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The design and construction of adaptive façades have garnered increasing attention as a means to enhance the energy performance and sustainability of high-rise residential buildings. Adaptive façades can dynamically respond to environmental conditions, reducing reliance on energy-intensive systems and improving occupant comfort. Despite their potential, research on adaptive façade systems in the context of high-rise residential buildings, particularly in Australia, remains limited. This study aims to bridge this gap by identifying the key design parameters, challenges, and optimisation methods for adaptive façades. Through a combination of a comprehensive literature review and 15 semi-structured interviews with industry experts, this research provides insights into the design and performance of adaptive façades. The key findings reveal that the successful implementation of adaptive façades depends on a range of factors, including material choices, shading system typologies, and advanced simulation tools for energy performance analysis. A significant outcome of the study is the development of a conceptual framework that incorporates these design elements with environmental factors and building energy simulation, offering a structured approach to optimise adaptive façade performance. The framework assists architects and engineers in creating energy-efficient, sustainable high-rise residential buildings tailored to the Australian context. Additionally, the study highlights critical challenges, such as financial barriers, regulatory gaps, and the need for improved maintenance strategies, which must be addressed to facilitate the broader adoption of adaptive façades in the residential sector.

In recent decades, works have been published on the Hybrid Renewable Energy System (HRES) to provide available, feasible, and efficient renewable energy systems. Several studies have looked at the efficiency of the systems in terms of sustainability through performance parameters. This study aims at estimating the optimum HRES based on biomass and photovoltaic (PV) using the case study of 94 residential buildings with an electricity demand of 84.5 kWp. The influence of key parameters (global solar irradiation, component efficiencies, fuel consumption, economic convenience) and their impact on the performance and cost of the system is investigated. The optimum system is evaluated by the simulation software HOMER Pro. A single year of hourly data is used to analyze the component performance and the overall system performance. In this work, a mathematical model based on the IEC 61724 standard is used to incorporate numerous performance indicators that are critical for estimating the performance of a hybrid system. Evaluating results comprise of three performance basic indicators, namely, energy efficiency, system sizing, and economic parameters.

PurposeSynthetic ammonia is not only the basis of the fertilizer industry in China but also has the highest energy consumption and pollution emissions in the chemical industry. The objective of this study was to conduct a cradle-to-gate life cycle assessment (LCA) of ammonia production based on different raw materials to identify the crucial processes and parameters and to provide suggestions for clean and sustainable development of the ammonia industry in China.MethodsBased on actual industrial data, this study comprehensively evaluated the resource consumption and pollution emissions caused by different raw material routes and coal-to-ammonia technologies from a life cycle perspective according to the LCA standards ISO 14040 series and using the CML 2001 method and identified the key environmental impact categories and stage contributions. In addition, the effects of various input parameters on the environmental burden were specified through sensitivity analysis. Accordingly, suggestions for improving the environmental performance of ammonia production are proposed.ResultsThe environmental burdens of the coal-based and coke oven gas-based routes were 1.43 and 1.7 times higher than that of the natural gas-based route, respectively. The significant differences were mainly reflected in the greenhouse effect, acidification, and fossil energy depletion. Advanced coal-to-ammonia technology, represented by coal water slurry gasification, showed a lower environmental burden than the traditional intermittent gasification technology, especially in terms of greenhouse gas (GHG) emissions and energy consumption. The GHG emissions involved in producing ammonia decreased from 3.88 to 2.18 kg per 1 kg of ammonia, and energy consumption decreased by approximately 17%, from 5.69 to 4.71 MJ.ConclusionsCoal is the main raw material used for ammonia production in China, and the results showed that the application of advanced coal gasification with energy-saving technologies can effectively improve the environmental performance of synthetic ammonia production in China.

Currently, there is great interest in producing thermal energy (heat) from renewable sources and storing this energy in a suitable system. The use of a latent heat storage (LHS) system using a phase change material (PCM) is a very efficient storage means (medium) and offers the advantages of high volumetric energy storage capacity and the quasi-isothermal nature of the storage process. In recent years, phase change materials (PCMs) have become an interesting research area due to their advantages especially in thermal energy storage (TES). Indeed, there are a large number of PCMs that melt and solidify over a wide temperature range, making them interesting thermal energy storage media in several applications. In the literature, research on PCMs and their associated applications has attracted and still attracts great interest from various researchers and scientists. Most of the research studies on phase change materials (PCMs) have been generally devoted to the development of PCM-based energy storage technologies, the promotion of PCM-based renewable energy sources, and the encouragement of sustainable/profitable (economic) use of PCM-based energy. In order to get an overview of current progress and trends, to highlight research and to identify gaps, from the literature reviews undertaken on this research topic, it is useful to review the major research studies conducted in this field. Our analysis showed that the literature lacks many comprehensive analyses and studies on the applications of PCMs, the phase transition processes (melting and solidification) of PCMs and the factors that influence these transitions, and in particular the calculation models of the thermal performance parameters of a PCM performing a phase transition and the thermal performance parameters of a PCM-based TES system (referred to as LHS unit). To address these questions, we have presented in this review article a detailed overview of the literature on (a) relevant practical applications of PCMs, (b) characteristics and performances of phase transition processes, (c) major factors influencing PCM transition processes such as geometric design of the PCM tank and its orientation, imposed boundary and operating conditions, thermophysical properties of the material (PCM), and (d) models for calculating thermal performance parameters for a PCM performing a phase transition and for an LHS unit. In addition, several techniques aimed at improving heat transfer in PCMs have been introduced and discussed. The findings indicate that there are three types of PCMs: eutectic, inorganic, and organic. Numerous other industries also use PCMs, such as solar energy (including thermal energy storage through the use of photovoltaic and latent heat systems); buildings; HVAC systems; textiles; the biomedical, food, and agricultural industries; the automotive sector; and desalination. Besides PCMs classification and use, it was found that during phase transitions of PCMs heat transfer is dominated by conduction and natural convection. During melting, conduction heat transfer is dominant in the early stages, and as the PCM melts, natural convection dominates. Unlike melting, solidification is dominated by conductive heat transfer. On the other hand, boundary conditions, material properties, and enclosure configuration and orientation all found having an impact on melting and solidification. In this context, by increasing, for example, thermal conductivity, viscosity, wall-imposed temperature, and PCM initial temperature, as well as by decreasing PCM latent heat of melting, PCM melting point, and PCM system orientation, the melting process rate increases. However, by increasing thermal conductivity, viscosity, melting point, and PCM system orientation, as well as by lowering the latent heat of melting, the initial PCM temperature, and the imposed wall temperature, the solidification process rate increases. Lastly, introducing external fields and adding high thermal conductivity additives like fins, metal foam, and nanoparticles can greatly increase the rate at which PCM melts and solidifies.

Rock abrasivity plays an important role in the machine design, construction scheduling, and budgeting of TBM projects. Establishing several faster and simpler estimation equations for the Cerchar Abrasivity Index (CAI) of rocks is, therefore, very important. This study investigated the correlation between the CAI and mechanical properties of rock, rock mass classification parameters, and machine performance. A TBM construction database including 159 tunnel sections is established. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis (0.66 < R2 < 0.76). In this process, a normalized specific energy is proposed to evaluate the machine performance. The results show that the rock compressive strength and brittleness index are the most dependent parameters to explain CAI, and the estimated rock mass strength also indicates a close correlation. In addition, the contribution of a rock mass classification system and machine performance index for estimating CAI cannot be ignored. Finally, the estimation performance of the developed equations is compared and evaluated, and a new method for selecting an optimal model based on ranking is proposed. Since the input parameters of the proposed equations can be readily available at the project planning stage, they are very practical for TBM designers, tunnel designers, and contractors.HighlightsThe correlations between the CAI and rock mechanical properties, rock mass classification parameters, and machine performance are investigated.A normalized specific energy index is proposed for assessing the performance of rocks excavation by TBMs.Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis.A new method for selecting optimal estimation model based on ranking is presented.

In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.

The stochastic fractal search (SFS) algorithm, among population-based metaheuristic automation algorithms, is a robust optimization algorithm for solving optimization problems in different fields of science, inspired by the diffusion feature and natural growth phenomenon seen regularly in random fractals. However, as in population-based optimization algorithms, it is a great challenge to effectively design the selection process in the SFS method. To imitate the selection process in nature effectively and accurately, the dynamic-fitness-distance-balance (dFDB) selection method has been used in the SFS algorithm in six different versions. In this way, the dFDB-SFS algorithm has been developed, which more effectively mimics nature with exploitation, exploration, and balanced search capabilities. Firstly, the performance of the proposed dFDB-SFS algorithm was investigated in CEC 2020 benchmark test functions. Wilcoxon and Friedman statistical analyses of the results obtained from the test functions were made and according to these analysis results, the best version of the proposed approach was determined. Secondly, the performance of the proposed algorithm was investigated in determining photovoltaic module parameters, which is one of the real-world engineering problems. In this article, the dFDB-SFS algorithm uses the root mean square error (RMSE) as the objective function to estimate the unknown parameters of the single diode model (SDM), double diode model (DDM), and PV module models. In terms of a quantitative and qualitative performance evaluation, it reveals that the proposed algorithm provides better results than other proposed algorithms in terms of accuracy and robustness when obtaining PV parameters.

Personalized exoskeleton assistance provides users with the largest improvements in walking speed 1 and energy economy 2 – 4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s −1 . Human movements encode information that can be used to personalize assistive devices and enhance performance. A portable ankle exoskeleton uses a data-driven method and wearable sensors to adapt to the user as they walk in a natural setting.

Background Land use for the conversion of energy from renewable sources into electrical energy is increasingly competing with cultural landscapes and natural areas. It is anticipated that by 2050, solar energy generation will have increased by a factor of 15, which will result in a considerable expansion of the land area required for photovoltaic (PV) power plants on a global scale. An increase in the efficiency of PV modules and an optimisation of the space usage for PV power plant construction will contribute to a reduction in the expected environmental impact on land use. This study represents an empirical investigation into the European development of specific energy and area-relevant key performance indicators of free-field PV power plants. It employs a comprehensive sample drawn from diverse European geographical locations from different installation years. Methods This study investigated the evolution of various location-independent and location-dependent system parameters over time, using a sample of 107 free-field PV power plants across diverse European regions from 2006 to 2022 related to the fenced area. The investigations concentrated on the land use per installed power, land use per module area, land use per generated electrical energy, generated electrical energy per PV module area, energy density, capacity factor, and power density. The determined data provide the first European average life cycle inventory data, disaggregated by year and location, for environmental life cycle assessment. To facilitate a comparison of the system parameters of PV power plants with those of other renewable energy technologies, a further database was employed, including 89 power plants from the biomass, wind power, geothermal energy, solar thermal energy, and photovoltaic sectors. The selected samples were compiled from this database to compare the area-specific energy yields of both data sources. Results The European trends for free-field PV power plants demonstrate a 60% reduction in specific land use per installed power and land use per generated electrical energy over the study period. In 2022, the median values were 14 m 2 /kW and 0.011 m 2 .a/kWh, respectively. The analysis indicates that three significant technological advances have occurred at approximately 5-year intervals. At the mounting design level, the land use per module area for conventional fixed-tilt row systems decreased by 30%. Overall, the mean land usage of all the considered PV power plants is threefold greater than the module area over the entire study period. Likewise, the results show that the high land usage caused by tracking systems is entirely compensated for by a relatively high energy yield, which presents an opportunity to develop innovative designs for multiple-use systems. A comparison of PV power plants with other renewable energy power plants reveals that solar thermal heat is distinctly superior in terms of the energy yield received per unit area. Conclusions To minimise land use, it is recommended that minimum energy efficiency requirements should be defined for new free-field PV power plants in addition to an optimised mounting design within the fenced area. The high energy yield of tracking systems, which have comparatively large row/pole distances, provides the opportunity for multiple uses of the ground area. Furthermore, the discrepancy in energy yield between northern and southern Europe underscores the need for a more comprehensive European planning strategy with regard to the future location of free-field PV power plants. To realise energy transition in the future, it will also be essential to consider all energy potentials together rather than to focus on isolated and small-scale initiatives. The policy changes require Europe-wide coordination, coupled with tailored national and regional definitions. Integrated spatial and energy planning could be a potential avenue for achieving this challenging aim.

The observation-based source terms available in the third-generation wave model WAVEWATCH III (i.e., the ST6 package for parameterizations of wind input, wave breaking, and swell dissipation terms) are recalibrated and verified against a series of academic and realistic simulations, including the fetch/duration-limited test, a Lake Michigan hindcast, and a 1-yr global hindcast. The updated ST6 not only performs well in predicting commonly used bulk wave parameters (e.g., significant wave height and wave period) but also yields a clearly improved estimation of high-frequency energy level (in terms of saturation spectrum and mean square slope). In the duration-limited test, we investigate the modeled wave spectrum in a detailed way by introducing spectral metrics for the tail and the peak of the omnidirectional wave spectrum and for the directionality of the two-dimensional frequency–direction spectrum. The omnidirectional frequency spectrum E ( f ) from the recalibrated ST6 shows a clear transition behavior from a power law of approximately f −4 to a power law of about f −5 , comparable to previous field studies. Different solvers for nonlinear wave interactions are applied with ST6, including the Discrete Interaction Approximation (DIA), the more expensive Generalized Multiple DIA (GMD), and the very expensive exact solutions [using the Webb–Resio–Tracy method (WRT)]. The GMD-simulated E ( f ) is in excellent agreement with that from WRT. Nonetheless, we find the peak of E ( f ) modeled by the GMD and WRT appears too narrow. It is also shown that in the 1-yr global hindcast, the DIA-based model overestimates the low-frequency wave energy (wave period T > 16 s) by 90%. Such model errors are reduced significantly by the GMD to ~20%.