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Thermal comfort is an important concern in the energy efficiency improvement of commercial buildings. Thermal comfort research focuses mostly on temperature control, but humidity control is an important aspect to maintain indoor comfort too. In this paper, an optimal humidity control model (OHCM) is presented. Model predictive control (MPC) strategy is applied to implement the optimal operation of the desiccant wheel during working hours of a commercial building. The OHCM is revised to apply the MPC strategy. A case is studied to illustrate the practical applications of the MPC strategy.

Abstract This paper empirically evaluates the extent of energy resilience achieved in a socially-deprived community in Oxford, through deployment of solar photovoltaic (PV) systems and smart batteries (internet enabled and controllable) across a cluster of 82 dwellings (households). The methodological approach comprised dwelling and household surveys, along with high frequency monitoring of household electricity consumption, solar PV generation, battery charge and discharge data. In the monitored households, average daily electricity consumption was found to be positively related with dwelling size, number of occupants and number of appliances used. Although 117 MWh of PV electricity was generated within a year across 74 dwellings, peak generation did not match peak consumption, demonstrating the need for battery storage. Home batteries were found to increase self-consumption of PV electricity and offset grid demand through discharge of stored PV electricity marginally at an average of 6%, depending on the size of the PV system, surplus PV electricity available and size of the battery. Aggregating solar generation and storage at a community level showed that peak grid electricity demand between 17:00 and 19:00 was reduced by 8% through the use of smart batteries across 74 dwellings. In future, a local energy sharing scheme could be developed, wherein not all dwellings would need to have solar PV systems, but rather have internet enabled batteries that could be monitored and controlled virtually.

Abstract In this study, soil temperatures at different depths in Turkey's different regions were investigated theoretically. Soil temperature data are critical for different research interests such as ecology, biology, technique processes, forestry, agriculture, energy, food sector, ground heat exchanger applications, thermal energy storage applications, and so forth. This investigation gives information related to the prediction of soil temperature's dependence with depth and time especially for shallow geothermal applications. Soil temperature values depend on a great deal of varied parameters such as thermal conductivity, short term climatic conditions and moisture content. The main issue is that despite these temperatures are extremely important values, they can not be obtained in a short time. Due to this reason, we study a mathematical model related to the prediction of soil temperature. Within this context, 81 cities and their approximately 300.000 data, both, monthly air and soil temperatures between 1960 and 2015 were studied and finally seven regions in Turkey were investigated and final average soil temperature values were achieved. Measured data taken from the Izmir State Meteorological Station, and predicted soil temperatures at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm were analyzed for each region in Turkey according to data obtained fifty years ago. Finally, at depths of 5 cm, 10 cm, 20 cm, 50 cm and 100 cm, the maximum average percentage errors in Turkey were 16%, 14.8%, 13.5%, 14.4%, 13.9% respectively. In conclusion, we evaluate the relationship between ambient air temperatures and soil temperatures in terms of depths from 5 to 3000 cm.

Twenty-minute neighbourhoods highlight the importance of well-connected and mixed-used neighbourhoods and communities with proximate access to employment, essential services, public transport, and open spaces. Shorter distances together with re-prioritised public spaces encourage more active transport choices, resulting in public health benefits and reduced environmental pollution. Higher liveability brought about by mixed-use developments enables people to have equitable access to local facilities, amenities, and employment opportunities, promoting vibrancy, social cohesion, and intergenerational connections. The attributes of 20-minute neighbourhoods also combine to create places, that are acknowledged as friendly for all ages, address changing needs across the life course, and provide better support for the ageing population. Furthermore, there are indications that 20-minute neighbourhoods may be more resilient against many of the negative impacts of stringent public health protocols such as those implemented in periods of lockdown during the Covid-19 pandemic. In this article, we evaluate and compare planning policies and practices aimed at establishing 20-minute neighbourhoods in Melbourne (Australia) and Scotland (the UK). Using case studies, we discuss similarities and differences involved in using place-based approaches of 20-minute neighbourhoods to address 21st-century challenges in key areas of health and wellbeing, equity, environmental sustainability, and community resilience.

The blade geometric variations are usually ignored in the prediction of bladed disk aerodynamic damping values. This situation is the result of the high computational cost associated with the full-annulus unsteady flow CFD models required to account for these blade geometric differences. This paper presents an approach that can account for these geometric differences with high fidelity and at a reasonable one-time cost. The approach is based on the use of the influence-coefficient (IC) method together with a set of sensitivity coefficients defined for the blade geometry changing effects. The sensitivity coefficients make use of a set of principal component analysis (PCA) modes that describe the measured blade geometry variation. Once the sensitivity coefficients are determined, they are used to construct the IC matrices and to predict the aerodynamic damping values associated with the geometrically mistuned disk. The currently proposed method is unique in two aspects. The first is to follow the observed physics while making assumptions in the linearization process to reduce the number of required sensitivity coefficients. The second is to construct the multiblade CFD model, with blades of different geometries, in a unique way to reduce the data generation costs. Two IC approximation formulas were developed. If NP denotes the number of PCA modes used to describe the blade geometric variation, one formula reduces the number of required multiblade unsteady CFD models from (NP +1) [3] to (NP +1) [2], the other reduces the number of CFD models from (NP +1) [2] to NP +1. Results obtained from these two formulas are compared and validated.

The combustion of coal is responsible for nearly 40% of the world's electricity production, and char combustion accounts for about half of that amount. Clearly, an understanding of the combustion mechanism of carbon is of great importance not only because of its industrial significance but because it is a model heterogeneous reaction. A number of recent studies have been concerned with ab initio molecular orbital calculations on graphite including model chemistry and the reactions with molecular oxygen. This study is concerned with oxidation steps involving the attachment of oxygen to a graphene layer at high temperature leading to the formation of carbon monoxide, and particular attention is paid to the subsequent oxidation reactions. In addition, the reaction of oxygen with carbon catalyzed by metals inherent within the char matrix and the reaction of molecular oxygen with the analogous biomass char are investigated and their reaction paths are discussed.

Dokan reservoir is located on the Lesser Zab River NE Iraq. Its drainage area is 11,690 km2 with a storage capacity of 6.87 × 109 m3. Calculation of the volume of the reservoir before the construction of the dam and from the bathymetric survey conducted in 2014 indicates that an annual average of 7 million cubic meters of sediments are deposited within the reservoir. This reduced the storage capacity of the reservoir by 28%.

The operation of centrifugal pumps can generate instabilities and pressure pulsations that may be detrimental to the integrity and performance of the pump. In the present study a numerical investigation of the time variation of pressure within a complete centrifugal pump was undertaken. A range of parameters and three flow rates were investigated and the pulsations were extracted at 15 different locations covering important pump regions. The transient flow results compared reasonably with experimental data obtained in a limited experimental survey and clearly indicated the pump locations experiencing the largest pulsation levels. It was also noted that monitoring pulsations at the top dead centre of the pump volute casing would provide a better indication of internal pump pulsations than monitoring at the discharge.

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-10⁶ plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.

Abstract. Wake steering is a technique that optimizes the energy production of a wind farm by employing yaw control to misalign upstream turbines with the incoming wind direction. This work highlights the important dependence between wind direction variations and wake steering optimization. The problem is formalized over time as the succession of multiple steady-state yaw control problems interconnected by the rotational constraints of the turbines and the evolution of the wind. Then, this work proposes a reformulation of the yaw optimization problem of each time step by augmenting the objective function by a new heuristic based on a wind prediction. The heuristic acts as a penalization for the optimization, encouraging solutions that will guarantee future energy production. Finally, a synthetic sensitivity analysis of the wind direction variations and wake steering optimization is conducted. Because of the rotational constraints of the turbines, as the magnitude of the wind direction fluctuations increases, the importance of considering wind prediction in a steady-state optimization is empirically demonstrated. The heuristic proposed in this work greatly improves the performance of controllers and significantly reduces the complexity of the original sequential decision problem by decreasing the number of decision variables.