RB-DEM Modeling and Simulation of Non-Persisting Rough Open Joints Based on the IFS-Enhanced Method

When the geological environment of rock masses is disturbed,numerous non-persisting open joints can appear within it.It is crucial to investigate the effect of open joints on the mechanical properties of rock mass.However,it has been challenging to generate realistic open joints in traditional experimental tests and numerical simulations.This paper presents a novel solution to solve the problem.By utilizing the stochastic distribution of joints and an enhanced-fractal interpolation system(IFS)method,rough curves with any orientation can be generated.The Douglas-Peucker algorithm is then applied to simplify these curves by removing unnecessary points while preserving their fundamental shape.Subsequently,open joints are created by connecting points that move to both sides of rough curves based on the aperture distribution.Mesh modeling is performed to construct the final mesh model.Finally,the RB-DEM method is applied to transform the mesh model into a discrete element model containing geometric information about these open joints.Furthermore,this study explores the impacts of rough open joint orientation,aperture,and number on rock fracture mechanics.This method provides a realistic and effective approach for modeling and simulating these non-persisting open joints.

Fracture of two three-dimensional parallel internal cracks in brittle solid under ultrasonic fracturing

Similar to hydraulic fracturing(HF), the coalescence and fracture of cracks are induced within a rock under the action of an ultrasonic field, known as ultrasonic fracturing(UF). Investigating UF is important in both hard rock drilling and oil and gas recovery. A three-dimensional internal laser-engraved crack(3D-ILC) method was introduced to prefabricate two parallel internal cracks within the samples without any damage to the surface. The samples were subjected to UF. The mechanism of UF was elucidated by analyzing the characteristics of fracture surfaces. The crack propagation path under different ultrasonic parameters was obtained by numerical simulation based on the Paris fatigue model and compared to the experimental results of UF. The results show that the 3D-ILC method is a powerful tool for UF research.Under the action of an ultrasonic field, the fracture surface shows the characteristics of beach marks and contains powder locally, indicating that the UF mechanism includes high-cycle fatigue fracture, shear and friction, and temperature load. The two internal cracks become close under UF. The numerical result obtained by the Paris fatigue model also shows the attraction of the two cracks, consistent with the test results. The 3D-ILC method provides a new tool for the experimental study of UF. Compared to the conventional numerical methods based on the analysis of stress-strain and plastic zone, numerical simulation can be a good alternative method to obtain the crack path under UF.

Effect of Saccharomyces cerevisiae on alfalfa nutrient degradation characteristics and rumen microbial populations of steers fed diets with different concentrate-to-forage ratios

Live yeast（Saccharomyces cerevisiae） constitutes an effective additive for animal production;its probiotic effect may be related to the concentrate-to-forage ratio（CTFR）.The objective of this study was to assess the effects of S.cerevisiae（SC） on fiber degradation and rumen microbial populations in steers fed diets with different levels of dietary concentrate.Ten Simmental × Local crossbred steers（450 ± 50 kg BW） were assigned to a control group or an SC group.Both groups were fed the same basal diet but the SC group received SC supplementation（8 × 10^9 cfu/h/d through the ruminal fistula）following a two-period crossover design.Each period consisted of four phases,each of which lasted 17 d：10 d for dietary adaptation,6 d for degradation study,and 1 d for rumen sample collection.From the 1^（st） to the 4^（th） phase,steers were fed in a stepwise fashion with increasing CTFRs,i.e.,30：70,50：50,70：30,and 90：10.The kinetics of dry matter and fiber degradation of alfalfa pellets were evaluated;the rumen microbial populations were detected using real-time PCR.The results revealed no significant（P〉 0.05） interactions between dietary CTFR and SC for most parameters.Dietary CTFR had a significant effect（P〈 0.01） on degradation characteristics of alfalfa pellets and the copies of rumen microorganism;the increasing concentrate level resulted in linear,quadratic or cubic variation trend for these parameters.SC supplementation significantly（P〈 0.05） affected dry matter（DM） and neutral detergent fiber（NDF）degradation rates（c_（DM）,c_（NDF）） and NDF effective degradability（ED_（NDF））.Compared with the control group,there was an increasing trend of rumen fungi and protozoa in SC group（P 〈 0.1）;copies of total bacteria in SC group were significantly higher（P〈 0.05）.Additionally,percentage of Ruminobacter amylophilus was significantly lower（P〈 0.05）but percentage of Selenomonas ruminantium was significantly higher（P〈 0.05） in the SC group.In a word,dietary CTFR had a significant effect on degradation characteristics of forage and rumen microbial population.S.cerevisiae had positive effects on DM and NDF degradation rate or effective degradability of forage;S.cerevisiae increased rumen total bacteria,fungi,protozoa,and lactate-utilizing bacteria but reduced starch-degrading and lactate-producing bacteria.

A Novel Hybrid Intelligent Prediction Model for Valley Deformation: A Case Study in Xiluodu Reservoir Region, China

The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam,and an accurate prediction of valley deformation(VD)remains a challenging part of risk mitigation.In order to enhance the accuracy of VD prediction,a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising(EEMD-ITD),Differential evolutions—Shuffled frog leaping algorithm(DE-SFLA)and Least squares support vector machine(LSSVM)is proposed.The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD;then,ITD is applied for redundant information denoising on special sub-series,and the denoised deformation is divided into the trend and periodic deformation components.Meanwhile,several relevant triggering factors affecting the VD are considered,from which the input features are extracted by Grey relational analysis(GRA).After that,DE-SFLA-LSSVM is separately performed to predict the trend and periodic deformation with the optimal inputs.Ultimately,the two individual forecast components are reconstructed to obtain the final predicted values.Two VD series monitored in Xiluodu reservoir region are utilized to verify the proposed model.The results demonstrate that:(1)Compared with Discrete wavelet transform(DWT),better denoising performance can be achieved by EEMD-ITD;(2)Using GRA to screen the optimal input features can effectively quantify the deformation response relationship to the triggering factors,and reduce the model complexity;(3)The proposed hybrid model in this study displays superior performance on some compared models(e.g.,LSSVM,Backward Propagation neural network(BPNN),and DE-SFLA-BPNN)in terms of forecast accuracy.

Toward Interoperable Multi‑hazard Modeling: A Disaster Management System for Disaster Model Service Chain

A natural hazard-related disaster event often causes a series of secondary disasters, forming a disaster chain. Modeling the evolution of disaster chains in multihazard scenarios is crucial for risk governance and urban resilience. However, existing multi-hazard models are limited by complex model design and fixed disaster types, making it impossible to ensure flexible reactions to complex and diverse scenarios. This study presents a disaster management system for disaster model service chain(DMSC) to implement interoperable multi-hazard modeling. To achieve efficient model interaction in the DMSC, a management module is designed to normalize heterogeneous single-hazard models based on disaster system theory and the Open Geospatial Consortium standards, enabling them to be accessible,reusable, and interoperable. The normalized models are then adaptively orchestrated through an orchestration module to establish optimal executable DMSCs for different multihazard scenarios. Taking an earthquake disaster chain as a case study, we demonstrate that the disaster management system shows stable and flexible performance for multihazard modeling.

Qinghai-tibetan plateau peatland sustainable utilization under anthropogenic disturbances and climate change

Often referred to as the“Third Pole,”China’s Qinghai-Tibetan Plateau developed large amounts of peatland owing to its unique alpine environment.As a renewable resource,peat helps to regulate the climate as well as performing other important functions.However,in recent years,intensifying climate change and anthropogenic disturbances have resulted in peatland degradation and consequently made sustainable development of peatland more difficult.This review summarizes peatland ecological and economic functions,including carbon sequestration,biodiversity conservation,energy supplies,and ecotourism.It identifies climate change and anthropogenic disturbances as the two key factors attributing to peatland degradation and ecosystem carbon loss.Current problems in environmental degradation and future challenges in peatland management under the effects of global warming are also discussed and highlighted.