Accurate engine performance models are important for model-based performance evaluation of aero engine.The accuracy of the model often depends on engine component maps,so there is a need for a method that can accurate...Accurate engine performance models are important for model-based performance evaluation of aero engine.The accuracy of the model often depends on engine component maps,so there is a need for a method that can accurately correct the component maps of the model over a wide range.In this paper,a new method for modifying component maps is proposed,this method combines the correction of the scaling factors with the solution process of the off-design working point,and uses the adjustment of the variable geometric parameters of the engine to change the position of the working line,in order to obtain more correction results and guarantee high accuracy in a wider range.The method is validated by taking the main fan of the Adaptive Cycle Engine(ACE),an ideal power unit for a new generation of multi-purpose and ultra-wide working range aircraft,as an example.The results show that the maximum error between the corrected component maps and the target maps is less than 1%.New possibility for more precise component maps can be realized in this paper.展开更多
High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NR...High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.展开更多
A data-driven modelling method for predicting the aero-derivative gas turbine start-up performance has been developed. The test data are used to correct the compressor and turbine sub-idle maps based on extrapolation,...A data-driven modelling method for predicting the aero-derivative gas turbine start-up performance has been developed. The test data are used to correct the compressor and turbine sub-idle maps based on extrapolation, enhancing the accuracy within the whole sub-idle range. The hydraulic starter and temperature lag models are concluded in this method. By the start-up component maps, hydraulic power and fuel supply, the start-up process can be simulated, and the performance characteristics of the gas turbine and components can be calculated. The model is verified by three sets of test data on different environmental operation condition. The error of start-up times, speeds, temperatures and pressures between the start-up simulation and test data are within 10%, showing a high modeling accuracy.展开更多
This study evaluates the performance of 15 Coupled Model Intercomparison Project Phase 6(CMIP6)models(before and after downscaling)in simulating autumn precipitation extremes in Southwest China based on a high-resolut...This study evaluates the performance of 15 Coupled Model Intercomparison Project Phase 6(CMIP6)models(before and after downscaling)in simulating autumn precipitation extremes in Southwest China based on a high-resolution,statistically downscaled CMIP6 dataset,using the CN05.1 dataset as a reference.The Bias Correction Constructed Analogues with Quantile mapping reordering(BCCAQ)method used in deriving the downscaled CMIP6 dataset significantly enhances the models'abilities to reproduce the spatial patterns of the extreme precipitation indices,particularly for total precipitation,number of moderate rain days(R10),and number of heavy rain days(R20).Notable improvements are also observed for maximum 1-day precipitation(RX1),maximum 5-day precipitation(RX5),and simple daily intensity index(SDII),alongside reduced inter-model spread and systematic biases.Bias correction also improves the simulation of interannual variability,substantially reducing the root mean square error(RMSE)for total precipitation,R10,and R20.Increased interannual variability in the future is expected for certain indices,spatially concentrated for RX1 and RX5 in the south and R20 in the east.Projections using the bias-corrected multi-model ensemble under the SSP2-4.5 and SSP5-8.5 scenarios indicate a significant intensification of autumn extreme precipitation in both intensity-and frequency-related indices by the 2080s,especially in southern Southwest China,with precipitation becoming more concentrated in heavier events.Consecutive dry days(CDDs)exhibit spatial variability with an observed increase in the southeast,while consecutive wet days(CWDs)shows no significant change.These findings highlight an increased risk of intensified autumn rainfall and altered precipitation patterns in the region under future climate change.展开更多
基金funded by National Nature Science Foundation of China(NSFC)(Nos.51776010,and 91860205)the support from Collaborative Innovation Center of Advanced Aero-Engine,china。
文摘Accurate engine performance models are important for model-based performance evaluation of aero engine.The accuracy of the model often depends on engine component maps,so there is a need for a method that can accurately correct the component maps of the model over a wide range.In this paper,a new method for modifying component maps is proposed,this method combines the correction of the scaling factors with the solution process of the off-design working point,and uses the adjustment of the variable geometric parameters of the engine to change the position of the working line,in order to obtain more correction results and guarantee high accuracy in a wider range.The method is validated by taking the main fan of the Adaptive Cycle Engine(ACE),an ideal power unit for a new generation of multi-purpose and ultra-wide working range aircraft,as an example.The results show that the maximum error between the corrected component maps and the target maps is less than 1%.New possibility for more precise component maps can be realized in this paper.
基金funded by the National Key R&D Program of China(NO.2022YFB3903903)the National Natural Science Foundation of China(NO.41974008,NO.42074045).
文摘High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.
基金the Excellence Research Group Program(ERGP,the former Basic Science Center Program)No.52488101the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA 29050000)for sponsoring the work in this paper.
文摘A data-driven modelling method for predicting the aero-derivative gas turbine start-up performance has been developed. The test data are used to correct the compressor and turbine sub-idle maps based on extrapolation, enhancing the accuracy within the whole sub-idle range. The hydraulic starter and temperature lag models are concluded in this method. By the start-up component maps, hydraulic power and fuel supply, the start-up process can be simulated, and the performance characteristics of the gas turbine and components can be calculated. The model is verified by three sets of test data on different environmental operation condition. The error of start-up times, speeds, temperatures and pressures between the start-up simulation and test data are within 10%, showing a high modeling accuracy.
基金Supported by the Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX0558)Chongqing Meteorological Department Talent Support Project(RCZC-202303)。
文摘This study evaluates the performance of 15 Coupled Model Intercomparison Project Phase 6(CMIP6)models(before and after downscaling)in simulating autumn precipitation extremes in Southwest China based on a high-resolution,statistically downscaled CMIP6 dataset,using the CN05.1 dataset as a reference.The Bias Correction Constructed Analogues with Quantile mapping reordering(BCCAQ)method used in deriving the downscaled CMIP6 dataset significantly enhances the models'abilities to reproduce the spatial patterns of the extreme precipitation indices,particularly for total precipitation,number of moderate rain days(R10),and number of heavy rain days(R20).Notable improvements are also observed for maximum 1-day precipitation(RX1),maximum 5-day precipitation(RX5),and simple daily intensity index(SDII),alongside reduced inter-model spread and systematic biases.Bias correction also improves the simulation of interannual variability,substantially reducing the root mean square error(RMSE)for total precipitation,R10,and R20.Increased interannual variability in the future is expected for certain indices,spatially concentrated for RX1 and RX5 in the south and R20 in the east.Projections using the bias-corrected multi-model ensemble under the SSP2-4.5 and SSP5-8.5 scenarios indicate a significant intensification of autumn extreme precipitation in both intensity-and frequency-related indices by the 2080s,especially in southern Southwest China,with precipitation becoming more concentrated in heavier events.Consecutive dry days(CDDs)exhibit spatial variability with an observed increase in the southeast,while consecutive wet days(CWDs)shows no significant change.These findings highlight an increased risk of intensified autumn rainfall and altered precipitation patterns in the region under future climate change.
