• Energy Research
• 2021-2025close
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Turbulence and pressure fluctuations are key elements in the bed protection design of hydraulic structures. However, their roles in a hydraulic jump are not yet fully understood, and the nature of their relationships are not conclusive. In order to better understand the relationships between pressure fluctuations and flow characteristics of a hydraulic jump downstream of a weir, detailed measurements of flow kinematics using the nonintrusive techniques of particle image velocimetry and bubble image velocimetry and pressure using voltage-type pressure gauges were carried out in this study. The physical modeling of the hydraulic jump was carried out using the simultaneous measurements of pressure and turbulent flow properties. The distributions of flow properties, such as water level and velocity, were assessed in each case. Based on the measurements, the correlations between the pressure fluctuations and the variables were investigated by coupling the statistical values of the variables at the same points. The analysis results show that the water level and turbulence intensity are the main factors influencing the pressure fluctuations in the hydraulic jump. Using these factors, an empirical formula and dimensionless numbers are proposed to show that the pressure fluctuations depend on the bubble flow behavior.

Global wind resources greatly exceed current electricity demand and the levelized cost of energy from wind turbines has shown precipitous declines. Accordingly, the installed capacity of wind turbines grew at an annualized rate of about 14% during the last two decades and wind turbines now provide ~6–7% of the global electricity supply. This renewable electricity generation source is thus already playing a role in reducing greenhouse gas emissions from the energy sector. Here we document trends within the industry, examine projections of future installed capacity increases and compute the associated climate change mitigation potential at the global and regional levels. Key countries (the USA, UK and China) and regions (e.g., EU27) have developed ambitious plans to expand wind energy penetration as core aspects of their net-zero emissions strategies. The projected climate change mitigation from wind energy by 2100 ranges from 0.3–0.8 °C depending on the precise socio-economic pathway and wind energy expansion scenario followed. The rapid expansion of annual increments to wind energy installed capacity by approximately two times current rates can greatly delay the passing of the 2 °C warming threshold relative to pre-industrial levels. To achieve the required expansion of this cost-effective, low-carbon energy source, there is a need for electrification of the energy system and for expansion of manufacturing and installation capacity.

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Glacier retreat is evident on all continents, and the rate of retreat has accelerated during recent decades. Accurate, spatially explicit information on the position of glacier margins over time is useful for analyzing patterns of glacier retreat and measuring reductions in glacier surface area. This information is also essential for evaluating how mountain ecosystems are evolving due to climate warming and the attendant glacier retreat. Here, we present a non-comprehensive spatially explicit dataset showing multiple positions of glacier fronts since the Little Ice Age (LIA) maxima, including many data from the pre-satellite era. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings, and aerial or satellite images with a supplement of geochronology; and own field data. We provide ESRI shapefiles showing 728 past positions of 94 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8).

The level of advancement in the understanding of the mechanical properties of volcanic rocks is comparatively lower than that of sedimentary rocks. As part of the SUCCEED Project (Synergetic Utilisation of CO2 Storage Coupled with Geothermal Energy Deployment), which aims to investigate the feasibility of injecting captured and produced CO2 into the reservoirs to enhance geothermal production and achieve permanent CO2 storage at the Hellisheiði Geothermal Field in Iceland, this experimental research provides significant insights into the petrophysical and mechanical properties of the volcanic rocks collected from surface outcrops. The subsurface in Hellisheiði is mainly built up of hyaloclastite formations and interglacial basaltic lavas. During a field campaign samples were collected in different outcrops, ensuring that the samples were of high quality and sufficiently diverse to enable comprehensive analysis. Four samples per block and rock type have been prepared from the collected blocks, and they have been subjected to different laboratory tests to evaluate their petrophysical properties, such as porosity, density, and permeability, and their geomechanical behavior, using Unconfined Compression Test (UCS), Active-Source Acoustic Test, and Splitting Tensile Strength Test. Additionally, laboratory experiments have been conducted to investigate the impact of rapid cooling on rock damage due to thermal fracturing. The results show that there are interdependent relationships between porosity, bulk density, ultimate strength, Young's modulus, and wave velocities that can be observed when considering average values per rock. The rocks studied showed a negative correlation between porosity and other parameters and a direct correlation between ultimate strength and Young's Modulus. When examining individual rock samples, no significant correlations were observed between porosity and other parameters, however, those correlations where evident when comparing between different rock types, emphasizing the importance of ...

Abstract The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

AbstractThe adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (Brassica carinata) among the farmers in the Little River Experimental Watershed located in the southern state of Georgia in the United States. Each farmer's adoption behavior is modeled using the profitability difference between traditional crop rotations (with and without carinata at different contract prices), the adoption rate of neighboring farmers, and their land allocation decisions from managing a risky portfolio of enterprises. Carinata production in the winter season once every 3 years has no conflict with the most profitable and popular traditional row crop rotations, such as cotton‐cotton‐cotton and cotton‐cotton‐peanut, to a larger extent. The results show that 28% and 85% of farmers in the watershed will adopt carinata after 33 years at a contract price of $13/bushel (bu) under two different assumptions of low (2.5%) and high (5%) initial neighborhood adoption rates. The proportions of land allocated to carinata to the total farmland under field crops are 38% and 85% after 33 years under the same low and high neighborhood adoption rates, respectively. Our results suggest that fixing the appropriate contract price of carinata will bring additional profits to farmers without any significant foreseeable agronomic risks, thereby increasing the adoption rate of carinata at a regional level.