Search results

This study has assessed the thermal performance of locally fabricated bio-based building envelopes made of coconut and corn husk composite bricks to reduce building wall heat transmission load and energy consumption towards green building adaptation.

Samples of coconut fiber (coir) and corn husk fiber bricks were fabricated and tested for their thermophysical properties using the Transient Plane Source (TPS) 2500s instrument. A simulation was conducted using Dynamic Energy Response of Building - Lunds Tekniska Hogskola (DEROB-LTH) to determine indoor temperature variation over 24 h. The time lag and decrement factor, two important parameters in evaluating building envelopes, were also determined.

The time lag of the bio-based composite building envelope was found to be in the range of 4.2–4.6 h for 100 mm thickness block and 10.64–11.5 h for 200 mm thickness block. The decrement factor was also determined to be in the range of 0.87–0.88. The bio-based composite building envelopes were able to maintain the indoor temperature of the model from 25.4 to 27.4 °C, providing a closely stable indoor thermal comfort despite varying outdoor temperatures. The temperature variation in 24 h, was very stable for about 8 h before a degree increment, providing a comfortable indoor temperature for occupants and the need not to rely on air conditions and other mechanical forms of cooling. Potential energy savings also peaked at 529.14 kWh per year.

The findings of this study present opportunities to building developers and engineers in terms of selecting vernacular materials for building envelopes towards green building adaptation, energy savings, reduced construction costs and job creation.

This study presents for the first time, time lag and decrement factor for bio-based composite building envelopes for green building adaptation in hot climates, as found in Ghana.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

This study investigates the impact of the fire decay phase on structural damage using the sectional analysis method. The primary objective of this work is to forecast the non-dimensional capacity parameters for the axial and flexural load-carrying capacity of reinforced concrete (RC) sections for heating and the subsequent post-heating phase (decay phase) of the fire.

The sectional analysis method is used to determine the moment and axial capacities. The findings of sectional analysis and heat transfer for the heating stage are initially validated, and the analysis subsequently proceeds to determine the load capacity during the fire’s heating and decay phases by appropriately incorporating non-dimensional sectional and material parameters. The numerical analysis includes four fire curves with different cooling rates and steel percentages.

The study’s findings indicate that the rate at which the cooling process occurs after undergoing heating substantially impacts the axial and flexural capacity. The maximum degradation in axial and flexural capacity occurred in the range of 15–20% for cooling rates of 3 °C/min and 5 °C/min as compared to the capacity obtained at 120 min of heating for all steel percentages. As the fire cooling rate reduced to 1 °C/min, the highest deterioration in axial and flexural capacity reached 48–50% and 42–46%, respectively, in the post-heating stage.

The established non-dimensional parameters for axial and flexural capacity are limited to the analysed section in the study owing to the thermal profile, however, this can be modified depending on the section geometry and fire scenario.

The study primarily focusses on the degradation of axial and flexural capacity at various time intervals during the entire fire exposure, including heating and cooling. The findings obtained showed that following the completion of the fire’s heating phase, the structural capacity continued to decrease over the subsequent post-heating period. It is recommended that structural members' fire resistance designs encompass both the heating and cooling phases of a fire. Since the capacity degradation varies with fire duration, the conventional method is inadequate to design the load capacity for appropriate fire safety. Therefore, it is essential to adopt a performance-based approach while designing structural elements' capacity for the desired fire resistance rating. The proposed technique of using non-dimensional parameters will effectively support predicting the load capacity for required fire resistance.

The fire-resistant requirements for reinforced concrete structures are generally established based on standard fire exposure conditions, which account for the fire growth phase. However, it is important to note that concrete structures can experience internal damage over time during the decay phase of fires, which can be quantitatively determined using the proposed non-dimensional parameter approach.

The Heating, Ventilation and Air Conditioning (HVAC) system is a significant energy consumer in built environments, and the building energy consumption could be minimised by optimising HVAC controls. Hence, this paper aims to investigate the applicability of Variable Refrigerant Flow (VRF) air conditioning systems for optimising the indoor comfort of buildings in Sri Lanka.

To address the research aim, the quantitative approach following the survey research strategy was deployed. Data collected through questionnaires were analysed using descriptive statistical tools, including Mean Rating (MR), Relative Important Index (RII) and Standard Deviation (SD).

The findings revealed that VRF systems are popularly used in Sri Lankan apartment buildings. Furthermore, energy efficiency and comfort were recognised as the most significant top-ranked benefits, while ventilation issues and initial cost were recognised as significant challenges. Moreover, the allocation of trained technicians and provision of proper ventilation through a Dedicated Outdoor Air System (DOAS) were highlighted as applicable mitigation strategies for the identified challenges in VRFs.

The study recommends VRF systems as a suitable technology to ensure energy efficiency, reduce GHG emissions and achieve climate performance within the built environment. The opportunities for adopting VRF systems for developing countries could be explored based on the research findings. The identified challenges would assist the design engineers and facilities professionals to devise suitable strategies to mitigate issues of VRF systems in developing countries.

This research provides empirical proof of the energy efficiency and comfort aspects of VRFs. The study has explored and recommended VRF technology as a beneficial application to overcome the persistent energy crisis in developing countries.

The aim is to holistically assess the environmental performance of windows and analyse how their design and characteristics contribute to the overall performance of the building/space. This study focuses on the performance of windows in patient rooms hosting less mobile people.

This study investigates the life cycle environmental impacts of different glazing types, window frames and fire safety doors at the product level. This article also presents a building-integrated environmental analysis of patient rooms that considers the multiple functionalities of windows by incorporating dynamic energy analysis, comfort and daylighting performance with a life cycle assessment (LCA) study.

The results indicate that the amount of flat glass is the main contributor to the environmental impacts of the glazing units. As for the patient rooms, global warming shows the most significant contribution to the environmental costs, followed by human toxicity, particulate matter formation and eutrophication. The key drivers for these impacts are production processes and operational energy use. This study highlights the significance of evaluating a wide range of criteria for assessing the performance of windows.

An integrated assessment approach is used to investigate the influence of windows on environmental performance by considering the link between window/design parameters and their effects on energy use/costs, daylighting, comfort and environmental impacts. The embodied impacts of different building elements and the influence of various design parameters on environmental performance are assessed and compared. The environmental costs are expressed as an external environmental cost (euro).

Living walls (LWs), vegetated walls with an integrated growth layer behind, are being increasingly incorporated in buildings. Examining plant characteristics’ comparative impacts on LWs’ energy efficiency-related thermal behavior was aimed, considering that studies on their relative effects are limited. LWs of varying leaf albedo, leaf transmittance and leaf area index (LAI) were studied for Antalya, Turkey for typical days of four seasons.

Dynamic simulations run by Envi-met were used to assess the plant characteristics’ influence on seasonal and orientation-based heat fluxes. After model calibration, a sensitivity analysis was conducted through 112 simulations. The minimum, mean and maximum values were investigated for each plant characteristic. Energy need (regardless of orientation), temperature and heat flux results were compared among different scenarios, including a building without LW, to evaluate energy efficiency and variables’ impacts.

LWs reduced annual energy consumption in Antalya, despite increasing energy needs in winter. South and west facades were particularly advantageous for energy efficiency. The impacts of leaf albedo and transmittance were more significant (44–46%) than LAI (10%) in determining LWs’ effectiveness. The changes in plant characteristics changed the energy needs up to ca 1%.

This study can potentially contribute to generating guiding principles for architects considering LW use in their designs in hot-humid climates.

The plant characteristics’ relative impacts on energy efficiency, which cannot be easily determined by experimental studies, were examined using parametric simulation results regarding three plant characteristics.

Determining the suitable material and accurate thickness of the thermal insulation layer used in exterior walls during the design phase of a building can be challenging. This study aims to determine suitable material and optimum thickness for the insulation layer considering both operational and embodied factors by a comprehensive assessment of the energy, economic and environmental (3E) parameters.

First, the energy model of an existing building was created by using Autodesk Revit software according to the as-built floor layout to evaluate the impact of five alternative insulating materials in varying thickness values. Second, using the results derived from the model, a thorough evaluation was conducted to ascertain the optimal insulation material and thickness through individual analysis of 3E factors, followed by a comprehensive analysis considering the three aforementioned factors simultaneously.

The findings indicated that polyurethane with 13 cm thickness, rockwool with 10 cm thickness and EPS with 20 cm thickness were the best states based on energy consumption, cost and environmental footprint, respectively. After completing the 3E investigation, the 15-cm-thick mineral wool insulation was presented as the ideal state.

This study explores how suitable material and thickness of insulating material can be determined in advance during the design phase of a building, which is a lot more accurate and cost-effective than applying insulating materials by assumed thickness in the construction phase.

To the best of the authors’ knowledge, this paper is unique in investigating the advantages of using thermally insulating materials in the context of a mosque structure, taking into account its distinctive attributes that deviate from those of typical buildings. Furthermore, there has been no prior analysis of the cost and sustainability implications of these materials concerning the characteristics of subtropical monsoon climate.

Given the distinct and unique climates in these countries, research conducted in other parts of the world may not be directly applicable. Therefore, it is crucial to conduct research tailored to the specific climatic conditions of Australia and New Zealand to ensure accuracy and relevance.

Given population growth, urban expansions and predicted climate change, researchers should provide a deeper understanding of microclimatic conditions and outdoor thermal comfort in Australia and New Zealand. The study’s objectives can be classified into three categories: (1) to analyze previous research works on urban microclimate and outdoor thermal comfort in Australia and New Zealand; (2) to highlight the gaps in urban microclimate studies and (3) to provide a summary of recommendations for the neglected but critical aspects of urban microclimate.

The findings of this study indicate that, despite the various climate challenges in these countries, there has been limited investigation. According to the selected papers, Melbourne has the highest number of microclimatic studies among various cities. It is a significant area for past researchers to examine people’s thermal perceptions in residential areas during the summer through field measurements and surveys. An obvious gap in previous research is investigating the impacts of various urban contexts on microclimatic conditions through software simulations over the course of a year and considering the predicted future climate changes in these countries.

This paper aims to review existing studies in these countries, provide a foundation for future research, identify research gaps and highlight areas requiring further investigation.

From May 2024, Victoria (Australia) will mandatorily raise the minimum house energy rating standards from 6 to 7 stars. However, the latest data shows that only 5.73% of new Victorian houses were designed beyond 7-star. While previous literature indicates the issue’s link to the compliance behaviour of building practitioners in the design phase, the underlying behavioural determinants are rarely explored. This study thus preliminarily examines building practitioners’ compliance behaviour with 7-star Australian house energy ratings and beyond.

Using a widely-applied method to initially examine an under-explored phenomenon, eight expert interviews were conducted with building practitioners, a state-level industry regulator and a leading national building energy policy researcher. The study triangulated the data with government-led research reports.

The experts indicate that most building practitioners involved in mainstream volume projects do not go for 7 stars, mainly due to perceived compliance costs and reliance on standardized designs. In contrast, those who work on custom projects are more willing to go beyond 7-star mostly due to the moral norms for a low-carbon environment. The experts further agree that four behavioural determinants (attitudes towards compliance, subjective norms, perceived behavioural control and personal norms) co-shape building practitioners’ compliance behaviour. Interventions targeting these behavioural determinants are recommended for achieving 7 stars and beyond.

This study demonstrates the behavioural determinants that influence building practitioners’ compliance decisions, and offers insight regarding how far they will go to meet 7 stars. It can facilitate the transition to 7 stars by informing policymakers of customized interventions to trigger behaviour change.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.