Treatment of singultus following craniocerebral injury Intranasal cavity drip infusion versus intramuscular injection of aminazine

Craniocerebral injury always accompanies with singultus, while frequent singultus may cause increased intracranial pressure. Simultaneously, respiratory alkalosis and cerebral hypoxia induced by respiratory disorder may aggravate craniocerebral injury. OBJECTIVE： To observe the therapeutic effects of intranasal cavity drip infusion of aminazine and intramuscular injection on singultus following craniocerebral injury. DESIGN： Contrast observation. SETTING： Department ofNeurosurgery, Xi＇an Aerospace General Hospital. PARTICIPANTS： A total of 102 patients with singultus following craniocerebral injury were selected from the Department of Neurosurgery, Xi＇an Aerospace General Hospital from June 2001 to June 2006. Patients with craniocerebral injury were diagnosed with CT examination and randomly divided into nasal cavity medication group （n =62） and intramuscular injection group （n =40）. There were 44 males and 18 females in the nasal cavity medication group and their mean age was （33±4） years; while, there were 26 males and 14 females in the intramuscular injection group and their mean age was （29±4） years. All patients and their relatives provided the confirmed consent. METHODS： Patients in the nasal cavity medication group were slowly dripped aminazine solution into bilateral nasal cavity with the dosage of 12.5 mg （0.5 mL）. Patients who had no obvious effect or had mild improvement received the treatment once every 6 hours. The treatment was stopped if symptoms were also observed after the fifth medication. In addition, patients in the intramuscular injection group received intramuscular injection of 50 mg aminazine. Patients who had no obvious effect or had mild improvement received the treatment once every 6 hours. The treatment was changed if symptoms were also observed after the fifth medication. MAIN OUTCOME MEASURES： Therapeutic effects of different medications in the two groups. RESULTS： All 102 patients were involved in the final analysis. Effective rate in the nasal cavity medication group was higher than that in the intramuscular injection group, and there was significant difference （ x^2= 11.882, P 〈 0.01 ）. At 6 hours after onset of singultus, effective rate in the nasal cavity medication group was higher than that in the intramuscular injection group, and there was significant difference （ x^2 =8.188, P 〈 0.01）. CONCLUSION： Therapeutic effects of intranasal cavity drip infusion of aminazine on singultus following craniocerebral injury are superior to those of intramuscular injection.

Modeling Reynolds stress anisotropy invariants via machine learning

The presentation and modeling of turbulence anisotropy are crucial for studying large-scale turbulence structures and constructing turbulence models.However,accurately capturing anisotropic Reynolds stresses often relies on expensive direct numerical simulations(DNS).Recently,a hot topic in data-driven turbulence modeling is how to acquire accurate Reynolds stresses by the Reynolds-averaged Navier-Stokes(RANS)simulation and a limited amount of data from DNS.Many existing studies use mean flow characteristics as the input features of machine learning models to predict high-fidelity Reynolds stresses,but these approaches still lack robust generalization capabilities.In this paper,a deep neural network(DNN)is employed to build a model,mapping from tensor invariants of RANS mean flow features to the anisotropy invariants of high-fidelity Reynolds stresses.From the aspects of tensor analysis and input-output feature design,we try to enhance the generalization of the model while preserving invariance.A functional framework of Reynolds stress anisotropy invariants is derived theoretically.Complete irreducible invariants are then constructed from a tensor group,serving as alternative input features for DNN.Additionally,we propose a feature selection method based on the Fourier transform of periodic flows.The results demonstrate that the data-driven model achieves a high level of accuracy in predicting turbulence anisotropy of flows over periodic hills and converging-diverging channels.Moreover,the well-trained model exhibits strong generalization capabilities concerning various shapes and higher Reynolds numbers.This approach can also provide valuable insights for feature selection and data generation for data-driven turbulence models.

One neural network approach for the surrogate turbulence model in transonic flows

With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.

Machine learning for adjoint vector in aerodynamic shape optimization

Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.