Infrastructure Engineering - Research Publications

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    Integrating asset-specific flood vulnerability assessments with value-based preservation processes to develop the Heritage Building Flood Robustness Toolkit
    Snelling, R ; Rismanchi, B ; Holzer, D (ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER, 2024-03-01)
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    Fifth-generation district heating and cooling: Opportunities and implementation challenges in a mild climate
    Gjoka, K ; Rismanchi, B ; Crawford, RH (Elsevier, 2024-01-01)
    Fifth-generation district heating and cooling (5GDHC) systems have the potential to provide simultaneous heating and cooling, allowing for energy exchange between users with different needs. However, their viability in mild climates with a higher share of cooling demand remains unclear. In this paper, we propose a framework for assessing the engineering, economic and environmental performance of a 5GDHC system compared to a state-of-the-art business-as-usual solution and demonstrate it through a practical case study for a university campus in Melbourne, Australia. When accessible heat sources and sinks are available, the 5GDHC system provides a cost-effective solution, with annual cost savings between 9 and 29 % and GHG emissions reduction between 25 and 58 % compared to an already advanced business-as-usual system. Additionally, by using peak off-peak tariffs and an hourly emission factor for the electricity consumed, we demonstrate the 5GDHC operational flexibility in pursuing different objectives, such as minimising cost or emissions, respectively. The results suggest that 5GDHC systems are an economically and environmentally viable solution in milder climates, and a successful implementation of 5GDHC in Australia can create new market opportunities and pave the way for its adoption in other countries with similar climatic conditions and no established history of district heating systems.
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    Optimal design of micro pumped-storage plants in the heart of a city
    Boroomandnia, A ; Rismanchi, B ; Wu, W ; Anderson, R (ELSEVIER, 2024-02)
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    Water distribution system design integrating behind-the-meter solar under long-term uncertainty
    Yao, J ; Wu, W ; Simpson, AR ; Rismanchi, B (ELSEVIER, 2023-11)
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    Balcony design and surrounding constructions effects on natural ventilation performance and thermal comfort using CFD simulation: a case study
    Izadyar, N ; Miller, W ; Rismanchi, B ; Garcia-Hansen, V ; Matour, S (TAYLOR & FRANCIS LTD, 2023-09-03)
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    Efficient HVAC system identification using Koopman operator and machine learning for thermal comfort optimisation
    Wahba, N ; Rismanchi, B ; Pu, Y ; Aye, L (Elsevier BV, 2023-08-15)
    The aim of this article is to improve the efficiency of heating, ventilation, and air conditioning (HVAC) systems by using a linear control approach. Conventional HVAC systems use a wall thermostat and a simplified ON/OFF controller to condition the thermal environment, but this approach is not always efficient in meeting indoor heat loads. To address this issue, we propose using the Koopman operator combined with Machine Learning, a linear embedding method, to model the nonlinear behaviour of thermal comfort indices. Specifically, we use the Predictive Mean Vote (PMV) index, which has been a superior indicator of occupants’ thermal sensation. We apply Computational Fluid Dynamics to create high-dimensional training, testing, and validation datasets, and a deep autoencoder network framework to map the original nonlinear coordinates of the PMV index into a latent space where the system is behaving linearly. Our results show that the Koopman autoencoder can reproduce and predict data from the latent space, enabling offline system identification for the zone thermal conditions and this has the potential to improve HVAC feedback control systems.
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    A review of micro hydro systems in urban areas: Opportunities and challenges
    Boroomandnia, A ; Rismanchi, B ; Wu, W (PERGAMON-ELSEVIER SCIENCE LTD, 2022-11)
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    Thermal and energy performance evaluation of a full-scale test cabin equipped with PCM embedded radiant chilled ceiling
    Mousavi, S ; Rismanchi, B ; Brey, S ; Aye, L (Elsevier BV, 2023-06-01)
    The escalating global demand for space cooling has led to the emergence of new cooling technologies, including the phase change material embedded radiant chilled ceiling (PCM-RCC) system. This technology improves energy efficiency and indoor environmental quality, while also offering demand-side flexibility. The present study experimentally evaluates the thermal efficiency and energy performance of a PCM-RCC system in a full-scale test cabin equipped with PCM panels. Here, the transient thermal behaviour of PCM ceiling panels besides the cooling energy delivered during charging-discharging cycles are examined. The indoor thermal comfort and peak electricity demand reduction enabled by the present PCM-RCC are also discussed. The results reveal that chilled water circulation for 4–5 h overnight was sufficient to fully recharge the PCM panels. Over 80% of the occupancy time was classified as “Class B″ thermal comfort according to ISO 7730. The system's daily electricity usage was mostly concentrated during off-peak hours, accounting for ∼70% of the total usage. While the controlling schedule used in this study responded to the transient thermal behaviour of the indoor space and PCM ceiling panels, a more dynamic, predictive schedule is necessary to improve the system's overall efficiency and further enhance indoor thermal comfort in response to the changing environmental conditions.
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    A Technology Assessment Approach for Achieving Sustainable Communities: An Energy Master Plan for a New Urban Development
    Santillan, MR ; Syn, JW ; Shandiz, SC ; Huang, Y ; de Lacerda, MP ; Rismanchi, B (MDPI, 2022-04)
    In the era of climate change and rapid urbanisation, communities and infrastructures need to be planned and designed in a way that promotes sustainable living. The provision of clean and affordable energy is a key to this aim. This paper proposes a technology assessment approach that is based on the triple bottom line (environmental, social and economic) sustainability framework. This approach can be employed in the technology screening that is involved in the early stages of the energy master planning process and can be applied to different community typologies in various locations and climates. The developed approach is demonstrated through a new urban renewal project case study in Fishermans Bend, Melbourne, in which a set of technological options were screened according to the project’s goals. The connection between the energy master plan and local and global sustainable development goals is discussed and policy interventions are proposed. The results show that the proposed approach could effectively enable the evaluation of the technological sustainability performance of the community by demonstrating the design trade-offs and the implementation of the sustainability objectives during the energy master planning process. Moreover, the proposed approach could provide guidance for effective policy making. It was found that government energy policies, regulations and incentives play a vital role in the feasibility of an energy master plan. Lastly, the proposed approach could facilitate the achievement of local and international targets, such as the UN SDGs, by 2050.
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    Application of Artificial Neural Networks for Virtual Energy Assessment
    Mortazavigazar, A ; Wahba, N ; Newsham, P ; Triharta, M ; Zheng, P ; Chen, T ; Rismanchi, B (MDPI, 2021-12)
    A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions.