Flow Variables Research Articles

Streamflow Prediction Using Complex Networks.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.

Multi-layer non-hydrostatic free-surface flow model with kinematic seafloor for seismic tsunami generation

Earthquake induced tsunamis pose devastating threat to coastal communities worldwide. Accurate description of tsunami generation by kinematic fault rupture is of importance to investigate earthquake events and evaluate tsunami impacts. The paper develops a multi-layer non-hydrostatic model with a kinematic bottom boundary condition and conducts comprehensive validation to examine the model’s capability in resolving seismic tsunami generation. The two-dimensional governing equations of the multi-layer non-hydrostatic free-surface flow system equipped with kinematic seafloor displacement is first introduced in Cartesian coordinates. A combined finite difference and finite volume scheme is utilized to discretize the governing equations with flow variables arranged on a staggered Arakawa C-grid. Application of pressure correction technique to the discretized formulations yields Poisson-type equations from which the non-hydrostatic pressure is solved for the next time step to complete the temporal integration. Based on existing analytical solutions, four groups of tsunami generation cases considering broad ranges of source parameters, including horizontal scale, rise time, and rupture velocity, are designed to demonstrate performance of the proposed non-hydrostatic model with one-, two-, and three-layers as well as the hydrostatic one. Comparison of 59 generation cases including extreme scenarios indicates the non-hydrostatic model performs better than the hydrostatic model in reproducing the entire waveform and predicting the maximum wave amplitude. High modeling accuracy can be achieved through incorporation of more layers. The proposed multi-layer non-hydrostatic model is a powerful tool for investigating earthquake source mechanisms and evaluating coastal tsunami hazards.

Potential Legacy of SWOT Mission for the Estimation of Flow–Duration Curves

Flow–duration curves (FDCs) provide a compact view of the historical variability of river flows, reflecting climate conditions and the main hydrologic features of river basins. The Surface Water and Ocean Topography (SWOT) satellite mission will enable the estimation of river flows globally, by sensing rivers wider than 100 m with a sampling recurrence from 3 to 21 days. This study investigated the lifetime mission potential for FDC estimation through the comparison between remotely-sensed and empirical FDCs. We employed the Global Runoff Data Center dataset and derived SWOT-like river flows by selecting gauging stations of rivers wider than 100 m with more than 10-year long daily river flow time series. Overall, 1200 gauged river cross-sections were examined. For each site, we created a set of 24 SWOT-simulated FDCs (i.e., based on different sampling recurrences, mean biases, and random errors) to be compared against their empirical counterparts through the Nash–Sutcliffe efficiency and the mean relative error. Our results show that climate and the sampling recurrence play a key role on the performance of SWOT-based FDCs. Tropical and temperate climates performed the best, whereas arid climates mostly revealed higher uncertainties, especially for high- and low-flows.

Overview of microbial communities in the surface water of the Seine River to understand their response to climate change and human activities

AbstractDiverse microbial communities play a crucial role in maintaining the proper functioning of river ecosystems and are considered important indicators of river water quality. Although the Seine River being heavily impacted by human activities, little is known about the microbial communities in its surface waters. In order to monitor water quality and promote sustainable use, we studied its microbial diversity using high-throughput sequencing technology, and explored their relationships with physico-chemical properties. The dominant phyla identified were Proteobacteria, Actinobacteriota, Bacteriodota, and Cyanobacteria. The presence of the Alphaproteobacteria and Gammaproteobacteria indicates that the Seine River water nutrient profile is mainly determined by the recalcitrant organic compounds present in WWTP effluents. Bacterial diversity showed significant temporal variability with a highly significant difference in bacterial composition between 2020 and 2021, probably due to variations in water flow favoring Cyanobacteria growth. Summer displayed higher microbial activity and abundance than autumn, attributed to temperature and orthophosphate content. Spatial variation in bacterial composition was observed between sites upstream and downstream of Paris, as well as before and after of the Seine Valenton-WWTP, subject to an accumulation phenomenon and impacted by wastewater treatment. Further assessment of emerging contaminants and other pollutants is required to better understand these variations. These results provide a basic understanding of the microbial community in the Seine River, serving as a reference for assessing the impact of implementing new wastewater disinfection techniques in the near future. Graphical abstract

Effects of tributary inflows on unsteady flow hysteresis and hydrodynamics in the mainstream

ABSTRACT Flooding propagation is a crucial aspect of hydrological monitoring and forecasting. Previous studies have focused on hysteresis in the rating curve, caused by energy loss during flood propagation. However, the impact of tributary inflow on hysteresis downstream remains unclear, leading to inconsistent field observations on whether it strengthens or weakens hysteresis. In this study, we conducted flume experiments to identify the relationship between hysteresis in unsteady flow and the discharge magnitude of the tributary and the unsteady flow period in the mainstream. It was found that the discharge variations in the tributary had a larger influence on hysteresis compared to the periodical variations in the mainstream unsteady flow. Interestingly, the hysteresis of the unsteady flow had an initial strengthening followed by weakening as the tributary discharge increased. When the tributary inflow was low, the widening of the downstream cross-section sharpened the flood wave, increasing the hysteresis. However, as the tributary discharge increased to generate a backwater effect on the mainstream, the pressure gradient flattened flood waves, thereby weakening the hysteresis. This study improves our understanding of how tributary inflow affects flood propagation in the mainstream, offering new insights for flood prediction and control.

The plane piston problem in a weakly gravitating generalized Chaplygin gas

The present study examines the behavior of a plane piston problem as it moves at a constant velocity in a generalized Chaplygin gas in the presence of a weak gravitational field. A systematic perturbation approach is utilized to obtain an analytical solution for the flow field up to the first order of ϵ. Also, the basic state solution is derived in case of weak shock waves. Further, we analyze the distribution patterns of flow variables for different values of α graphically. Moreover, an approximate solution for the strong shock wave has been found and discussed. The acquired solutions are highly influenced by generalized Chaplygin gas and the applied gravity, which is intriguing aspect from both a physical and scientific standpoint.

Steady Boussinesq convection: Parametric analyticity and computation

AbstractSteady solutions to the Navier–Stokes equations with internal temperature forcing are considered. The equations are solved in two dimensions using the Boussinesq approximation to couple temperature and density fluctuations. A perturbative Stokes expansion is used to prove that that steady flow variables are parametrically analytic in the size of the forcing. The Stokes expansion is complemented with analytic continuation, via functional Padé approximation. The zeros of the denominator polynomials in the Padé approximants are observed to agree with a numerical prediction for the location of singularities of the steady flow solutions. The Padé representations not only prove to be good approximations to the true flow solutions for moderate intensity forcing, but are also used to initialize a Newton solver to compute large amplitude solutions. The composite procedure is used to compute steady flow solutions with forcing several orders of magnitude larger than the fixed‐point method developed in previous work.

Approximation of the Pressure-discharge Curve in Inline Drip Irrigation System

The efficiency of drip irrigation systems depends directly on the uniformity of water discharge from emission devices. Ideally, all emitters should discharge equal amounts of water, but variations occur due to hydraulic and manufacturing factors. This study established the pressure-discharge relationship curve and determined emitter flow variation caused by the hydraulic and the manufacturer’s coefficients of variation for 2 and 4 lph inline emitters. The power exponent and constant of the pressure-discharge curve were determined by measuring the emitter flow rates at operating pressures ranging from 0.2 to 3.0 kg/cm². The emitter flow rates of 100 emitters were measured at an operating pressure of 1.0 kg/cm² to determine the manufacturing coefficient (Vm) and emitter flow variation (qvar(m)). The discharge exponent was found to be 0.46 for both emitter flow rates, with proportionality constants of 0.692 for 2 lph and 1.387 for 4 lph emitters respectively. The results showed a strong correlation between pressure and flow rate, with RMSE values of 0.51 and 0.34 lph, and coefficients of determination of 0.988 and 0.991 for 2 and 4 lph emitters, respectively. High manufacturing precision was indicated by low Vm values of 0.0491 for 2 lph and 0.055 for 4 lph emitters, while qvar(m) values were 0.261 for 2 lph and 0.283 for 4 lph emitters. Total coefficient of variation (Vq) values were 0.1 for 2 lph and 0.14 for 4 lph emitters, with total emitter flow variations (qvar) of 0.29 for 2 lph and 0.39 for 4 lph emitters. The study established the pressure-discharge curve for inline drip irrigation systems, emphasizing the critical relationship between pressure and flow rate. The derived chart from pressure discharge relationship is a valuable tool for estimating emitter flow variation due to hydraulic variation within the same subunit. Precise manufacturing and effective management of hydraulic variations are essential for ensuring uniform water distribution, optimizing drip irrigation systems, and ultimately enhancing crop yield and resource utilization.

Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales

Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow.

Knowledge in motion: temporal dynamics of wild food plant use in the Polish-Lithuanian-Belarusian border region

BackgroundUnderstanding how Local Ecological Knowledge (LEK) evolves over time is crucial for fostering social and environmental responsibility. This study aims to develop a conceptual model of plant knowledge circulation, providing insights into the temporal dynamics of LEK in the Polish-Lithuanian-Belarusian border region. It explores the key patterns and driving forces behind changes in the use of wild plants for food.MethodsField research was conducted in 60 rural settlements across Podlasie Voivodeship (Poland), Vilnius Region (Lithuania), and Hrodna Region (Belarus). This included 200 semi-structured interviews and participant observation among two local communities, Lithuanians and Poles. To assess the temporal dynamics of wild food use, we performed a cross-ethnic, cross-border analysis over time, dividing the data into three major temporal dimensions: past, continuous, and recently acquired uses.ResultsOf the 72 wild plant taxa reported by Poles or Lithuanians in the Polish-Lithuanian-Belarusian borderland, 47 were continuously used for food, 58 were utilised in the past, and 41 were recently acquired. Cross-country trends were similar, with Poland showing more past uses. Diachronic comparisons between Poles and Lithuanians in each studied country revealed no significant differences. Recently acquired taxa overlapped considerably with those used continuously and in the past. The most diversely utilised taxa showed the greatest overlaps. By observing the movement of specific plant taxa within various time dimensions, we distinguished overlapping flow variations: retention (3 taxa), decay (11), invention (8), stagnation (17), revitalisation (6), re-invention (3), and knowledge in motion (24). Shifts in the use of wild food plants were influenced by changes in environmental conditions, governmental policies, cultural practices, and economic factors.ConclusionThe findings of this study have important implications for improving methods of tracking changes in LEK and enhancing our understanding of the relationship between people and nature. Our results underscore the importance of considering knowledge circulation over time in different directions. Recognising the various stages of knowledge circulation might help in pursuing sustainable solutions that balance the needs of human communities with environmental protection.

A resource prediction method for air traffic cyber-physical-social system

Air traffic is exhibiting the characteristics of large flow, strong coupling, and high time variation. Therefore, the complex network of air traffic is more vulnerable to disturbances. When it is disturbed, the failure of some nodes spreads through dependency relationships in the network, resulting in cascade failure. In the event of a cascade failure, the network may quickly collapse until it is paralyzed, with widespread delays and flight cancellations. The current flow management and deployment methods still remain in the control-oriented stage, which is mainly completed by air traffic controls (ATCs), and lack of accurate flow adjustment and effective utilization of capacity. The whole air traffic system and its peripheral factors are intricate, so human and social factors must be integrated into the control and decision-making of the system. Considering engineering and social factors such as operation environment, social environment, personnel, rules, equipment, and information processing, we analyse the air traffic in a cyber-physical-social system (CPSS). To reflect the actual system behaviour rules, dynamic response, limit state, and so on, the corresponding computational experiment and comprehensive evaluation system are established. Based on neural networks and other technologies, a resource prediction scheme based on task demand is proposed for multi-dimensional resources such as airports, air routes, and ATC, to reduce the cost of system resource scheduling and improve resource utilization through resource prediction and adjustment. Finally, the accuracy of the proposed resource prediction algorithm is verified by theoretical analysis and simulation.

Pressurized liquid extraction followed by high-performance liquid chromatography for determination of beta-ecdysone extracted from Pfaffia glomerata (Spreng.) Pedersen

Pfaffia glomerata (Spreng.) Pedersen has among its main bioactive compounds saponins, with the phytoestroid β-ecdysone as its chemical marker. In this study, pressurized liquid extraction (PLE), a green extraction technique used to obtain bioactive compounds from plants, was employed to extract beta-ecdysone from P. glomerata leaves, stems, and roots. The 22 factorial design was used with the variables temperature (333 K and 353 K) and flow rate (1.5 and 2 mL min−1), pressure (300 Bar), time (60 min), and solvent [ethanol and distilled water (70:30 (v/v)] were kept constant for all parts of the plant. The results of experimental responses demonstrated that the factors temperature and flow rate significantly interfere with the yields of leaf (0.499%), root (0.65%) and stem (0.764%) extracts. The latter presented presents the highest yield compared to the other parts of the plant. HPLC results showed the presence of beta-ecdysone in all parts of the plant with concentrations of β-ecdysone 86.82, 76.53 and 195.86 mg L−1 to leaf, root and stem, respectively. FT Raman results exhibited typical peaks of beta-ecdysone, such as 3310 cm−1, 1654 cm−1, and 1073 cm−1 for all plant parts. Another interesting result was the presence of the peak at 1460 cm−1 in the PLE root extract can be associated with selenium. This foundational knowledge confirms that the PLE extraction process was efficient in obtaining the chemical marker of Pfaffia glomerata in all plant parts.

High-order compact gas-kinetic scheme in arbitrary Lagrangian-Eulerian formulation

This study proposes an extension of the high-order compact gas-kinetic scheme (CGKS) to compressible flow simulation in an arbitrary Lagrangian-Eulerian (ALE) formulation in unstructured mesh. The ALE method is achieved by subdividing arbitrary mesh into tetrahedrons and integrating flux function in a local coordinate system at the cell interface to ensure geometric conservation law. The scheme incorporates a compact reconstruction with third-order accuracy for updating both cell-averaged conservative flow variables and their gradients. HWENO-type nonlinear reconstruction and gradient compression factors are adopted to improve the accuracy and robustness of the scheme. A multi-stage multi-derivative (MSMD) time-stepping method is also implemented to achieve high-order time accuracy with fewer middle stages. The scheme is used to study problems involving moving boundaries. The numerical experiments demonstrate the effectiveness of the scheme in capturing the accurate solutions of both low-speed smooth flow and highly compressible ones with strong shock waves.

Rainfall, peak river flow and flow variability drive spatio‐temporal change in the extent of riparian woodland in an African protected area savanna

AbstractVerbal accounts, supported by limited ground‐based and satellite images, reveal decreasing riparian woodland and a loss of large trees along the rivers of the Kruger National Park (KNP, South Africa) over the last century. A multi‐decadal analysis was conducted to identify trends in extent and possible drivers of riparian woodland change. Aerial and satellite imagery (1936–2018) were used to quantify changes in the extent of riparian woodland at 18 sites on 14 rivers in KNP. These changes were compared in a multivariate time‐series with river flow and local rainfall. Particular attention was paid to cumulative flow effects, as well as the frequency and magnitude of large infrequent disturbances such as droughts and floods. Riparian tree cover fluctuated over the time period, and the trajectory of change varied between sites. Most (11) sites experienced a decrease in overall riparian tree cover over ~80 years, with these declines being significant at six sites. Peak flow and maximum rainfall events were strongly associated with these decreases, indicating that flood events are potentially the biggest driver of tree loss from the system. Indeed, the mega‐flood event of 2000 and subsequent large floods have resulted in substantial declines in riparian woodland extent in recent decades. Alternatively, flow variability and cumulative rainfall significantly influenced woodland expansion in isolated cases. With global change models predicting more erratic rainfall and an increased likelihood of large infrequent disturbances, together with increasing demands to abstract more water, the long‐term future of these dynamic habitats and their associated biota here is uncertain.

Active Willis metamaterials with programmable density and stiffness

Investigation and implementation of Active Willis Metamaterials (AWM) have been done exclusively, in all the available literature, by approaches that do not rely on any solid control theory basis. When coupled with piezoelectric control elements, the available approaches have not included, from the first principles, the exact form of the constitutive relationship of the piezoelectric materials. Furthermore, in all these approaches, stability analysis, robustness, ability to accommodate uncertainty or parameter changes, or consideration of disturbance rejection has not been addressed at all. More importantly, the available formulations have always mixed the flow and effort variables of the AWM, resulting in a form that is totally incompatible for the use in generating, investigating, or even designing any appropriate sensing or control applications of the material. In this paper, the piezoelectric-based AWM is modeled, from the first principles, to develop a constitutive coupling form that enables its use in actuation, sensing, and as an integrated controller that can be analyzed, designed, and optimized using the classical, optimal, and robust control system theories. Lagrange dynamics formulation is used to generate the equations governing the Willis coupling, the piezoelectric coupling, as well as the active robust controller. With this developed controlled-based structure of the AWM, the inherent and powerful capabilities of the AWM that lie in its ability to robustly control the material properties themselves such as the compliance (or stiffness) and specific volume (or density) are demonstrated in great detail via several numerical examples. Controlling these properties enables the AWM to be used in numerous important and imaginative applications such as cloaking, beam shifting, beam focusing, as well as many other applications that are limited only by our imagination.

Effect of non-equilibrium thermochemistry on Pitot pressure measurements in shock tunnels (or: Is 0.92 really the magic number?)

Pitot pressure is the most common measurement in high total enthalpy shock tunnels for test condition verification. Nozzle calculations using multi-temperature non-equilibrium thermochemistry are needed in conjunction with Pitot measurements to quantify freestream properties. Pitot pressure is typically matched by tuning the boundary layer transition location in these simulations. However, non-equilibrium thermochemistry effects on the Pitot probe are commonly ignored. A computational study was undertaken to estimate the effect of non-equilibrium thermochemistry on Pitot pressure and freestream conditions. The test flow was produced by a Mach 7 nozzle in a reflected shock tunnel for air at a relatively low total enthalpy of 2.67MJ/kg. Three different thermochemical models (equilibrium, finite-rate chemistry and two-temperature thermochemistry) were employed to compute flow variables at the nozzle exit and Pitot probe. Pitot pressures from these simulations were compared against those obtained via experiments. The results show a departure from the commonly utilized C of 0.92 in the reduced Rayleigh-Pitot equation form for high Mach numbers. Additionally, calculations were done with a sweep of free-stream conditions and resulting in values for one- and two-temperature models to use in future shock tunnel studies. Overall, our results show that the influence of finite-rate thermochemistry should be taken into account, even at relatively low flow enthalpies.

Optimizing heat transport in a permeable cavity with an isothermal solid block: Influence of nanoparticles volume fraction and wall velocity ratio

Abstract This study examines the influence of wall velocity ratio on mixed convective heat transport in a permeable cavity containing an isothermal solid block at its center. The analysis considers the characteristics of various flow variables, i.e., Darcy number, wall velocity ratio, Richardson number, and volume fraction of suspended nanoparticles, on heat transport and material flow characteristics. The principal equations are solved implementing the semi-implicit method for pressure linked equations algorithm, and the outcomes are compared with existing literature. The study shows that rising estimations of Darcy number, velocity ratio, Richardson number, and nanoparticles volume fraction lead to improved heat transfer rates. For example, at high Richardson number (100) and solid volume fraction (0.05), increasing the velocity ratio from 0.5 to 1.5 results in a 6% (5%) upsurge in heat transport rate. Conversely, at smaller Richardson number (0.01), the heat transport rate upsurges by 29% (28%). Similarly, at high Darcy numbers and low wall velocity ratios, a 3% (4%) escalate in heat transport rate is observed with an increase in nanoparticles concentration from 0 to 0.05, while a 9% (8%) increase in thermal performance is achieved at low Darcy numbers. The study emphasizes the importance of optimizing the combination of nanoparticles volume fraction, Darcy number, velocity ratio, and Richardson number to maximize thermal performance in the porous cavity.

Numerical Simulation and Experimental Study of the Rotor–Stator Interaction of a Turbine Under Variable Flow Coefficients

Numerical Simulation and Experimental Study of the Rotor–Stator Interaction of a Turbine Under Variable Flow Coefficients

Urban Traffic Mobility Optimization Model: A Novel Mathematical Approach for Predictive Urban Traffic Analysis

This research introduces the Urban Traffic Mobility Optimization Model (UTMOM), a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling. Designed to decode the complexities of urban mobility patterns, UTMOM meticulously evaluates daily traffic dynamics with a focus on reducing discrepancies and underscoring variations in traffic intensity, particularly during peak times. Our findings unveil pivotal insights into the differences across datasets, providing a substantial contribution to the realms of traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of comprehending fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM’s precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments.

A Preconditioner-Based Data-Driven Polynomial Expansion Method: Application to Compressor Blade With Leading Edge Uncertainty

Abstract In engineering practice, the amount of measured data is often scarce and limited, posing a challenge in uncertainty quantification (UQ) and propagation. Data-driven polynomial chaos (DDPC) is an effective way to tackle this challenge. However, the DDPC method faces problems from the lack of robustness and convergence difficulty. In this paper, a preconditioner-based data-driven polynomial chaos (PDDPC) method is developed to deal with UQ problems with scarce measured data. Two numerical experiments are used to validate the computational robustness, convergence property, and application potential in case of scarce data. Then, the PDDPC is first applied to evaluate the uncertain impacts of real leading edge (LE) errors on the aerodynamic performance of a two-dimensional compressor blade. Results show that the overall performance of compressor blade is degraded and there is a large performance dispersion at off-design incidence conditions. The actual blade performance has a high probability of deviating from the nominal performance. Under the influence of uncertain LE geometry, the probability distributions of the total pressure loss coefficient and static pressure ratio have obvious skewness characteristics. Compared with the PDDPC method, the UQ results obtained by the fitted Gaussian and Beta probability distributions seriously underestimate the performance dispersion of compressor blade. The mechanism analysis illustrates that the large flow variation around the leading edge is the main reason for the overall performance degradation and the fluctuations of the entire flow field.