Numerical manifold method for thermo-mechanical coupling simulation of fractured rock mass

As a calculation method based on the Galerkin variation,the numerical manifold method(NMM)adopts a double covering system,which can easily deal with discontinuous deformation problems and has a high calculation accuracy.Aiming at the thermo-mechanical(TM)coupling problem of fractured rock masses,this study uses the NMM to simulate the processes of crack initiation and propagation in a rock mass under the influence of temperature field,deduces related system equations,and proposes a penalty function method to deal with boundary conditions.Numerical examples are employed to confirm the effectiveness and high accuracy of this method.By the thermal stress analysis of a thick-walled cylinder(TWC),the simulation of cracking in the TWC under heating and cooling conditions,and the simulation of thermal cracking of the SwedishÄspöPillar Stability Experiment(APSE)rock column,the thermal stress,and TM coupling are obtained.The numerical simulation results are in good agreement with the test data and other numerical results,thus verifying the effectiveness of the NMM in dealing with thermal stress and crack propagation problems of fractured rock masses.

Evaluation of flue gas desulfurization gypsum as a low-cost precipitant for phosphorus removal from anaerobic digestion effluent filtrate

Land application of anaerobic digestion(AD)effluent as a fertilizer is desirable for nutrient recycling,but often supplies excess phosphorus(P),which contributes to surface water eutrophication.Reducing the P content in AD effluent filtrate using calcium(Ca)treatment prior to land application is a potential strategy for improving effluent disposal and meeting the discharge standard.This study took flue gas desulphurization(FGD)gypsum,a by-product of coal-fired power plants,as a low-cost Ca source,and combined with traditional phosphorus removal agents to achieve high phosphorus removal efficiency with less chemical cost.As the results showed,FGD gypsum dosages of 20 mmol/L Ca(3.44 g/L)and 40 mmol/L Ca(6.89 g/L)removed up to 97.1%of soluble P(initially 102.8 mg/L)within 60-90 minutes.Combining FGD gypsum treatment with traditional chemical treatments using calcium hydroxide[Ca(OH)2]or ferric chloride(FeCl3)could achieve>99%P removal with reduced chemical costs.This study demonstrated that FGD gypsum is an efficient calcium-based precipitant for phosphorus removal,offering a cost-effective and sustainable approach to enhance wastewater treatment practices and meet discharge standards in wastewater management.

Numerical modeling assisting in surgical treatment of total anomalous pulmonary venous connection in children

Objective To develop a model using patient-specific computational fluid dynamics(CFD)to predict the required anastomotic size for total anomalous pulmonary venous connection(TAPVC)surgery and to forecast surgical outcomes.Methods Based on clinical data from patients,a CFD model was used to simulate the anastomosis between pulmonary venous confluence and the left atrium.Blood flow velocity,wall shear stress,power loss,and pressure were calculated using numerical algorithms within the model.Various sizes of anastomosis were applied during the simulation.The energy dissipation at the anastomosis was computed from the results and compared with real-world data.Results As the simulated anastomotic size increased,blood flow velocity,pulmonary venous pressure,and energy loss decreased.However,when the anastomotic size exceeded 18 mm,the efficiency of energy conversion no longer improved.The realistic and simulated velocities matched well for anastomosis sizes ranging from 15 to 20 mm.Conclusion The model can assist surgeons in preoperative planning for determining the anastomotic size in TAPVC surgical treatment.

Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma

Background Preoperative imaging assessment of surgical risk is very important for the prognosis of these children.To develop and validate a radiomics-based machine learning model based on the analysis of radiomics features to predict surgical risk in children with abdominal neuroblastoma(NB).Methods A retrospective study was conducted from April 2019 to March 2021 among 74 children with abdominal NB.A total of 1874 radiomic features in MR images were extracted from each patient.Support vector machines(SVMs)were used to establish the model.Eighty percent of the data were used as the training set to optimize the model,and 20%of the data were used to validate its accuracy,sensitivity,specificity and area under the curve(AUC)to verify its effectiveness.Results Among the 74 children with abdominal NB,55(65%)had surgical risk and 19(35%)had no surgical risk.A t test and Lasso identified that 28 radiomic features were associated with surgical risk.After developing an SVMbased model using these features,predictions were made about whether children with abdominal NB had surgical risk.The model achieved an AUC of 0.94(a sensitivity of 0.83 and a specificity of 0.80)with 0.890 accuracy in the training set and an AUC of 0.81(a sensitivity of 0.73 and a specificity of 0.82)with 0.838 accuracy in the test set.Conclusions Radiomics and machine learning can be used to predict the surgical risk in children with abdominal NB.The model based on 28 radiomic features established by SVM showed good diagnostic efficiency.