Mixing Coefficients Research Articles

Maximum-likelihood estimation of the Po-MDDRCINAR(p) model with analysis of a COVID-19 data

Integer-valued data are frequently encountered in time series studies. A pth-order mixed dependence-driven random coefficient integer-valued autoregressive time series model (Po-MDDRCINAR(p)) in view of binomial and negative binomial operators, where the innovation sequence follows a Poisson distribution, is investigated to provide meaningful theoretical explanations. Strict stationary and ergodicity of the model are demonstrated. Furthermore, the conditional least-squares and conditional maximum-likelihood methods are adopted to estimate the parameters, where the asymptotic characterization of the estimators is derived. Finite-sample properties of the conditional maximum-likelihood estimator are examined in relation to the widely used conditional least-squares estimator. The conclusion is that, if the Poisson assumption of the innovation sequence can be justified, conditional maximum-likelihood method performs better in terms of MADE and MSE. Finally, the practical performance of the model is illustrated by a set of COVID-19 data of suspected cases in China with a comparison with relevant models that exist so far in the literature.

Study on the Vertical Distribution Characteristics of Suspended Sediment Driven by Waves and Currents

Port coasts are affected by waves and tidal currents, and sediment continues to silt up, leading to a reduction in the depth of water in the channel, blocking the channel and seriously affecting the safe operation of ports. The main cause of sediment deposition in ports is suspended sediment transport, and the characteristics of the vertical distribution of suspended sediment concentrations are the embodiment of the suspended sediment transport law. This paper is divided into three parts to study the vertical distribution characteristics of suspended sediment concentrations. Firstly, the shortcomings of the traditional diffusion model were analysed by using the finite mixing theory (FMT); secondly, the sediment mixing length coefficient κs model was introduced and combined with the sediment group settling velocity model to establish the vertical distribution model of suspended sediment concentrations under wave–current; finally, the effects of various factors on the vertical distribution of the suspended sediment concentration were investigated. The results show that the model in this paper has the characteristics of “low variance and low bias”, which solves the problem that κs is difficult to determine. When the model κs < κs′ (κs′ = 0.4), the concentration of suspended sediment predicted by κs′ is overestimated, and vice versa. As the sediment concentration increases, the interaction between particles increases and the vertical distribution of the suspended sediment concentration shows the pattern of “small top and large bottom”. The larger the particle size, the greater the sedimentation rate of the suspended sediment, and a large amount of sediment will be suspended near the bottom without mixing. The higher the wave height, the stronger the boundary layer turbulence and the movement of the water particles’ trajectory, and the smaller the difference in sediment concentration between the bottom and the sea surface.

CFD-guided scaling of Pseudomonas putida fermentation

This study evaluated the scale-up of Pseudomonas putida fed-batch fermentation from a 2 L benchtop-scale stirred bioreactor to a 200 L pilot-scale tank by using a validated computational fluid dynamic (CFD) model. One of the major concerns in this fermentation process is the potential reduction in mixing quality with increasing scale, leading to lower yield or product quality. For a low-risk scale-up, a multiphase Euler-Euler CFD model was developed that simulated the hydrodynamics of the fed-batch system at various filling volumes, representing different stages of the fermentation process. The model was validated using experimental data of mixing time and mass transfer coefficient. The hydrodynamic model was then coupled with a Monod kinetic model of P. putida ‘s fermentation. Response surface methodology was used to generate a performance map of the pilot bioreactor at various aeration, agitation, and bioreactor filling volumes. The study considered different established scale-up approaches, such as constant tip speed and aeration rate across scales, constant kLa, as well as constant power to unit of liquid volume (P/V). The performance of the bioreactor was assessed, and the optimum operating ranges of the input parameters were obtained at different stages of the fermentation. Using performance map the possibility of substrate and oxygen gradient formation, and the gradient severity inside the pilot bioreactor at different working volumes were evaluated.

Multi-vendor validation of automated left ventricular analysis from 3D echocardiography using artificial intelligence

Abstract Background Accurate echocardiographic assessment of left ventricular (LV) function is dependent on both image acquisition and analysis. Although 3D echocardiography (3DE) is increasingly recommended for volume quantification, manually derived volumes are limited by operator subjectivity, variable image quality, and the lengthy duration of analysis. Consequently, several automated approaches based on artificial intelligence (AI) have been proposed to segment the LV cavity from 3DE and have demonstrated better accuracy and reproducibility compared to human experts. While the utility of AI models in the clinic depends on their ability to generalise to different populations and ultrasound vendors, the external validation of developed models remains limited. Purpose This study evaluates the ability of an existing AI model trained using data from a single vendor to accurately estimate routine 3DE LV indices in an independent external dataset acquired using equipment from a different vendor. Methods A previously published and internally validated deep learning model for segmentation of the LV cavity and myocardium from 3DE was used to automatically derive LV indices including end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and ejection fraction (EF) [1]. A second independent dataset consisting of paired 3DE and CMR images from 65 marathon runners from a different centre (using equipment from a different vendor) was subsequently used for external validation. Images from both datasets were subjectively scored from 1 (poor) to 5 (excellent) as a measure of image quality. Agreement with CMR as the reference was assessed using a two-way mixed effects intraclass correlation coefficient (ICC), supplemented by Bland-Altman analysis, to determine the model bias (between 3DE and CMR) and 95% limits of agreement (LOA). To assess whether performance could be improved by inclusion of multi-vendor data during training, the initial model was fine-tuned using a small subset (n=9) of images from the second centre. Results The mean image quality score from the second dataset was 2.5 (± 1.1), which was significantly lower than the mean of 3.4 (± 1.0) from the first dataset. Despite vendor and image quality differences, the initial model produced good agreement with CMR in EDV, ESV, and LV mass but only poor agreement in EF (Fig 1). Fine tuning greatly improved agreement in EF with reduced bias and narrower 95% LOA (Fig 2). Conclusion With the addition of a small number of cases for fine-tuning, AI models for automated LV analysis can achieve good performance analysing 3DE images acquired from a different vendor and population. Advancing model generalisability through more sophisticated data augmentation strategies may enable truly vendor-agnostic automated LV analysis from 3DE.

A subchannel analysis code for advanced liquid metal fast reactor cores and study on heat transfer characteristics of core geometry parameters

The liquid metal fast reactor represents an important reactor type for the future development of nuclear energy. Accurately modeling and predicting the subchannel flow and heat transfer phenomenon in the rod bundles plays a key role in ensuring core safety. Consequently, a subchannel code suitable for the liquid metal fast reactor is analyzed based on the subchannel analysis code of the Pressurized Water Reactor (PWR). The modified code incorporates various types of liquid metals, such as lead, lead-bismuth eutectic (LBE), and sodium, along with their constitutive models, enhancing the applicability of the liquid metal reactor systems. This code has been verified and validated at several levels, including comparing the code simulation results with other similar subchannel codes results, high-dimensional Computational Fluid Dynamics (CFD) simulations, and experiment data. The sensitivity analysis of core geometric parameters and turbulent mixing coefficients is performed based on the modified version code. The influence of the core geometry, including the rod Pitch to Diameter ratio (P/D), Rod to Wall gap (RTW) and the wire wrap pitch (H) on the sensitivity of outlet temperature has been investigated. The results show that the new subchannel analysis code suitable for liquid metal cooled reactor shows reasonable agreement with the verified and validated results. The geometric parameters, such as P/D, RTW and H have noticeable effects on the outlet temperature difference of the subchannels. Additionally, the outlet temperature distribution in the subchannel is significantly affected by different turbulent mixing coefficients and the distribution of the turbulent mixing coefficient also influenced by the reactor core sizes. Overall, this study could support the rod bundles design and safety analysis in the liquid metal reactors.

Numerical simulation study of liquid–liquid mixing of high-viscosity fluids under laminar flow in a reverse flow multi-stage Tesla valve

In this study, computational fluid dynamics was employed to conduct a numerical simulation of the mixing performance and flow characteristics of two highly viscous liquids under laminar flow conditions within a reversed Tesla valve. Scalar transport techniques are employed to analyze the efficiency of liquid–liquid mixing in high-viscosity fluids. The focus of this study is to investigate the optimal mixing behavior between different parameters. Results indicate that an increase in Reynolds number leads to intensified Dean vortices, thereby promoting liquid–liquid mixing efficiency. Additionally, the mixing coefficient shows a negative correlation with Schmidt number (Sc), with a diminishing impact on the mixing coefficient when Sc ≥ 104. This is attributed to the dominance of fluid flow in controlling mixing within the channel at higher Schmidt numbers. Furthermore, this study compares the influence of valve angles (α) and stage numbers (n) on the mixing coefficient under identical Reynolds and Schmidt number conditions. As the number of Tesla valve stages increases, fluid acceleration within the pipeline is enhanced. Moreover, larger valve angles result in increased lengths of the curved section, leading to higher mixing efficiency. Therefore, to enhance mixing efficiency, it is recommended to increase the valve angle and the number of stages in the Tesla valve.

Carbon Emission Prediction Algorithm for Community Electricity Consumption Based on Improved PSO Algorithm with Optimized Autoregressive Moving Average Models

Abstract The anticipation of carbon emissions stemming from community electricity usage stands as a pivotal field of study. Such analysis holds the potential to furnish communities with informed pathways towards crafting viable carbon reduction strategies, thereby contributing significantly to the overarching goals of carbon peaking and eventual neutrality. The intricacies of carbon emissions from community electricity consumption are manifold, entangled with variables like energy consumption patterns, energy mix, and carbon emission coefficients. Hence, the imperative lies in crafting a predictive framework adept at holistically integrating these variables to enhance prognostic precision and reliability. Within this research, we propose a dynamic carbon emission factor regression model. This model is uniquely poised to capture real-time shifts influenced by changes in energy structure and policy landscapes, thereby amplifying predictive sensitivity. Leveraging the Particle Swarm Optimization (PSO) algorithm, we synchronously optimize the autoregressive terms (p) of the Autoregressive Integrated Moving Average (ARIMA) model and the moving average terms (q) of the MA model to attain a globally optimal solution. Crucially, this approach obviates the need for manual intervention and arbitrary parameter selection. In contrast to conventional optimization methodologies, our paper advances the PSO’s weight calculation mechanism. By assigning greater weight values during the initial iterations, the algorithm maintains robust global search capabilities. Subsequently, the inertia weight diminishes progressively throughout the iteration process, fostering precise local exploration.

Development and testing of a tool to assess parental care for children with mental illness (PCCMI).

Involving parents in mental health treatment services can empower parents to manage their child's illness. We conducted this study to develop a tool to assess parental care for children with mental illness. The present study was conducted in five phases that included in-depth interviews with parents in the outpatient department (OPD), focus group discussions (FGDs) with parents in an in-patient setting, and with a multidisciplinary team, item development, and the assessment of psychometric properties of the tool. Parental care for children with mental illness (PCCMI) has 50 items derived from a thematic analysis of in-depth interviews of 31 parents and 2 FGDs with eight parents admitted at CPC and eight mental health professionals experienced in child and adolescent psychiatry. It is a 5-point Likert scale. The tool showed a high item-level Content Validity Index (I-CVI range: 0.8-1.0) and a high Scale Content Validity Index (S-CVI = 0.91). The test-retest method was used to calculate reliability. A total of 38 parents were included in the test-retest method. The mother and father's mean age and standard deviation (SD) scores were 39.7 ± 5.8 and 45.1 ± 4.9, respectively. Most parents' education level is above graduation (mothers: 86.8% and fathers: 86%). Around 63% of mothers were housewives and had normal pregnancies. The single measure two-way mixed absolute agreement intraclass correlation coefficient (ICC) value for 38 subjects was 0.96 with a 95% confidence interval (CI: 0.90-0.95), indicating high reliability. PCCMI has high I-CVI and S-CVI and good ICC test-retest reliability. This tool will help mental health professionals in the formal assessment of parental care for children with mental illness and provide the intervention to address issues related to the same.

Existence and Uniqueness of the Solution of a Mixed Problem for a Parabolic Equation Under Nonconventional Boundary Conditions

Our research focuses on examining a mixed problem associated with a second-order parabolic equation that features temporal mixing and variable coefficients, subject to non-local and non-self-adjoint boundary conditions. We establish the problem’s unique solvability by imposing specific conditions on the provided data, utilizing a combination of residue and contour integral methods. Additionally, our study produces an explicit analytical solution for addressing the problem at hand.

The geometry of admixture in population genetics: the blessing of dimensionality.

We present a geometry-based interpretation of the f-statistics framework, commonly used in population genetics to estimate phylogenetic relationships from genomic data. The focus is on the determination of the mixing coefficients in population admixture events subject to post-admixture drift. The interpretation takes advantage of the high dimension of the dataset and analyzes the problem as a dimensional reduction issue. We show that it is possible to think of the f-statistics technique as an implicit transformation of the genomic data from a phase space into a subspace where the mapped data structure is more similar to the ancestral admixture configuration. The 2-way mixing coefficient is, as a matter of fact, carried out implicitly in this subspace. In addition, we propose the admixture test to be evaluated in the subspace because the comparison with the conventional one provides an important assessment of the admixture model. The overarching geometric framework provides slightly more general formulas than the f-formalism by using a different rationale as a starting point. Explicitly addressed are 2- and 3-way admixtures. The mixture proportions are provided by suitable linear fits, in 2 or 3 dimensions, that can be easily visualized. The difficulties encountered with introgression and gene flow are also addressed. The developments and findings are illustrated with numerical simulations and real-world cases.

Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach.

Electroencephalography (EEG)-based applications in brain-computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc.

Grey‐zone simulations of shallow‐to‐deep convection transition using dynamic subgrid‐scale turbulence models

AbstractWe examine the ability of two dynamic turbulence closure models to simulate the diurnal development of convection and the transition from dry to shallow cumuli and then to deep convection. The dynamic models are compared with the conventional Smagorinsky scheme at a range of cloud‐resolving and grey‐zone resolutions. The dynamic schemes include the Lagrangian‐averaged, scale‐dependent dynamic Smagorinsky model and a Lagrangian‐averaged, dynamic mixed model. The conventional Smagorinsky model fails to reproduce the shallow convection stage beyond the large‐eddy simulation regime, continuously building up the convective available potential energy that eventually leads to an unrealistic deep convection phase. The dynamic Smagorinsky model significantly improves the representation of shallow and deep convection; however, it exhibits issues similar to the conventional scheme at coarser resolutions. In contrast, the dynamic mixed model closely follows the large‐eddy simulation results across the range of sub‐kilometre simulations. This is achieved by the combined effect of an adaptive length scale and the inclusion of the Leonard terms, which can produce counter‐gradient fluxes through the backscatter of energy from the subgrid to the resolved scales and enable appropriate non‐local contributions. A further sensitivity test on the inclusion of the Leonard terms on all hydrometeor fluxes reveals the strong interaction between turbulent transport and microphysics and the possible need for further optimisation of the dynamic mixed model coefficients together with the microphysical representation.

First Scan Search for Dark Photon Dark Matter with a Tunable Superconducting Radio-Frequency Cavity.

Dark photons have emerged as promising candidates for dark matter, and their search is a top priority in particle physics, astrophysics, and cosmology. We report the first use of a tunable niobium superconducting radio-frequency cavity for a scan search of dark photon dark matter with innovative data analysis techniques. We mechanically adjusted the resonant frequency of a cavity submerged in liquid helium at a temperature of 2K, and scanned the dark photon mass over a frequency range of 1.37MHz centered at 1.3GHz. Our study leveraged the superconducting radio-frequency cavity's remarkably high quality factors of approximately 10^{10}, resulting in the most stringent constraints to date on a substantial portion of the exclusion parameter space on the kinetic mixing coefficient ε between dark photons and electromagnetic photons, yielding a value of ε<2.2×10^{-16}.

Numerical Simulation and Experimental Comparison of System Analysis Module 1D Mixing Model for Cold Shock Transients in the Gallium Thermal-Hydraulic Mixing Facility

Liquid metals are being investigated as coolants in many advanced reactor designs because of their high thermal conductivity and effectiveness at high temperatures. However, they often pose challenges to reactor operation and safety because of the complex thermal mixing and stratification in the plenum of pool-type reactor designs. The advanced system analysis code System Analysis Module (SAM) currently under development at Argonne National Laboratory aims to develop and implement thermal mixing models to accurately capture these complex thermal fluid behaviors. In this study, the SAM thermal mixing model was compared against experimental data from the Gallium Thermal-Hydraulic Experiment facility, a scaled liquid metal test facility that uses gallium as a surrogate fluid to investigate the stratification and thermal mixing of low-Prandtl-number fluids in the upper plenum of a liquid metal–cooled reactor. Two cold shock transient cases were used: one with stable stratified flow (Ri = 32) and one with stronger thermal mixing (Ri = 0.5). The resultant temperatures were then compared with the experimental temperatures over the entire plenum to assess the ability of the mixing models to capture the thermal behavior and to better correspond mixing parameters to various flow scenarios. Generally, the zero-dimensional mixing model was more capable of capturing the bulk temperature of the component modeled assuming that an accurate mass flow rate was provided, but it was inherently unable to capture thermal gradients in space. The one-dimensional mixing model was capable of capturing that the thermal gradients provided accurate selection of the mixing coefficients. The temperature at the outlet junction was compared over time for each of the mixing models with the recorded experimental temperature. The implemented mixing models demonstrated the ability to effectively capture the overall thermal behavior for stronger mixing scenarios but struggled with more stably stratified flows. It was found that a system analysis code’s covering of the entire range of different operating conditions still remains a challenging task, and it is suggested that further model and closure improvements are necessary to accurately capture complex thermal mixing and stratification phenomena.

Research on the hybrid chaos-coud salp swarm algorithm

Salp swarm algorithm (SSA) is a new swarm intelligence optimization algorithm, which has the advantages of simple structure, almost no parameter setting. However, SSA also has the shortcomings of slow convergence speed in the early stage and low optimization accuracy in the later stage when searching for the optimal solution. To address the problems, this study proposes a hybrid chaos-cloud salp swarm algorithm (CC-SSA). First, to accelerate the convergence speed, a positive normal cloud generator is used to perform local search on the superior salp individuals. Second, to enhance the diversity of CC-SSA and avoid it from falling into local optimum, the chaotic map is used to perform global search on the inferior salp individuals by adding global perturbation. Third, to control the execution ratio of global and local search, the mixed control parameter (MLMS) and population allocation coefficient (r) are introduced to organically combine three algorithms, SSA, chaotic map, and cloud model. Finally, to evaluate the performance of the proposed algorithm, it is compared with other 9 conventional algorithms on 12 classic functions. The experimental results show that CC-SSA has an average accuracy rate of 92.92 %, which ranks first. In addition, the average iteration of CC-SSA scores the third. Therefore, compared to conventional optimization algorithms, CC-SSA has better performance in terms of execution time and optimize accuracy.

A demographic and performance assessment of horses used in draw-based Intercollegiate Horse Shows Association competitions

Intercollegiate Horse Show Association (IHSA) competitions evaluate riders on equitation and skills when riding randomly drawn horses. This study considered demographic and competition-related variables of horses involved in IHSA competitions, and their correlation to performance through points accumulated. Demographics were recorded for fifty-nine horses in two-day Hunter Seat and Western IHSA shows along with use of artificial riding aids, day of competition, number of rides, and points, to evaluate performance. Data were analyzed using a mixed effect model and correlation coefficients with significance set at P < 0.05 (SAS 9.4). Hunter Seat horses averaged more rides per day than Western (P < 0.0001), but points per ride were not different between disciplines (P = 0.57) or days of shows (P = 0.47). Use of artificial aids did not impact points per ride a horse accumulated (P = 0.63 Hunter Seat, P = 0.41 Western). Age, (r = -0.10, P = 0.60; r = 0.02, P = 0.90), BCS (r = 0.15, P = 0.42; r = 0.17, P = 0.34), height (r = 0.20, P = 0.28; r = 0.15, P = 0.39), and weight (r = 0.23, P = 0.23; r = 0.20, P = 0.25) were weakly correlated with points earned for Hunter Seat and Western horses, respectively. Random draw in IHSA is suggested to be effective for equitation competitions, allowing a fair opportunity for riders, regardless of the horse-related factors evaluated in this study.

Projection-based reduced order modeling of multi-species mixing and combustion

High-fidelity simulations of mixing and combustion processes are computationally demanding and time-consuming, hindering their wide application in industrial design and optimization. This study proposes projection-based reduced order models (ROMs) to predict spatial distributions of physical fields for multi-species mixing and combustion problems in a fast and accurate manner. The developed ROMs explore the suitability of various regression methods, including kriging, multivariate polynomial regression (MPR), k-nearest neighbors (KNN), deep neural network (DNN), and support vector regression (SVR), for the functional mapping between input parameters and reduced model coefficients of mixing and combustion problems. The ROMs are systematically examined using two distinct configurations: steam-diluted hydrogen-enriched oxy-combustion from a triple-coaxial nozzle and fuel-flexible combustion in a practical gas-turbine combustor. The projected low-dimensional manifolds are capable of capturing important combustion physics, and the response surfaces of reduced model coefficients present pronounced nonlinear characteristics of the flowfields with varying input parameters. The ROMs with kriging present a superior performance of establishing the input–output mapping to predict almost all physical fields, such as temperature, velocity magnitude, and combustion products for both test problems. The accuracy of DNN is less encouraging owing to the stringent requirement on the size of training database. KNN performs well in the region near the design points but its effectiveness diminishes when the test points are distant from the sampling points, whereas SVR and MPR exhibit large prediction errors. For the spatial prediction at unseen design points, the ROMs achieve a prediction time of up to eight orders of magnitude faster than conventional numerical simulations, rendering an efficient tool for the fast prediction of mixing and combustion fields and potentially an alternative of a full-order numerical solver.

Tipping Points in Overturning Circulation Mediated by Ocean Mixing and the Configuration and Magnitude of the Hydrological Cycle: A Simple Model

Abstract In the modern ocean, the transformation of light surface waters to dense deep waters primarily occurs in the Atlantic basin rather than in the North Pacific or Southern Oceans. The reasons for this remain unclear, as both models and paleoclimatic observations suggest that sinking can sometimes occur in the Pacific. We present a six-box model of overturning that combines insights from a number of previous studies. A key determinant of the overturning configuration in our model is whether the Antarctic Intermediate Waters are denser than the northern subpolar waters, something that depends on the magnitude and configuration of atmospheric freshwater transport. For the modern ocean, we find that although the interbasin atmospheric freshwater flux suppresses Pacific sinking, the poleward atmospheric freshwater flux out of the subtropics enhances it. When atmospheric temperatures are held fixed, North Pacific overturning can strengthen with either increases or decreases in the hydrological cycle, as well as under reversal of the interbasin freshwater flux. Tipping-point behavior, where small changes in the hydrological cycle may cause the dominant location of densification of light waters to switch between basins and the magnitude of overturning within a basin to exhibit large jumps, is seen in both transient and equilibrium states. This behavior is modulated by parameters such as the poorly constrained lateral diffusive mixing coefficient. If hydrological cycle amplitude is varied consistently with global temperature, northern polar amplification is necessary for the Atlantic overturning to collapse. Certain qualitative insights incorporated in the model can be validated using a fully coupled climate model. Significance Statement Currently, the global overturning circulation involves the conversion of waters lighter than Antarctic Intermediate Water to deep waters denser than Antarctic Intermediate Water primarily in the North Atlantic, rather than in the North Pacific or Southern Oceans. Many different factors have been invoked to explain this configuration, with atmospheric freshwater transport, basin geometry, lateral mixing, and Southern Ocean winds playing major roles. This paper develops a simple theory that combines previous theories, presents the intriguing idea that alternate configurations might be possible, and identifies multiple possible tipping points between these states.

Experimental study of air curtains blocking saltwater intrusion under estuary dynamic conditions

The saltwater intrusion seriously threatens the water supply security of estuaries, while the existing salty blocking measures (e.g., gates and dams) are rude and the dynamic mechanism is not fully considered. In order to investigate countermeasures to reduce the risk of the saltwater intrusion, as well as enrichment blocking methods, in this paper, the salt-inhibiting properties of air curtain under tidal action was investigated based on a long-distance physical flume with dual tidal-salinity control capability. Experimental results indicate that air curtains significantly diminish distance of saltwater into estuaries. The bubble plumes, on the one hand, promote vertical mixing, elevating high-salinity water from the depths to the surface, thereby disrupting the vertical circulation structure of salt wedge. On the other hand, the energy dissipation and barrier effect produced by bubble bursting can significantly reduce the momentum of the invading saltwater. Meanwhile, the research emphasizes that the salt intrusion length is negatively correlated with the airflow discharge, whereas the salty-blocking effect prefer better of the weakly-mixed estuary than the strongly-mixed. Most importantly, the air curtain mixing coefficient ka were proposed at the first time to quantify the salty blocking effect. The mixing coefficient ka is the function of airflow discharge Qa and tidal current ut, demonstrating a direct proportionality to Qa and ut−1. Moreover, a quantitative relationship between ka and Qa/ut was obtained from experimental results. The research results would help inform the flexible salt-inhibiting measures of estuarine air curtain.

Flow patterns and heat transfer of an idealized square city in non-uniform heat flux and different background wind conditions

The city scale buoyancy-driven flow is crucial for the wind and thermal environment in urban areas, exerting a significant impact on the city ventilation, pollutants dispersion and heat removal. The intra-urban variations of hot spots and heat flux are drawing more attention on beating the urban heat. This study aims at analyzing the influences of non-uniform heat flux and background wind on the city-scale heat removal ability and underlying mechanisms. The large eddy simulation model was used and verified with water tank experiments. Results show that positive heat flux and negative heat flux in the rural areas will enhance and depress the strength of the urban heat dome flow respectively compared to the adiabatic condition. The high-speed regions are wider and stronger when the rural area has positive heat flux condition. Under positive rural heat flux condition, the time-average mixed layer height in urban areas across the city center plane is nearly twice that under negative rural heat flux condition. Besides, the city-scale heat transfer coefficients in positive rural heat flux condition are higher than those under negative rural heat flux and adiabatic conditions. For cases of non-uniform heat flux in urban areas, the differences of time-average mixed layer height and heat transfer coefficients are not obvious. The heat transfer coefficients decrease initially and then increase with increasing background wind speed indicating the non-linear interaction between approaching wind and urban heat dome flow. Moreover, the growth rate of the heat transfer coefficient decreases when the non-dimensional wind speed exceeds 1.19.