A reduced-order model for fast predicting ionized flows of hypersonic vehicles along flight trajectory

An improved Reduced-Order Model(ROM)is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low-dimensional space by performing the Proper Orthogonal Decomposition(POD)on snapshots and is coupled with the Radial Basis Function(RBF)to achieve fast prediction speed.However,due to the disparate scales in the ionized flow field,the conventional ROM usually generates spurious negative errors.Here,this issue is addressed by performing flow-solution preprocessing in logarithmic space to improve the conventional ROM.Then,extra orthogonal polynomials are introduced in the RBF interpolation to achieve additional improvement of the prediction accuracy.In addition,to construct high-efficiency snapshots,a trajectory-constrained adaptive sampling strategy based on convex hull optimization is developed.To evaluate the performance of the proposed fast prediction method,two hypersonic vehicles with classic configurations,i.e.a wave-rider and a reentry capsule,are used to validate the proposed method.Both two cases show that the proposed fast prediction method has high accuracy near the vehicle surface and the free-stream region where the flow field is smooth.Compared with the conventional ROM prediction,the prediction results are significantly improved by the proposed method around the discontinuities,e.g.the shock wave and the ionized layer.As a result,the proposed fast prediction method reduces the error of the conventional ROM by at least 45%,with a speedup of approximately 2.0×105compared to the Computational Fluid Dynamic(CFD)simulations.These test cases demonstrate that the method developed here is efficient and accurate for predicting ionized hypersonic flows.

Artificial intelligence aided design of film cooling scheme on turbine guide vane

In recent years,artificial intelligence(AI)technologies have been widely applied in many different fields including in the design,maintenance,and control of aero-engines.The air-cooled turbine vane is one of the most complex components in aero-engine design.Therefore,it is interesting to adopt the existing AI technologies in the design of the cooling passages.Given that the application of AI relies on a large amount of data,the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning.It includes the parameterized three-dimensional(3-D)geometrical modeling,automatic meshing and computational fluid dynamics(CFD)batch automatic simulation of different film cooling structures through customized developments of UG,ICEM and Fluent.It is demonstrated that the trained artificial neural network(ANN)can predict the cooling effectiveness on the external surface of the turbine vane.The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model.Using this established method,a multi-output model is constructed based on which a simple tool can be developed.It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.

Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model

Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants,thereby creating improved indoor air quality(IAQ).Among various simulation techniques,Markov chain model is a relatively new and efficient method in predicting indoor airborne pollutants.The existing Markov chain model(for indoor airborne pollutants)is basically assumed as first-order,which however is difficult to deal with airborne particles with non-negligible inertial.In this study,a novel weight-factor-based high-order(second-order and third-order)Markov chain model is developed to simulate particle dispersion and deposition indoors under fixed and dynamic ventilation modes.Flow fields under various ventilation modes are solved by computational fluid dynamics(CFD)tools in advance,and then the basic first-order Markov chain model is implemented and validated by both simulation results and experimental data from literature.Furthermore,different groups of weight factors are tested to estimate appropriate weight factors for both second-order and third-order Markov chain models.Finally,the calculation process is properly designed and controlled,so that the proposed high-order(second-order)Markov chain model can be used to perform particle-phase simulation under consecutively changed ventilation modes.Results indicate that the proposed second-order model does well in predicting particle dispersion and deposition under fixed ventilation mode as well as consecutively changed ventilation modes.Compared with traditional first-order Markov chain model,the proposed high-order model performs with more reasonable accuracy but without significant computing cost increment.The most suitable weight factors of the simulation case in this study are found to be(λ_(1)=0.7,λ_(2)=0.3,λ_(3)=0)for second-order Markov chain model,and(λ_(1)=0.8,λ_(2)=0.1,λ_(3)=0.1)for third-order Markov chain model in terms of reducing errors in particle deposition and escape prediction.With the improvements of the efficiency of state transfer matrix construction and flow field data acquisition/processing,the proposed high-order Markov chain model is expected to provide an alternative choice for fast prediction of indoor airborne particulate(as well as gaseous)pollutants under transient flows.