The combination of advanced photoelectric detectors has rendered single-band camouflage materials ineffective,necessitating the development of infrared multispectral camouflage.However,the design and fabrication of ex...The combination of advanced photoelectric detectors has rendered single-band camouflage materials ineffective,necessitating the development of infrared multispectral camouflage.However,the design and fabrication of existing works remain complex as they usually require the integration of multiscale structures.Here,we introduce phase modulation into the infrared camouflage metasurfaces with metal-dielectric-metal configuration,enabling them to achieve camouflage across more bands.Based on this strategy,a simple but effective single-layer cascaded metasurface is demonstrated for the first time to achieve low reflection at multi-wavelength lasers,low infrared radiation in atmospheric windows,and broadband thermal management.As a proof-of-concept,a 4-inch sample with a minimum linewidth of 1.8μm is fabricated using photolithography.The excellent infrared multispectral camouflage performance is verified in experiments,showing low reflectance in 0.9–1.6μm,low infrared emissivity in mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)bands,and high absorptance at the wavelength of 10.6μm.Meanwhile,broadband high emissivity in 5–8μm can provide high-performance radiative heat dissipation.When the input power is 1.57 W·cm^(-2),the surface/radiation temperature of the metasurface decreases by 5.3℃/18.7℃ compared to the reference.The proposed metasurface may trigger further innovation in the design and application of compact multispectral optical devices.展开更多
In order to overcome the existing disadvantages of offline laser shock peening detection methods, an online detection method based on acoustic wave signals energy is provided. During the laser shock peening, an acoust...In order to overcome the existing disadvantages of offline laser shock peening detection methods, an online detection method based on acoustic wave signals energy is provided. During the laser shock peening, an acoustic emission sen- sor at a defined position is used to collect the acoustic wave signals that propagate in the air. The acoustic wave signal is sampled, stored, digitally filtered and analyzed by the online laser shock peening detection system. Then the system gets the acoustic wave signal energy to measure the quality of the laser shock peening by establishing the correspondence between the acoustic wave signal energy and the laser pulse energy. The surface residual stresses of the samples are measured by X-ray stress analysis instrument to verify the reliability. The results show that both the surface residual stress and acoustic wave signal energy are increased with the laser pulse energy, and their growth trends are consistent. Finally, the empirical formula between the surface residual stress and the acoustic wave signal energy is established by the cubic equation fitting, which will provide a theoretical basis for the real-time online detection of laser shock peening.展开更多
Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh ...Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.展开更多
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi...With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.展开更多
Obtaining unsteady hydrodynamic performance is of great significance for seaplane design.Common methods for obtaining unsteady hydrodynamic performance data include tank test and Computational Fluid Dynamics(CFD)numer...Obtaining unsteady hydrodynamic performance is of great significance for seaplane design.Common methods for obtaining unsteady hydrodynamic performance data include tank test and Computational Fluid Dynamics(CFD)numerical simulation,which are costly and time-consuming.Therefore,it is necessary to obtain unsteady hydrodynamic performance in a low-cost and high-precision manner.Due to the strong nonlinearity,complex data distribution,and temporal characteristics of unsteady hydrodynamic performance,the prediction of it is challenging.This paper proposes a Temporal Convolutional Diffusion Model(TCDM)for predicting the unsteady hydrodynamic performance of seaplanes given design parameters.Under the framework of a classifier-free guided diffusion model,TCDM learns the distribution patterns of unsteady hydrodynamic performance data with the designed denoising module based on temporal convolutional network and captures the temporal features of unsteady hydrodynamic performance data.Using CFD simulation data,the proposed method is compared with the alternative methods to demonstrate its accuracy and generalization.This paper provides a method that enables the rapid and accurate prediction of unsteady hydrodynamic performance data,expecting to shorten the design cycle of seaplanes.展开更多
The accuracy of numerical computation heavily relies on appropriate meshing,whichserves as the foundation for numerical computation.Although adaptive refinement methods areavailable,an adaptive numerical solution is l...The accuracy of numerical computation heavily relies on appropriate meshing,whichserves as the foundation for numerical computation.Although adaptive refinement methods areavailable,an adaptive numerical solution is likely to be ineffective if it originates from a poorly ini-tial mesh.Therefore,it is crucial to generate meshes that accurately capture the geometric features.As an indispensable input in meshing methods,the Mesh Size Function(MSF)determines the qual-ity of the generated mesh.However,the current generation of MSF involves human participation tospecify numerous parameters,leading to difficulties in practical usage.Considering the capacity ofmachine learning to reveal the latent relationships within data,this paper proposes a novel machinelearning method,Implicit Geometry Neural Network(IGNN),for automatic prediction of appro-priate MSFs based on the existing mesh data,enabling the generation of unstructured meshes thatalign precisely with geometric features.IGNN employs the generative adversarial theory to learnthe mapping between the implicit representation of the geometry(Signed Distance Function,SDF)and the corresponding MSF.Experimental results show that the proposed method is capableof automatically generating appropriate meshes and achieving comparable meshing results com-pared to traditional methods.This paper demonstrates the possibility of significantly decreasingthe workload of mesh generation using machine learning techniques,and it is expected to increasethe automation level of mesh generation.展开更多
The extraordinary Super Typhoon(STY)Muifa(2022)made landfall four times and had a significant impact on the coastal regions from south to north of China.Although previous studies have demonstrated the‘pumping effect&...The extraordinary Super Typhoon(STY)Muifa(2022)made landfall four times and had a significant impact on the coastal regions from south to north of China.Although previous studies have demonstrated the‘pumping effect'of typhoons on the enhancement of reactive nitrogen(Nr)wet deposition over the ocean,it is uncertain how Nr deposition is influenced by typhoons thatmake prolonged mechanism due tomultiple landfalls.In this study,theNr wet deposition induced by STYMuifawas investigated fromthe perspective of in-and below-cloud processes based on the Nested Air Quality Prediction Modeling System with an online tracer-tagging module.High volume of Nr wet deposition caused by Muifa migrated from south to north,passing over half of China's coastal cities.Compared to the typhoon generated vicinity,both mean values of the oxidized and reduced nitrogen wet deposition over the Typhoon affected regions were increased about 20.4 and 66.1 times after landfall even with the similar rainfall.Emissions from the four landfall areas of China contributed to the majority of Nr wet deposition with significantly enhanced proportion of in-cloud deposition.The strong pumping effect of typhoon to the Nr deposition along the coastal areas and the risk of ecosystem effects requires further researches and higher demands on the control of nitrogen emissions of National Industrial Park,which usually located in China's coastal cities.展开更多
High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to ...High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN' method provides robust and steady convergence compared to the ‘‘DG+SA' method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.展开更多
Increased nitrogen （N） deposition will often lead to a decline in species richness in grassland ecosystems but the shifts in functional groups and plant traits are still poorly understood in China. A field experimen...Increased nitrogen （N） deposition will often lead to a decline in species richness in grassland ecosystems but the shifts in functional groups and plant traits are still poorly understood in China. A field experiment was conducted at Duolun, Inner Mongolia, China, to investigate the effects of N addition on a temperate steppe ecosystem. Six N levels （0, 3, 6, 12, 24, and 48 g N/（m2-a）） were added as three applications per year from 2005 to 2010. Enhanced N deposition, even as little as 3 g N/（m2.a） above ambient N deposition （1.2 g N/（m2.a））, led to a decline in species richness of the whole community. Increasing N addition can significantly stimulate aboveground biomass of perennial bunchgrasses （PB） but decrease perennial forbs （PF）, and induce a slight change in the biomass of shrubs and semi-shrubs （SS）. The biomass of annuals （AS） and perennial rhizome grasses （PR） accounts for only a small part of the total biomass. Species richness of PF decreased significantly with increasing N addition rate but there was a little change in the other functional groups. PB, as the dominant functional group, has a relatively higher height than others. Differences in the response of each functional group to N addition have site-specific and species-specific characteristics. We initially infer that N enrichment stimulated the growth of PB, which further suppressed the growth of other functional groups.展开更多
Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find t...Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find the global optima. In this paper, a new hybrid optimization framework based on Differential Evolution and Invasive Weed Optimization（IWO_DE/Ring） is developed, which combines global and local search to improve the performance, where a Multiple-Output Gaussian Process（MOGP） is used as the surrogate model. We first use several test functions to verify the performance of the IWO_DE/Ring method, and then apply the optimization framework to a supercritical airfoil design problem. The convergence and the robustness of the proposed framework are compared against some other optimization methods. The IWO_DE/Ringbased approach provides much quicker and steadier convergence than the traditional methods.The results show that the stability of the dynamic optimization process is an important indication of the confidence in the obtained optimum, and the proposed optimization framework based on IWO_DE/Ring is a reliable and promising alternative for complex aeronautical optimization problems.展开更多
Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate s...Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.展开更多
Methane （OH4）, carbon dioxide （CO2） and nitrous oxide （N2O） are known to be major greenhouse gases that contribute to global warming. To identify the flux dynamics of these greenhouse gases is, therefore, of gre...Methane （OH4）, carbon dioxide （CO2） and nitrous oxide （N2O） are known to be major greenhouse gases that contribute to global warming. To identify the flux dynamics of these greenhouse gases is, therefore, of great significance. In this paper, we conducted a comparative study on an alpine grassland and alpine wetland at the Bayinbuluk Grassland Eco-system Research Station, Chinese Academy of Sciences. By using opaque, static, manual stainless steel chambers and gas chromatography, we measured the fluxes of CH4, N2O and CO2 from the grassland and wetland through an in situ monitoring study from May 2010 to October 2012. The mean flux rates of CH4, N2O and CO2 for the experimental alpine wetland in the growing season （from May to October） were estimated at 322.4 μg/（m2.h）, 16.7 μg/（m2.h） and 76.7 mg/（m2.h）, respectively; and the values for the alpine grassland were -88.2 μg/（m2.h）, 12.7 μg/（m2.h）, 57.3 mg/（m2.h）, respectively. The gas fluxes showed large seasonal and annual variations, suggesting weak fluxes in the non-growing season. The relationships between these gas fluxes and environmental factors were analyzed for the two alpine ecosystems. The results showed that air temperature, precipitation, soil temperature and soil moisture can greatly influence the fluxes of CH4, N2O and CO2, but the alpine grassland and alpine wetland showed different feedback mechanisms under the same climate and environmental conditions.展开更多
Western Yunnan is a region with intensive tectonic activity and serious earthquake risk. It is of significant importance to study three dimensional crustal structure of this region to understand the tectonic setting a...Western Yunnan is a region with intensive tectonic activity and serious earthquake risk. It is of significant importance to study three dimensional crustal structure of this region to understand the tectonic setting and disaster mechanism. Densification and digitalization of seismic networks in this region provides an opportunity to study the velocity structure with bulletin data. In this study, we collect P-wave data of 10 403 regional earthquakes recorded by 79 seismic stations from January 2008 to December 2010. In addition to first arrivals data （Pg with epieentral distance less than 200 km and Pn）, the Pg （or P） data with epicentral distance more than 200 km are also considered as later direct arrivals in the tomographic inversion. We also compare the quantity and the quality of the seismic data before 2010 and after 2010. The test results show that adding the follow-up Pg phase can effectively improve the inversion ability of crustal imaging, and quantity and the data quality are significantly improved since 2010. The tomographie results show that： （1） The Honghe fault zone, which is the major fault systems in this region, may cut through the entire crust, and the velocity contrasts between two sides at lower crust beneath the Honghe fault are estimated at higher than 10%, while the velocity difference below Nujiang fault zone extends only in the upper crust; （2） Most of the earthquakes in the region occurred at the interface of high-velocity media and low-velocity media, i.e., the areas with high velocity gradient, which has been validated in other areas.展开更多
The hilly area of Southwest China is a typical rice production area which is limited by seasonal droughts and low temperature in the early rice growth period.A field experiment was conducted on three typical paddy fie...The hilly area of Southwest China is a typical rice production area which is limited by seasonal droughts and low temperature in the early rice growth period.A field experiment was conducted on three typical paddy fields(low-lying paddy field,medium-elevation paddy field,and upland paddy field)in this region.Nitrogen(N)treatment(180 kg N ha-1 year-1)was compared to a control treatment(0 kg N ha-1 year-1)to evaluate the effects of integrated rice management(IRM)on rice growth,grain yield,and N utilization.Integrated rice management integrated raised beds containing plastic mulch,furrow irrigation,and triangular transplanting.In comparison to traditional rice management(TRM),IRM promoted rice tiller development,with 7–13 more tillers per cluster at the maximum tillering stage and 1–6 more tillers per cluster at the end of tillering stage.Integrated rice management significantly increased the rice aboveground biomass by 34.4%–109.0%in different growth periods and the aboveground N uptake by 25.3%–159.0%.Number of productive tillers significantly increased by 33.0%,resulting in a 33.0%increase in grain yield and 8.0%improvement of N use efficiency(NUE).Grain yields were significantly increased in all three paddy fields assessed,with IRM being the most important factor for grain yield and productive tiller development.Effects of paddy field type and N level on N uptake by aboveground plants were reflected in the rice reproductive growth period,with the effects of IRM more striking due to the dry climate conditions.In conclusion,IRM simultaneously improved rice yield and NUE,presenting a valuable rice management technique in the paddy fields assessed.展开更多
China has the world’s highest nitrogen(N)application rate,and the lowest N use efficiency(NUE).With the crop yield increasing,serious N pollution is also caused.An in-situ field experiment(2011–2015)was conducted to...China has the world’s highest nitrogen(N)application rate,and the lowest N use efficiency(NUE).With the crop yield increasing,serious N pollution is also caused.An in-situ field experiment(2011–2015)was conducted to examine the effects of three N levels,0(i.e.,no fertilizer N addition to soil),120,and 180 kg N ha-1,using integrated rice management(IRM).We investigated rice yield,aboveground N uptake,and soil surface N budget in a hilly region of Southwest China.Compared to traditional rice management(TRM),IRM integrated raised beds,plastic mulch,furrow irrigation,and triangular transplanting,which significantly improved rice grain yield,straw biomass,aboveground N uptake,and NUE.Integrated rice management significantly improved 15N recovery efficiency(by 10%)and significantly reduced the ratio of potential15N loss(by 8%–12%).Among all treatments,the 120 kg N ha-1 level under IRM achieved the highest 15N recovery efficiency(32%)and 15N residual efficiency(29%),with the lowest 15N loss ratio(39%).After rice harvest,the residual N fertilizer did not achieve a full replenishment of soil N consumption,as the replenishing effect was insufficient(ranging from-31 to-49 kg N ha-1).Furthermore,soil surface N budget showed a surplus(69–146 kg N ha-1)under all treatments,and the N surplus was lower under IRM than TRM.These results indicate IRM as a reliable and stable method for high rice yield and high NUE,while exerting a minor risk of N loss.In the hilly area of Southwest China,the optimized N fertilizer application rate under IRM was found to be 100–150 kg N ha-1.展开更多
Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical simulation.In light of the introduced large amount of computational costs for many adaptive mesh methods,moving m...Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical simulation.In light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational expense.As the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD calculation.However,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is proposed.The regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression model.After the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area decreases.Benchmark examples are first used to verify the feasibility and effectiveness of the proposed method.And then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our method.Results show that the proposed method significantly improves the accuracy of the local flow features on the adaptive meshes.Our work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.展开更多
While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly,aerosol ammonium nitrate remains high in East China.As the high nitrate abundances are...While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly,aerosol ammonium nitrate remains high in East China.As the high nitrate abundances are strongly linked with ammonia,reducing ammonia emissions is becoming increasingly important to improve the air quality of China.Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions,long-term surface observation of ammonia concentrations are sparse.In addition,there is still no consensus on whether agricultural or non-agricultural emissions dominate the urban ammonia budget.Identifying the ammonia source by nitrogen isotope helps in designing a mitigation strategy for policymakers,but existing methods have not been well validated.Revisiting the concentration measurements and identifying source apportionment of atmospheric ammonia is thus an essential step towards reducing ammonia emissions.展开更多
Room temperature sodium-sulfur(RT Na-S)batteries are gaining extensive attention as attractive alternatives for large-scale energy storage,due to low cost and high abundancy of sodium and sulfur in nature.However,the ...Room temperature sodium-sulfur(RT Na-S)batteries are gaining extensive attention as attractive alternatives for large-scale energy storage,due to low cost and high abundancy of sodium and sulfur in nature.However,the dilemmas regarding soluble polysulfides(Na_(2)Sn,4<n<8)and the inferior reaction kinetics limit their practical application.To address these issues,we report the activated porous carbon fibers(APCF)with small sulfur molecules(S2-4)confined in ultramicropores,to achieve a reversible single-step reaction in RT Na-S batteries.The mechanism is investigated by the in situ UV/vis spectroscopy,which demonstrates Na2S is the only product during the whole discharge process.Moreover,the hierarchical carbon structure can enhance areal sulfur loading without sacrificing the capacity due to thorough contact between electrolyte and sulfur electrode.As a consequence,the APCF electrode with 38 wt%sulfur(APCF-38S)delivers a high initial reversible specific capacity of 1412 mAh g^(-1) and 10.6mAh cm^(-2)(avg.areal sulfur loading:7.5 mg cm^(-2))at 0.1 C(1C=1675 mA g^(-1)),revealing high degree of sulfur utilization.This study provides a new strategy for the development of high areal capacity RT Na-S batteries.展开更多
基金financial supports from the National Natural Science Foundation of China(Grant Nos.51925503&52105575)the Fundamental Research Funds for the Central Universities(Grant No.QTZX23063)+2 种基金the Aeronautical Science Foundation of China(Grant No.2022Z073081001)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20232028)the Open Research Funds of State Key Laboratory of Intelligent Manufacturing Equipment and Technology(Grant No.IMETKF2024008).
文摘The combination of advanced photoelectric detectors has rendered single-band camouflage materials ineffective,necessitating the development of infrared multispectral camouflage.However,the design and fabrication of existing works remain complex as they usually require the integration of multiscale structures.Here,we introduce phase modulation into the infrared camouflage metasurfaces with metal-dielectric-metal configuration,enabling them to achieve camouflage across more bands.Based on this strategy,a simple but effective single-layer cascaded metasurface is demonstrated for the first time to achieve low reflection at multi-wavelength lasers,low infrared radiation in atmospheric windows,and broadband thermal management.As a proof-of-concept,a 4-inch sample with a minimum linewidth of 1.8μm is fabricated using photolithography.The excellent infrared multispectral camouflage performance is verified in experiments,showing low reflectance in 0.9–1.6μm,low infrared emissivity in mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)bands,and high absorptance at the wavelength of 10.6μm.Meanwhile,broadband high emissivity in 5–8μm can provide high-performance radiative heat dissipation.When the input power is 1.57 W·cm^(-2),the surface/radiation temperature of the metasurface decreases by 5.3℃/18.7℃ compared to the reference.The proposed metasurface may trigger further innovation in the design and application of compact multispectral optical devices.
基金This study was co-supported by National Natural Science Foundation of China （51501219）, National Key Development Program of China （2016YFB 1192704）, NSFC -Liaoning Province United Foundation （U 1608259） and National Key Technology Research and Development Program of the Ministry of Science and Technology of China （2015BAFOBBO 1-01）.
文摘In order to overcome the existing disadvantages of offline laser shock peening detection methods, an online detection method based on acoustic wave signals energy is provided. During the laser shock peening, an acoustic emission sen- sor at a defined position is used to collect the acoustic wave signals that propagate in the air. The acoustic wave signal is sampled, stored, digitally filtered and analyzed by the online laser shock peening detection system. Then the system gets the acoustic wave signal energy to measure the quality of the laser shock peening by establishing the correspondence between the acoustic wave signal energy and the laser pulse energy. The surface residual stresses of the samples are measured by X-ray stress analysis instrument to verify the reliability. The results show that both the surface residual stress and acoustic wave signal energy are increased with the laser pulse energy, and their growth trends are consistent. Finally, the empirical formula between the surface residual stress and the acoustic wave signal energy is established by the cubic equation fitting, which will provide a theoretical basis for the real-time online detection of laser shock peening.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002 and 2019ZA052011)。
文摘Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011).
文摘With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.
基金supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011)the National Natural Science Foundation of China(No.12472236).
文摘Obtaining unsteady hydrodynamic performance is of great significance for seaplane design.Common methods for obtaining unsteady hydrodynamic performance data include tank test and Computational Fluid Dynamics(CFD)numerical simulation,which are costly and time-consuming.Therefore,it is necessary to obtain unsteady hydrodynamic performance in a low-cost and high-precision manner.Due to the strong nonlinearity,complex data distribution,and temporal characteristics of unsteady hydrodynamic performance,the prediction of it is challenging.This paper proposes a Temporal Convolutional Diffusion Model(TCDM)for predicting the unsteady hydrodynamic performance of seaplanes given design parameters.Under the framework of a classifier-free guided diffusion model,TCDM learns the distribution patterns of unsteady hydrodynamic performance data with the designed denoising module based on temporal convolutional network and captures the temporal features of unsteady hydrodynamic performance data.Using CFD simulation data,the proposed method is compared with the alternative methods to demonstrate its accuracy and generalization.This paper provides a method that enables the rapid and accurate prediction of unsteady hydrodynamic performance data,expecting to shorten the design cycle of seaplanes.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002 and 2019ZA052011)。
文摘The accuracy of numerical computation heavily relies on appropriate meshing,whichserves as the foundation for numerical computation.Although adaptive refinement methods areavailable,an adaptive numerical solution is likely to be ineffective if it originates from a poorly ini-tial mesh.Therefore,it is crucial to generate meshes that accurately capture the geometric features.As an indispensable input in meshing methods,the Mesh Size Function(MSF)determines the qual-ity of the generated mesh.However,the current generation of MSF involves human participation tospecify numerous parameters,leading to difficulties in practical usage.Considering the capacity ofmachine learning to reveal the latent relationships within data,this paper proposes a novel machinelearning method,Implicit Geometry Neural Network(IGNN),for automatic prediction of appro-priate MSFs based on the existing mesh data,enabling the generation of unstructured meshes thatalign precisely with geometric features.IGNN employs the generative adversarial theory to learnthe mapping between the implicit representation of the geometry(Signed Distance Function,SDF)and the corresponding MSF.Experimental results show that the proposed method is capableof automatically generating appropriate meshes and achieving comparable meshing results com-pared to traditional methods.This paper demonstrates the possibility of significantly decreasingthe workload of mesh generation using machine learning techniques,and it is expected to increasethe automation level of mesh generation.
基金supported by the National Natural Science Foundation of China(Nos.42122049 and 42377104)the Basic Strengthening Research Program(No.2021-JCJQ-JJ-1058)+1 种基金the Strategy Priority Research Programof Chinese Academy of Sciences(No.XDB0760403)the National Key Scientific and Technological Infrastructure project"Earth System Science Numerical Simulator Facility"(EarthLab)the Innovation Foundation of CPML/CMA(No.2024CPML-C027).
文摘The extraordinary Super Typhoon(STY)Muifa(2022)made landfall four times and had a significant impact on the coastal regions from south to north of China.Although previous studies have demonstrated the‘pumping effect'of typhoons on the enhancement of reactive nitrogen(Nr)wet deposition over the ocean,it is uncertain how Nr deposition is influenced by typhoons thatmake prolonged mechanism due tomultiple landfalls.In this study,theNr wet deposition induced by STYMuifawas investigated fromthe perspective of in-and below-cloud processes based on the Nested Air Quality Prediction Modeling System with an online tracer-tagging module.High volume of Nr wet deposition caused by Muifa migrated from south to north,passing over half of China's coastal cities.Compared to the typhoon generated vicinity,both mean values of the oxidized and reduced nitrogen wet deposition over the Typhoon affected regions were increased about 20.4 and 66.1 times after landfall even with the similar rainfall.Emissions from the four landfall areas of China contributed to the majority of Nr wet deposition with significantly enhanced proportion of in-cloud deposition.The strong pumping effect of typhoon to the Nr deposition along the coastal areas and the risk of ecosystem effects requires further researches and higher demands on the control of nitrogen emissions of National Industrial Park,which usually located in China's coastal cities.
基金co-supported by the Aeronautical Science Foundation of China (Nos. 20151452021and 20152752033)the National Natural Science Foundation of China (No. 61732006)
文摘High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN' method provides robust and steady convergence compared to the ‘‘DG+SA' method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.
基金supported by the One Hundred Person Project of Chinese Academy of Sciencesthe National Natural Science Foundation of China (40771188,41071151)+1 种基金the Innovative Group Grants from NSFC (30821003)the Sino-German project (DFG Research Training Group,GK1070)
文摘Increased nitrogen （N） deposition will often lead to a decline in species richness in grassland ecosystems but the shifts in functional groups and plant traits are still poorly understood in China. A field experiment was conducted at Duolun, Inner Mongolia, China, to investigate the effects of N addition on a temperate steppe ecosystem. Six N levels （0, 3, 6, 12, 24, and 48 g N/（m2-a）） were added as three applications per year from 2005 to 2010. Enhanced N deposition, even as little as 3 g N/（m2.a） above ambient N deposition （1.2 g N/（m2.a））, led to a decline in species richness of the whole community. Increasing N addition can significantly stimulate aboveground biomass of perennial bunchgrasses （PB） but decrease perennial forbs （PF）, and induce a slight change in the biomass of shrubs and semi-shrubs （SS）. The biomass of annuals （AS） and perennial rhizome grasses （PR） accounts for only a small part of the total biomass. Species richness of PF decreased significantly with increasing N addition rate but there was a little change in the other functional groups. PB, as the dominant functional group, has a relatively higher height than others. Differences in the response of each functional group to N addition have site-specific and species-specific characteristics. We initially infer that N enrichment stimulated the growth of PB, which further suppressed the growth of other functional groups.
基金supported by the Aeronautical Science Foundation of China (Nos.20151452021 and 20152752033)the National Natural Science Foundation of China (No.61300159)+1 种基金the Natural Science Foundation of Jiangsu Province of China (No.BK20130808)China Postdoctoral Science Foundation (No.2015M571751)
文摘Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find the global optima. In this paper, a new hybrid optimization framework based on Differential Evolution and Invasive Weed Optimization（IWO_DE/Ring） is developed, which combines global and local search to improve the performance, where a Multiple-Output Gaussian Process（MOGP） is used as the surrogate model. We first use several test functions to verify the performance of the IWO_DE/Ring method, and then apply the optimization framework to a supercritical airfoil design problem. The convergence and the robustness of the proposed framework are compared against some other optimization methods. The IWO_DE/Ringbased approach provides much quicker and steadier convergence than the traditional methods.The results show that the stability of the dynamic optimization process is an important indication of the confidence in the obtained optimum, and the proposed optimization framework based on IWO_DE/Ring is a reliable and promising alternative for complex aeronautical optimization problems.
基金supported by the funding of the Key Laboratory of Aerodynamic Noise Control(No.ANCL20190103)the State Key Laboratory of Aerodynamics,China(No.SKLA20180102)+1 种基金the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD).
文摘Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.
基金funded by the National Basic Research Program of China (2009CB825103)the National Natural Science Foundation of China (41340041)the West Light Foundation of the Chinese Academy of Sciences (XBBS201206)
文摘Methane （OH4）, carbon dioxide （CO2） and nitrous oxide （N2O） are known to be major greenhouse gases that contribute to global warming. To identify the flux dynamics of these greenhouse gases is, therefore, of great significance. In this paper, we conducted a comparative study on an alpine grassland and alpine wetland at the Bayinbuluk Grassland Eco-system Research Station, Chinese Academy of Sciences. By using opaque, static, manual stainless steel chambers and gas chromatography, we measured the fluxes of CH4, N2O and CO2 from the grassland and wetland through an in situ monitoring study from May 2010 to October 2012. The mean flux rates of CH4, N2O and CO2 for the experimental alpine wetland in the growing season （from May to October） were estimated at 322.4 μg/（m2.h）, 16.7 μg/（m2.h） and 76.7 mg/（m2.h）, respectively; and the values for the alpine grassland were -88.2 μg/（m2.h）, 12.7 μg/（m2.h）, 57.3 mg/（m2.h）, respectively. The gas fluxes showed large seasonal and annual variations, suggesting weak fluxes in the non-growing season. The relationships between these gas fluxes and environmental factors were analyzed for the two alpine ecosystems. The results showed that air temperature, precipitation, soil temperature and soil moisture can greatly influence the fluxes of CH4, N2O and CO2, but the alpine grassland and alpine wetland showed different feedback mechanisms under the same climate and environmental conditions.
基金supported by China National Special Fund for Earthquake Scientific Research in Public Interest (Grant 201208004)National Natural Science Foundation of China (grant 41174040)Scientific Research Institutes’ Basic Research and Development Operations Special Fund of Institute of Geophysics,China Earthquake Administration (grant DQJB10A01)
文摘Western Yunnan is a region with intensive tectonic activity and serious earthquake risk. It is of significant importance to study three dimensional crustal structure of this region to understand the tectonic setting and disaster mechanism. Densification and digitalization of seismic networks in this region provides an opportunity to study the velocity structure with bulletin data. In this study, we collect P-wave data of 10 403 regional earthquakes recorded by 79 seismic stations from January 2008 to December 2010. In addition to first arrivals data （Pg with epieentral distance less than 200 km and Pn）, the Pg （or P） data with epicentral distance more than 200 km are also considered as later direct arrivals in the tomographic inversion. We also compare the quantity and the quality of the seismic data before 2010 and after 2010. The test results show that adding the follow-up Pg phase can effectively improve the inversion ability of crustal imaging, and quantity and the data quality are significantly improved since 2010. The tomographie results show that： （1） The Honghe fault zone, which is the major fault systems in this region, may cut through the entire crust, and the velocity contrasts between two sides at lower crust beneath the Honghe fault are estimated at higher than 10%, while the velocity difference below Nujiang fault zone extends only in the upper crust; （2） Most of the earthquakes in the region occurred at the interface of high-velocity media and low-velocity media, i.e., the areas with high velocity gradient, which has been validated in other areas.
基金supported by the National Key Research and Development Program of China(Nos.2017YFD0301705 and 2018YFD0301203)the Innovation Ability Enhancement Nonprofit Research Deepening Project of Sichuan Province Financial Department,China(No.016GYSH-021)+1 种基金the Youth Foundation of Sichuan Academy of Agricultural Sciences,China(No.2015QNJJ-016)National Nonprofit Industry Research of China(No.201103003)
文摘The hilly area of Southwest China is a typical rice production area which is limited by seasonal droughts and low temperature in the early rice growth period.A field experiment was conducted on three typical paddy fields(low-lying paddy field,medium-elevation paddy field,and upland paddy field)in this region.Nitrogen(N)treatment(180 kg N ha-1 year-1)was compared to a control treatment(0 kg N ha-1 year-1)to evaluate the effects of integrated rice management(IRM)on rice growth,grain yield,and N utilization.Integrated rice management integrated raised beds containing plastic mulch,furrow irrigation,and triangular transplanting.In comparison to traditional rice management(TRM),IRM promoted rice tiller development,with 7–13 more tillers per cluster at the maximum tillering stage and 1–6 more tillers per cluster at the end of tillering stage.Integrated rice management significantly increased the rice aboveground biomass by 34.4%–109.0%in different growth periods and the aboveground N uptake by 25.3%–159.0%.Number of productive tillers significantly increased by 33.0%,resulting in a 33.0%increase in grain yield and 8.0%improvement of N use efficiency(NUE).Grain yields were significantly increased in all three paddy fields assessed,with IRM being the most important factor for grain yield and productive tiller development.Effects of paddy field type and N level on N uptake by aboveground plants were reflected in the rice reproductive growth period,with the effects of IRM more striking due to the dry climate conditions.In conclusion,IRM simultaneously improved rice yield and NUE,presenting a valuable rice management technique in the paddy fields assessed.
基金supported by the National Key Research and Development Program of China(Nos.2017YFD0301705 and 2018YFD0301203)the Innovation Ability Enhancement Nonprofit Research Deepening Project of Sichuan Province Financial Department,China(No.016GYSH-021)+1 种基金the Youth Foundation of Sichuan Academy of Agricultural Sciences,China(No.2015QNJJ-016)the Open Project of State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences(No.Y20160039)
文摘China has the world’s highest nitrogen(N)application rate,and the lowest N use efficiency(NUE).With the crop yield increasing,serious N pollution is also caused.An in-situ field experiment(2011–2015)was conducted to examine the effects of three N levels,0(i.e.,no fertilizer N addition to soil),120,and 180 kg N ha-1,using integrated rice management(IRM).We investigated rice yield,aboveground N uptake,and soil surface N budget in a hilly region of Southwest China.Compared to traditional rice management(TRM),IRM integrated raised beds,plastic mulch,furrow irrigation,and triangular transplanting,which significantly improved rice grain yield,straw biomass,aboveground N uptake,and NUE.Integrated rice management significantly improved 15N recovery efficiency(by 10%)and significantly reduced the ratio of potential15N loss(by 8%–12%).Among all treatments,the 120 kg N ha-1 level under IRM achieved the highest 15N recovery efficiency(32%)and 15N residual efficiency(29%),with the lowest 15N loss ratio(39%).After rice harvest,the residual N fertilizer did not achieve a full replenishment of soil N consumption,as the replenishing effect was insufficient(ranging from-31 to-49 kg N ha-1).Furthermore,soil surface N budget showed a surplus(69–146 kg N ha-1)under all treatments,and the N surplus was lower under IRM than TRM.These results indicate IRM as a reliable and stable method for high rice yield and high NUE,while exerting a minor risk of N loss.In the hilly area of Southwest China,the optimized N fertilizer application rate under IRM was found to be 100–150 kg N ha-1.
基金co-supported by the Key Laboratory of Aerodynamic Noise Control,China(No.ANCL20190103)the State Key Laboratory of Aerodynamics,China(No.SKLA20180102)the Aeronautical Science Foundation of China(Nos.2018ZA52002 and 2019ZA052011)。
文摘Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical simulation.In light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational expense.As the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD calculation.However,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is proposed.The regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression model.After the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area decreases.Benchmark examples are first used to verify the feasibility and effectiveness of the proposed method.And then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our method.Results show that the proposed method significantly improves the accuracy of the local flow features on the adaptive meshes.Our work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.
基金supported by the National Key Research and Development Program of China(Grant No.2017YFC0210100)National Research Program for Key Issues in Air Pollution Control(Grant No.DQGG0208)+1 种基金the National Natural Science Foundation of China(Grant No.41405144)WWW acknowledges support from the Atmospheric and Geospaces Sciences U.S.National Science Foundation(Grant No.AGS 1351932)。
文摘While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly,aerosol ammonium nitrate remains high in East China.As the high nitrate abundances are strongly linked with ammonia,reducing ammonia emissions is becoming increasingly important to improve the air quality of China.Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions,long-term surface observation of ammonia concentrations are sparse.In addition,there is still no consensus on whether agricultural or non-agricultural emissions dominate the urban ammonia budget.Identifying the ammonia source by nitrogen isotope helps in designing a mitigation strategy for policymakers,but existing methods have not been well validated.Revisiting the concentration measurements and identifying source apportionment of atmospheric ammonia is thus an essential step towards reducing ammonia emissions.
基金Natural Science Foundation of Jiangsu Province,Grant/Award Number:BK20170036National Natural Science Foundation of China,Grant/Award Numbers:51572129,51772154,51811530100+1 种基金the Materials Characterization Facility of Nanjing University of Science and Technology for XRD,SEM,and TEM experiments.This study was supported by National Natural Science Foundation of China(Nos.51572129,51772154,and 51811530100)Natural Science Foundation of Jiangsu Province(No.BK20170036).
文摘Room temperature sodium-sulfur(RT Na-S)batteries are gaining extensive attention as attractive alternatives for large-scale energy storage,due to low cost and high abundancy of sodium and sulfur in nature.However,the dilemmas regarding soluble polysulfides(Na_(2)Sn,4<n<8)and the inferior reaction kinetics limit their practical application.To address these issues,we report the activated porous carbon fibers(APCF)with small sulfur molecules(S2-4)confined in ultramicropores,to achieve a reversible single-step reaction in RT Na-S batteries.The mechanism is investigated by the in situ UV/vis spectroscopy,which demonstrates Na2S is the only product during the whole discharge process.Moreover,the hierarchical carbon structure can enhance areal sulfur loading without sacrificing the capacity due to thorough contact between electrolyte and sulfur electrode.As a consequence,the APCF electrode with 38 wt%sulfur(APCF-38S)delivers a high initial reversible specific capacity of 1412 mAh g^(-1) and 10.6mAh cm^(-2)(avg.areal sulfur loading:7.5 mg cm^(-2))at 0.1 C(1C=1675 mA g^(-1)),revealing high degree of sulfur utilization.This study provides a new strategy for the development of high areal capacity RT Na-S batteries.
