspiration from nature to solve advanced engineering problems has attracted the interests of engineers, designers and scientists. Biomimetics is to imitate and apply the elements, systems and mechanisms from nature to solve technological challenges as stated by Gleich et al. (2009).
They also added that one of nature’s solution which is being explored is fractal shapes. Fractal shapes appeared in a variety of cases such as snowflakes, blood vessels and plant root systems in nature. Fractal shapes consistently appear in situations which require mass or heat transfer throughout a large space. The optimal spreading and transfer throughout the space characteristics of fractal shapes, making them a practical solution to design more efficient heat and mass transfer devices. Fractal shapes were first employed to improve fluid mechanics designs by West et al. (1997) to minimise the workflow for bulk fluid transportation through a network of branching tubes.
On the other hand, two-phase flow in microscale channels has great applicability due to its diverse range of applications. As expressed by Serizawa et al. (2002), modern and advanced technologies such as micro-electro-mechanical systems, chemical process engineering, medical engineering and electronic cooling utilise multiphase flow in microchannels.
This work aims to investigate the application of nature-inspired fractal geometries as multiphase microscale flow passage using CFD analysis. ANSYS Fluent software has been utilised to investigate the flow characteristics numerically in order to improve the pressure drop and heat transfer. Also, this question will be raised whether two-phase flow patterns in fractal microchannels are different from straight channels or not.