Large Eddy Simulation of Bubble Column Bubbly Flow Considering Subgrid-Scale Turbulent Diffusion Effects and Bubble Oscillation

Through Euler/Euler large eddy simulation (LES) modeling, it is demonstrated that turbulent dispersion of bubbles can effectively indicate the impact of turbulent eddies on the bubble dynamics, i.e., the bubble oscillation behavior. This finding builds on previous work using the Euler/Lagrange LES modeling approach and leads to a significant improvement in predicting bubble lateral dispersion. Spatially filtered terms were proposed for the subgrid-scale (SGS) turbulent dispersion and added mass stress force models, with a modification made to the SGS eddy viscosity to reflect bubble turbulent dispersion and oscillations. The proposed model substantially improves the prediction of bubble volume fraction distribution, bubble and liquid phase velocity profiles, the turbulent kinetic energy spectrum, and mass transfer

Outlook for the coal industry and new coal production technologies

Historically, energy resources have evolved from high carbon to lower carbon fuels (from coal to oil to natural gas), then to non-carbon (hydroelectric, geothermal, wind power and solar). This dynamic process has reflected the evolution of human civilization and industrialization. As one of the most useful and classical energy resources, what is the outlook for the coal industry in the future? What factors will have a great impact on the outlook? Can new technologies in coal production make the coal industry cleaner and more competitive and increase its demand in the world market? How effective are CO2 capture technologies for coal power plants? This editorial work attempts to provide insights into these issues.1. The future outlook for the coal industryLimiting global warming to 2 ◦C versus pre-industrial levels would imply reducing carbon dioxide (CO2) emissions by 80% of the 1990 level by 2050 and a net-zero emission by the end of this century. To make this happen requires a fast transition of traditional fossil fuels to renewable energy such as wind and solar. In this framework, coal demand is expected to decline by about 8% by 2030 compared to the pre-crisis level in 2019. Advanced economies will cut their demand by 45% compared to 2019. China is still the largest consumer and producer, and the coal usage in China is expected to rebound in the near term and achieve its peak around 2025 followed by a gradual decline after 2025. In the Asia Pacific area, India, Indonesia and Southeast Asian countries will increase their coal demand for power and industrial usage in the next decade (IEA, 2020). By 2030 the global coal demand is projected to decrease by about 400 million tonnes of coal equivalent compared to 2019.2. The impact of major factors on the outlookEnvironmental concerns to reduce CO2 emissions from coal-fired plants, declining coal usage in the power sector due to renewables expansion and a cheap natural gas price, and policies that phase out coal to achieve carbon neutralities are the three major factors leading to the coal market downturn. The power sector accounts for nearly 65% of coal demand. By 2030, advanced economies will consume 50% less coal compared to their level in 2019 mainly due to policy-driven requirements. The increasing supply of renewables and natural gas has diversified the power generation sources, and significantly reduced the coal usage in this sector. Coal is the largest source of CO2 emissions and will be responsible for approximately 38% of the global CO2 emissions from 2020 to 2030. The policy-driven changes are significantly affecting coal usage worldwide (IEA, 2020; IMF, 2020).3. Development of new technologies in the coal industryPotential technologies are emerging in the coal industry, such as hydraulic fracturing to improve the coalbed methane production; the CO2 capture, utilization and sequestration (CCUS) to reduce CO2 emissions from coal combustion; the coal-to-liquid and coal-to-gas fuel conversion technologies to improve the fuel efficiency and reduce the CO2 emissions; internet of things, big data analytics, artificial intelligence, and automation to reduce operational costs and improve safety concerns and production efficiency in coal operations; and, underground coal gasification (UCG) to recover unminable coal. Particularly, UCG has been attempted for over a century, and has not yet achieved a commercial-scale development. Successful development and utilization of this technology would make the coal industry more competitive and increase its demand in the world market. A UCG operation consists of a series of injection and production wells drilled into a coal seam; the coal is ignited after certain air and/or oxygen is injected. Chemical reactions convert the coal to syngas by pyrolysis, combustion and gasification reactions in a manner similar to those processes in a surface gasifier. The produced syngas is a mixture of mainly carbon monoxide and hydrogen, which can be used as fuel for power generation and feedstock for various chemical products (i.e., hydrogen and ammonia) (Nourozieh et al., 2010; Seifi et al., 2015). The carbon captured during syngas utilization can be used for enhanced oil recovery.Emissions from syngas combustion are generally cleaner and less greenhouse gas emissions than coal-fired facilities. The UCG process is less costly than conventional surficial coal gasification because no coal mining, processing and transport are required, and no ash and slag removal or disposal is necessary. The environmental impact of UCG is relatively low compared to surficial gasification, as major disturbances in landscape and surface disposal of ash and coal tailings are not required. A properly designed UCG site will recognize and address potential groundwater pollution and subsidence issues; tests related to the cap rock integrity and highly cemented wells should be performed to avoid these issues. UCG can have obvious advantages compared with other in situ coal applications, including mining, coalbed methane exploration and development; the cavities after UCG can be used as CO2 storage (Jiang et al., 2019).4. CO2 capture technologies for coal power plantsFive main types of CO2 capture technologies from flue gas are proven. The average capture efficiency is from 80% to 90%. Cryogenic separation can provide the highest capture efficiency up to 99.99%. The CO2 capture step represents 70-80% of the overall CO2 capture and sequestration costs. Economic analysis shows that US $70-100 are needed to capture one tonne CO2 from flue gas (the average CO2 concentration is about 3-14$300 to $1,500 to capture CO2 directly from air (Brandl et al., 2021). It would be possible to commercialize the capture efficiency beyond 90%. However, operators are not able to make benefits under the current policies because of the high associated  capital and operational costs. The capture approaches include post-combustion, pre-combustion, oxyfuel combustion, chemical looping combustion and from air. The post-combustion technology is a mature technology, and has been widely applied. Future CO2 capture technologies are likely to focus on hybrid capture technologies, such as integrated CO2 capture and conversion. The coal use in the power sector surpassed 10 Gt CO2 emissions globally in 2018, and a successful application of carbon capture technologies can help the world reduce up to 8-10 Gt CO2 emissions annually. CCUS has received much research attention over the past two decades.Cited as: Ma, H., Chen, S., Xue, D., Chen, Y., Chen, Z. Outlook for the coal industry and new coal production technologies. Advances in Geo-Energy Research, 2021, 5(2): 119-120, doi: 10.46690/ager.2021.02.0

Pre-strain effect on twist springback of a 3D P-channel in deep drawing

With the widely use of high-strength steel sheets in the automotive industry, the twist springback phenomenon of the steel sheets under multi-step forming conditions has received extensive attention. In this work, the Dual Phase steel DP500 is taken as the research object to investigate the complex non-linear elastoplastic behaviors and twist springback under two-step loading paths. The large specimen with a pre-strain of 4% true strain in rolling direction is carried out on a large tensile testing machine, and several specific blanks are extracted from it at different directions for a subsequent P-channel forming. The influence of twist springback associated with the pre-strain is analyzed. The finite element model based on the non-linear elastic model and the homogeneous anisotropic hardening model (HAH) is also established for the springback prediction and stress analysis. The results indicate that the pre-strain has a considerable impact on the twist springback. The non-linear strain path changes resulted from pre-straining not only influence the residual stress but also affect the elastic modulus distribution.publishe

Iterative Data Refinement for Self-Supervised MR Image Reconstruction

Magnetic Resonance Imaging (MRI) has become an important technique in the clinic for the visualization, detection, and diagnosis of various diseases. However, one bottleneck limitation of MRI is the relatively slow data acquisition process. Fast MRI based on k-space undersampling and high-quality image reconstruction has been widely utilized, and many deep learning-based methods have been developed in recent years. Although promising results have been achieved, most existing methods require fully-sampled reference data for training the deep learning models. Unfortunately, fully-sampled MRI data are difficult if not impossible to obtain in real-world applications. To address this issue, we propose a data refinement framework for self-supervised MR image reconstruction. Specifically, we first analyze the reason of the performance gap between self-supervised and supervised methods and identify that the bias in the training datasets between the two is one major factor. Then, we design an effective self-supervised training data refinement method to reduce this data bias. With the data refinement, an enhanced self-supervised MR image reconstruction framework is developed to prompt accurate MR imaging. We evaluate our method on an in-vivo MRI dataset. Experimental results show that without utilizing any fully sampled MRI data, our self-supervised framework possesses strong capabilities in capturing image details and structures at high acceleration factors.Comment: 5 pages, 2 figures, 1 tabl

Crustal Structure of the Indochina Peninsula From Ambient Noise Tomography

The collision between the Indian and Eurasian plates promotes the southeastward extrusion of the Indochina Peninsula while the internal dynamics of its crustal deformation remain enigmatic. Here, we make use of seismic data from 38 stations and employ the ambient noise tomography to construct a 3‐D crustal shear‐wave velocity (Vs) model beneath the Indochina Peninsula. A low‐Vs anomaly is revealed in the mid‐lower crust of the Shan‐Thai Block and probably corresponds to the southern extension of the crustal flow from SE Tibet. Although the Khorat Plateau behaves as a rigid block, the observed low‐Vs anomalies in the lower crust and also below the Moho indicate that the crust may have been partially modified by mantle‐derived melts. The strike‐slip shearing motions of the Red River Fault may have dominantly developed crustal deformation at its western flank where a low‐Vs anomaly is observed at the upper‐middle crust

BIM-based space management system for operation and maintenance phase in educational office buildings

Lists and floor plans have been widely adopted as space management tools for educational office buildings. However, the two-dimensional floor plans fail to present the indoor complexity, which hinders users from intuitively observing the indoor equipment arrangements and adapting to the indoor environment within a short time. Meanwhile, insufficient research has been conducted on space management tools regarding building indoor navigation. A Building Information Modeling Space Management (BIMSM) system was proposed in this study based on BIM. This system is comprised of two components, i.e. indoor space allocation management and indoor path navigation. The real-time space usage can be queried and user demands may be matched with available space by applying the Space Usage Analysis (SUA) theory. After the establishment of indoor maps, an improved A* algorithm is used to provide smooth navigation paths, and the visualization of such paths can be provided in mobile terminals. The BIMSM system was applied in an office building in a university in Shanghai, China. In this case study, the overall user satisfaction reached 91.6% by greatly reducing space arrangement failures. The time indoor navigation took outperformed that based on the traditional A* algorithm, with the search efficiency increasing 5.28%. First published online 17 December 201

Optimization for Variable Height Wind Farm Layout Model

The optimization of wind farm layouts is very important for the effective utilization of wind resources. A fixed wind turbine hub height in the layout of wind farms leads to a low wind energy utilization and a higher LCOE (levelized cost of electricity). WOMH (Wind Farm Layout Optimization Model Considering Multiple Hub Heights) is proposed in this paper to tackle the above problem. This model is different from the traditional fixed hub height model, as it uses a variable height wind turbine. In WOMH, the Jensen wake and Weibull distribution are used to describe the wake effect on the wind turbines and wind speed distribution, respectively. An algorithm called DEGM (differential evolution and greedy method with multiple strategies) is proposed to solve WOMH, which is NP hard. In the DEGM, seven strategies are designed to adjust the distribution coordinates of wind turbines so that the height of the wind turbines will be arranged from low to high in the wind direction. This layout reduces the Jensen wake effect, thus reducing the value of the LCOE. The experimental results show that in the DEGM, when the number of wind turbines is 5, 10, 20, 30 and 50, the WOMH reduces the LCOE by 13.96%, 12.54%, 8.22%, 6.14% and 7.77% compared with the fixed hub height model, respectively. In addition, the quality of the solution of the DEGM is more satisfactory than that of the three-dimensional greedy algorithm and the DEEM (differential evolution with a new encoding mechanism) algorithm. In the case of five different numbers of wind turbines, the LCOE of DEGM is at least 3.67% lower than that of DEEM, and an average of 6.83% lower than that of three-dimensional greedy. The model and algorithm in this paper provide an effective solution for the field of wind farm layout optimization