Simulating Temporally and Spatially Correlated Wind Speed Time Series by Spectral Representation Method

In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m-dimensional Gaussian stochastic vector process Z(t) is employed to model the temporal-spatial correlation of wind speeds at m different sites. In general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of Z(t), which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of Z(t) can be analytically obtained. Then, Fourier transformation is implemented to establish the cross-spectral density matrix of Z(t), and an analytical approach is proposed to generate samples of wind speeds at m different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds

Numerical study of response behaviors of natural gas hydrate reservoir around wellbore induced by water jet slotting

The trial production of natural gas hydrate reservoirs remains poor. Reasonable reservoir reconstruction, which can improve formation permeability, is an important approach to increasing the efficiency and enhancing production. In this work, water jet slotting is proposed to reconstruct an natural gas hydrate reservoir near a wellbore. The spatial slots formed by water jet slotting not only directly constitute high-permeability channels, but also generate disturbances to the surrounding in-situ sediment. Water jet slotting disturbances to nearby sediment was investigated using a three dimensional flow-structure coupling model to evaluate the proposed reconstruction method. The reservoir at the SH2 site in the Shenhu area of the South China Sea was used as the reference. A horizontal slotting arrangement along the vertical well was adopted. The results demonstrate that water jet slotting can change the primary stress state of the sediment around the wellbore, and generate a dominant stress relaxation zone and small stress concentration zone. Within the stress relaxation zone, the in-situ compressive stress was remarkably reduced or even transformed into tensile stress, accompanied by sediment displacement and volumetric expansion strain. This is conducive to loosening the sediment around the wellbore and improving the permeability characteristics. In addition, the influence of the water jet slotting parameters including slot radius, spacing, and number on disturbances to the nearby sediment was studied. Reservoir responses to water jet slotting under balanced and unbalanced bottom-hole pressures were compared and analyzed. This study provides a reference for natural gas hydrate reservoir reconstruction using water jet slotting. Cited as: Huang, M., Su, D., Zhao, Z., Wu, L., Fang B., Ning, F.  Numerical study of response behaviors of natural gas hydrate reservoir around wellbore induced by water jet slotting. Advances in Geo-Energy Research, 2023, 7(2): 75-89. https://doi.org/10.46690/ager.2023.02.0

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance

An Inhibitory Effect of Extracellular Ca2+ on Ca2+-Dependent Exocytosis

Aim: Neurotransmitter release is elicited by an elevation of intracellular Ca 2+ concentration ([Ca 2+] i). The action potential triggers Ca 2+ influx through Ca 2+ channels which causes local changes of [Ca 2+] i for vesicle release. However, any direct role of extracellular Ca 2+ (besides Ca 2+ influx) on Ca 2+-dependent exocytosis remains elusive. Here we set out to investigate this possibility on rat dorsal root ganglion (DRG) neurons and chromaffin cells, widely used models for studying vesicle exocytosis. Results: Using photolysis of caged Ca 2+ and caffeine-induced release of stored Ca 2+, we found that extracellular Ca 2+ inhibited exocytosis following moderate [Ca 2+]i rises (2–3 mM). The IC50 for extracellular Ca 2+ inhibition of exocytosis (ECIE) was 1.38 mM and a physiological reduction (,30%) of extracellular Ca 2+ concentration ([Ca 2+]o) significantly increased the evoked exocytosis. At the single vesicle level, quantal size and release frequency were also altered by physiological [Ca 2+] o. The calcimimetics Mg 2+,Cd 2+, G418, and neomycin all inhibited exocytosis. The extracellular Ca 2+-sensing receptor (CaSR) was not involved because specific drugs and knockdown of CaSR in DRG neurons did not affect ECIE. Conclusion/Significance: As an extension of the classic Ca 2+ hypothesis of synaptic release, physiological levels of extracellular Ca 2+ play dual roles in evoked exocytosis by providing a source of Ca 2+ influx, and by directly regulatin

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO