Advances in Modelling of Heat and Mass Transfer in Porous Materials

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Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems

Energy Harvester Based on the Synchronization Phenomenon of a Circular Cylinder

A concept of generating power from a circular cylinder undergoing vortex-induced vibration (VIV) was investigated. Two lead zirconate titanate (PZT) beams which had high power density were installed on the cylinder. A theoretical model has been presented to describe the electromechanical coupling of the open-circuit voltage output and the vibration amplitudes based on a second-order nonlinear Van der pol equation and Gauss law. A numerical computation was applied to measure the capacity of the power generating system. The lift and drag coefficient and the vortex shedding frequency were obtained to verify how the nondimensional parameter reduced velocity Ur affects the fluid field. Meanwhile, a single-degree of freedom system has been added to describe the VIV, presynchronization, and synchronization together with postsynchronization regimes of oscillating frequencies. And the amplitudes of the vibration have been obtained. Finally, the vibrational amplitudes and the voltage output could go up to a high level in the synchronization region. The maximum value of the voltage output and the corresponding reduced velocity Ur were 8.42 V and 5.6, respectively

Evolution of the spatiotemporal pattern of PM2.5 concentrations in China – a case study from the Beijing-Tianjin-Hebei region

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration

Multi-Layer Arctic Mixed-Phase Clouds Simulated by a Cloud-Resolving Model: Comparison with ARM Observations and Sensitivity Experiments

A cloud-resolving model (CRM) is used to simulate the multiple-layer mixed-phase stratiform (MPS) clouds that occurred during a three-and-a-half day subperiod of the Department of Energy-Atmospheric Radiation Measurement Program s Mixed-Phase Arctic Cloud Experiment (M-PACE). The CRM is implemented with an advanced two-moment microphysics scheme, a state-of-the-art radiative transfer scheme, and a complicated third-order turbulence closure. Concurrent meteorological, aerosol, and ice nucleus measurements are used to initialize the CRM. The CRM is prescribed by time-varying large-scale advective tendencies of temperature and moisture and surface turbulent fluxes of sensible and latent heat. The CRM reproduces the occurrences of the single- and double-layer MPS clouds as revealed by the M-PACE observations. However, the simulated first cloud layer is lower and the second cloud layer thicker compared to observations. The magnitude of the simulated liquid water path agrees with that observed, but its temporal variation is more pronounced than that observed. As in an earlier study of single-layer cloud, the CRM also captures the major characteristics in the vertical distributions and temporal variations of liquid water content (LWC), total ice water content (IWC), droplet number concentration and ice crystal number concentration (nis) as suggested by the aircraft observations. However, the simulated mean values differ significantly from the observed. The magnitude of nis is especially underestimated by one order of magnitude. Sensitivity experiments suggest that the lower cloud layer is closely related to the surface fluxes of sensible and latent heat; the upper cloud layer is probably initialized by the large-scale advective cooling/moistening and maintained through the strong longwave (LW) radiative cooling near the cloud top which enhances the dynamical circulation; artificially turning off all ice-phase microphysical processes results in an increase in LWP by a factor of 3 due to interactions between the excessive LW radiative cooling and extra cloud water; heating caused by phase change of hydrometeors could affect the LWC and cloud top height by partially canceling out the LW radiative cooling. It is further shown that the resolved dynamical circulation appears to contribute more greatly to the evolution of the MPS cloud layers than the parameterized subgrid-scale circulation

Modeling of CO2 absorption into 4-diethylamino-2-butanol solution in a membrane contactor under wetting or non-wetting conditions

© 2022 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1016/j.ccst.2022.100069In this work, 4-diethylamino-2-butanol (DEAB) as a new type of alkanolamine solvent is used for CO2 capture in a hollow fiber membrane contactor (HFMC). A model describing the gas and liquid reactions and transport inside the membrane contactor under the wetting or non-wetting conditions was built. The countercurrent flow of natural gas and solvent was considered in the model. To investigate the influence of solvent type on decarburization efficiency, DEAB was used and compared with other common solvents such as potassium carbonate (K2CO3), triethylamine (TEA) and diethanolamine (DEA). Under the same operating conditions, the impact of parameters such as humidity, gas flow rate, liquid concentration, membrane length on the decarburization performance was examined. The results indicate that DEAB solvent has the best overall performance especially under the wetting conditions. It was noted that increasing liquid concentration, membrane length and decreasing gas flow rate enhance decarburization.Published versio

CCND1 as a Predictive Biomarker of Neoadjuvant Chemotherapy in Patients with Locally Advanced Head and Neck Squamous Cell Carcinoma

BACKGROUND: Cyclin D1 (CCND1) has been associated with chemotherapy resistance and poor prognosis. In this study, we tested the hypothesis that CCND1 expression determines response and clinical outcomes in locally advanced head and neck squamous cell carcinoma (HNSCC) patients treated with neoadjuvant chemotherapy followed by surgery and radiotherapy. METHODOLOGY AND FINDINGS: 224 patients with HNSCC were treated with either cisplatin-based chemotherapy followed by surgery and radiotherapy (neoadjuvant group, n = 100) or surgery and radiotherapy (non-neoadjuvant group, n = 124). CCND1 expression was assessed by immunohistochemistry. CCND1 levels were analyzed with chemotherapy response, disease-free survival (DFS) and overall survival (OS). There was no significant difference between the neoadjuvant group and non-neoadjuvant group in DFS and OS (p = 0.929 and p = 0.760) when patients treated with the indiscriminate administration of cisplatin-based chemotherapy. However, in the neoadjuvant group, patients whose tumors showed a low CCND1 expression more likely respond to chemotherapy (p<0.001) and had a significantly better OS and DFS than those whose tumors showed a high CCND1 expression (73% vs 8%, p<0.001; 63% vs 6%, p<0.001). Importantly, patients with a low CCND1 expression in neoadjuvant group received more survival benefits than those in non-neoadjuvant group (p = 0.016), however patients with a high CCND1 expression and treated with neoadjuvant chemotherapy had a significantly poor OS compared to those treated with surgery and radiotherapy (p = 0.032). A multivariate survival analysis also showed CCND1 expression was an independent predictive factor (p<0.001). CONCLUSIONS: This study suggests that some but not all patients with HNSCC may benefit from neoadjuvant chemotherapy with cisplatin-based regimen and CCND1 expression may serve as a predictive biomarker in selecting patients undergo less than two cycles of neoadjuvant chemotherapy

Recent advances in carbon dioxide utilization

Carbon dioxide (CO2) is the major contributor to greenhouse gas (GHG) emissions and the main driver of climate change. Currently, CO2 utilization is increasingly attracting interest in processes like enhanced oil recovery and coal bed methane and it has the potential to be used in hydraulic fracturing processes, among others. In this review, the latest developments in CO2 capture, utilization, conversion, and sequestration are examined through a multi-scale perspective. The diverse range of CO2 utilization applications, including mineralization, biological utilization, food and beverages, energy storage media, and chemicals, is comprehensively presented. We also discuss the worldwide research and development of CO2 utilization projects. Lastly, we examine the key challenges and issues that must be faced for pilot-scale and industrial applications in the future. This study demonstrates that CO2 utilization can be a driver for the future development of carbon capture and utilization technologies. However, considering the amount of CO2 produced globally, even if it can be reduced in the near-to mid-term future, carbon capture and storage will remain the primary strategy and, so, complementary strategies are desirable. Currently, the main CO2 utilization industry is enhanced oil and gas recovery, but considering the carbon life cycle, these processes still add CO2 to the atmosphere. In order to implement other CO2 utilization technologies at a large scale, in addition to their current technical feasibility, their economic and societal viability is critical. Therefore, future efforts should be directed toward reduction of energy penalties and costs, and the introduction of policies and regulation encouraging carbon capture, utilization and storage, and increasing the public acceptance of the strategies in a complementary manner

Afterpulse correction for micro-pulse lidar to improve middle and upper tropospheric aerosol measurements

Micro-pulse lidar (MPL) measurements have been widely used in atmospheric research over the past few decades. However, the MPL afterpulse noise has a large impact on the MPL aerosol measurement in the middle and upper troposphere, and an effective correction method is still lacking. Here, a new afterpulse correction approach is presented by using measurements with low-level optically thick clouds to act as the lids blocking atmospheric signals beyond the clouds completely. Examples are provided to illustrate the effectiveness of this correction method. Using one-year 2014 MPL measurements at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site, the impact of the correction on the aerosol measurements is quantified. The relative error (RE, %) of attenuated backscattering ratio (ABR) without the afterpulse correction is 30% and 190% at the height of 1.00 km and 9.00 km (AGL), and the RE is larger for weaker aerosol signals (ABR&lt;2). The RE of linear depolarization ratio (LDR), which is significantly higher than that of ABR for a given aerosol layer, is highly non-linear above 3.00 km, with a value of 300%-900% for weak aerosols (ABR&lt;2) above 3.00 km. Therefore, the afterpulse correction is critical for the middle and upper tropospheric aerosol observations. We demonstrated that our afterpulse correction can provide reasonable particle depolarization ratio (PDR) to properly identify dust aerosols. The newly developed method can be applied to long-term MPL measurements to support aerosol and mixed-phase/ice cloud interaction study at the NSA site.</p