Inflow Measurements Research Articles

Blade Surface Pressure Measurements in the Field and Their Usage for Aerodynamic Model Validation

ABSTRACTThis study presents results from a long‐term measurement campaign on a research wind turbine in the field. Pressure measurements are conducted at 25% blade radius over several months. Together with inflow measurements provided by a LiDAR system, they form an extensive dataset, which is used in the validation of numerical aerodynamic models. The model validation is conducted based on both ten‐minute average data as well as time‐resolved unsteady data. Initially, it is investigated how representative ten‐minute average pressure measurements are of the underlying unsteady aerodynamics. Binned ten‐minute average pressure distributions are then analysed together with their numerical counterpart, consisting of a combination of rotor and airfoil level aerodynamic/aeroelastic simulation results using average environmental and operating conditions as input. Finally, time‐resolved measurements and simulation results are compared, validating the aeroelastic tools' capability to reproduce unsteady aerodynamics.Overall, reasonable agreement is found between numerical simulations and field experiment data showcasing two aspects: Numerical tools based on blade element momentum theory and panel methods with viscous‐inviscid interaction remain relevant for simulating modern multi‐megawatt wind turbines, and long‐term pressure measurements provide invaluable means for validating such tools.

Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons

Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

Validation of new and existing methods for time-domain simulations of turbulence and loads

We seek to obtain a second-by-second match between the simulated and measured structural loads of a utility-scale wind turbine. To obtain the one-to-one load simulations, we start with the furthest upstream component of the modeling chain: the turbulent inflow. We consider new and existing methods to generate constrained-turbulence flow fields. The new method is based on large-eddy simulations (LES) and machine learning (ML). The existing methods include Kaimal-based TurbSim and the superstatistical wind field model. The inflow measurements used to constrain these simulations are obtained with a nacelle-mounted scanning lidar. We compare the flow fields for the different inflow simulation approaches and validate their associated load predictions against measurements collected in the Rotor Aero-dynamics, Aeroelastics, and Wake (RAAW) field campaign. We find that the rotor-position control developed for this study is key in enabling the time match between measurements and simulations. When this control approach is used, the load simulation performance tracks with the inflow simulation fidelity, with LES+ML yielding errors ≤ 4% for the damage-equivalent loads of flapwise bending moment, and tower fore-aft bending moments.

Validating low- and high-fidelity simulations of a yawed 8 MW wind turbine against measurements

This paper presents a comparison study of the aerodynamic behavior of an 8 MW wind turbine operating under yawed conditions using field measurements, Blade Element Momentum (BEM) theory and Computational Fluid Dynamics (CFD). Turbulent inflow wind field measurements are used to generate an IEC-compliant turbulence box upstream of the wind turbine that is used within the BEM tool MoWiT and the CFD tool chain by IWES in OpenFOAM. Both simulation approaches take into account blade flexibility using modal reduced, anisotropic beam elements in BEM and Geometrically Exact Beam Therory (GEBT) in CFD. While the focus is being set towards the CFD approach, turbine power output, blade root bending moments and blade tip deflections are analyzed. It is shown that both modeling approaches react differently towards the generated wind field.

Correlation of Blade Loading with SpinnerLidar-Measured Inflow

The Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) project’s main objective was collecting data for validation of aerodynamic and aeroelastic codes for large, flexible rotors. These data come from scanning lidars of the inflow and wake, met tower, profiling lidar, blade deflection from photogrammetry, turbine SCADA data (including root bending loads), and hub-mounted SpinnerLidar inflow measurements. The goal of the present work is to analyze various methods to align the SpinnerLidar inflow data in time and space with individual blade loading. These methods would prove a way of analyzing turbine response while estimating the flowfield at each blade and provide a way of improving turbine response understanding using field data in real time, not just from simulations. The hub-mounted SpinnerLidar measures the inflow in the rotor frame meaning the locations of the blades relative to the measurement pattern do not change. The present work outlines some methods for correlating the SpinnerLidar inflow measurements with root bending loads in the rotor frame of reference accounting for both changes in wind speed and rotor speed from the measurement location one diameter upstream to each blade.

Constrained synthetic wind fields from high-resolution 3D WindScanner measurements

The limited spatial and temporal resolution of wind measurements within the inflow of a wind turbine requires statistical modeling of synthetic wind fields, integrating actual measurements or derived statistics along with selected turbulence models. The short-range WindScanner technology enhances atmospheric wind measurements with high resolution in both spatial and temporal dimensions. This contribution utilizes 3D turbulent inflow measurements from three synchronized WindScanner to generate synthetic turbulent wind fields. Both, mean wind field parameters and turbulent time series are analyzed, and different input configurations for the constrained wind field generation are evaluated.Our findings indicate the presence of periods characterized by dominant horizontal shear and veer events, with wind speed and direction differences up to 1.53 ms−1 or 8.45° across the rotor span. Additionally, the study reveals that the optimal measurement configuration for constrained turbulence modeling varies depending on the specific evaluated location and velocity component being analyzed. Another observation is that excessive constraints, placed too closely, may lead to overfitting, thereby diminishing the representativeness of the synthetic field for the lateral velocity component.

Data-driven forecasting of FOWT dynamics and load time series using lidar inflow measurements

This study focuses on forecasting the fairlead tension and floater dynamics time series of floating offshore wind turbines (FOWTs) using a data-driven approach that incorporates onboard sensor measurements and lidar inflow data. Sensors on FOWTs can provide data on turbine dynamics, such as rotational and translational movements, and load metrics like mooring line loads. However, these sensors are limited to current state measurements and do not provide future signal projections. In this research, we investigate a data-driven forecasting methodology using a simulated dataset. This dataset encompasses FOWT responses to diverse environmental conditions and the associated lidar measurements. Utilizing a Long Short-Term Memory (LSTM) sequence-to-sequence model, this study forecasts the time series of fairlead tension, surge, and pitch for forecasting horizons of 20, 40, and 60 seconds, considering lidar ranges from 100m to 500m. The performance of these forecasting models is benchmarked against a simple persistence model. The results indicate that incorporating lidar inflow measurements significantly improves the forecasts of fairlead tensions and platform motions. The enhancement for pitch motion forecasts is observed across all forecasting horizons. For fairlead tension and surge motion, the enhancement is observed for the longer horizons of 40s and 60s. These findings underscore the value of lidar data in accurate forecasting and emphasize the need to account for the interplay between lidar range, wind speed, and forecasting horizon to achieve optimal forecast accuracy.

Determinants and effects of microvascular obstruction on serial change in left ventricular diastolic function after reperfused acute myocardial infarction.

Although left ventricular (LV) diastolic dysfunction is more related to functional capacity after acute myocardial infarction (AMI), the determinants of LV diastolic functional change after reperfused AMI remain unknown. This study aimed to investigate the effects of microvascular obstruction (MVO) on mid-term changes in LV diastolic function after reperfused AMI. In a cohort of 72 AMI patients who underwent successful revascularization, echocardiography and cardiovascular magnetic resonance imaging were repeated at 9-month intervals. The late gadolinium enhancement (LGE) amount, segmental extracellular volume fraction, global LV, and left atrial (LA) phasic functions, along with mitral inflow and tissue Doppler measurements, were repeated. Among the included patients, 31 (43%) patients had MVO. During the 9-month interval, LV ejection fraction (EF) and LV global longitudinal strain (GLS) were significantly improved in accordance with a decrease in LGE amount (from 18.2 to 10.3 g, p < 0.001) and LV mass. The deceleration time (DT) of early mitral inflow (188.6 ms-226.3 ms, p < 0.001) and LV elastance index (Ed; 0.133 1/ml-0.127 1/ml, p = 0.049) were significantly improved, but not in conventional diastolic functional indexes. Their improvements occurred in both groups; however, the degree was less prominent in patients with MVO. The degree of decrease in LGE amount and increase in LVEF was significantly correlated with improvement in LV-Ed or LA phasic function, but not with conventional diastolic functional indexes. In patients with reperfused AMI, DT of early mitral inflow, phasic LA function, and LV-Ed were more sensitive diastolic functional indexes. The degree of their improvement was less prominent in patients with MVO.

Antecedent snowmelt and orographic precipitation contributions to water supply of Pakistan disastrous floods, 2022

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%–40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.

Real-time yaw-misalignment calibration and field-test verification of wind turbine via machine learning methods

It has become a general consensus that nacelle-mounted LiDAR can be used to calibrate the yaw misalignment or drive the real-time yaw motions for wind turbines, which would improve the power-generation efficiency. The advantage of LiDAR utilization is that the accuracy of inflow wind measurement would be greatly improved, while its disadvantage is that the cost remains high and the data validity is not sufficiently high. In this paper, an efficient machine learning method for estimating LiDAR measurement is developed to establish the real-time yaw calibration framework and sustain the LiDAR rolling utilization. Firstly, the correlation of LiDAR measurement with SCADA features is analyzed to estimate LiDAR measurement using only SCADA data. Secondly, several machine learning algorithms are studied for performance comparison, and the dependence of each algorithm on data size is also analyzed. Experimental results show that, the proposed XGBoost algorithm has high accuracy, requires less data, and can quickly calibrate the yaw misalignment. Finally, the field testing is held for a commercial 2 MW wind turbine to verify the effectiveness. The field-test results show that the proposed method is feasible for industrial applications and can improve the annual theoretical power generation by 3.66% compared to the situation without calibration, which also provides an executable and economical solution for LiDAR replacement planning.

Characterization of wind turbine flow through nacelle-mounted lidars: a review

This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic view of the inflow and wake characteristics required to characterize and optimize wind turbine performance, carry out model validation and calibration, and aid in real-time control. The need to balance the enhanced capabilities and limitations of lidars compared to traditional anemometers inspired a broad variety of approaches for scan design and wind reconstruction, which we discuss in this review. We give particular emphasis to identifying common guidelines and gaps in the available literature with the aim of providing an exhaustive picture of the state-of-the-art techniques for reconstructing wind plant flow using nacelle-mounted lidars.

Particle trajectories, velocities, accelerations and rotation rates in snow avalanches

Abstract Understanding the dynamics of snow avalanches is crucial for predicting their destructive potential and mobility. To gain insight into avalanche dynamics at a particle level, the AvaNode in-flow sensor system was developed. These synthetic particles, equipped with advanced and affordable sensors such as an inertial measurement unit (IMU) and global navigation satellite system (GNSS), travel with the avalanche flow. This study focuses on assessing the feasibility of the in-flow measurement systems. The experiments were conducted during the winter seasons of 2021–2023, both in static snow cover and dynamic avalanche conditions of medium-sized events. Radar measurements were used in conjunction with the particle trajectories and velocities to understand the behaviour of the entire avalanche flow. The dynamic avalanche experiments allowed to identify three distinct particle flow states: (I) initial rapid acceleration, (II) a steady state flow with the highest velocities (9–17 ms−1), and (III) a longer deceleration state accompanied by the largest measured rotation rates. The particles tend to travel towards the tail of the avalanche and reach lower velocities compared to the frontal approach velocities deduced from radar measurements (ranging between 23–28 ms−1). The presented data give a first insight in avalanche particle measurements.

Atrial mechanical contraction and ambulatory atrioventricular synchrony: Predictors from the OPTIVALL study.

The role that preprocedural factors have on atrioventricular synchrony (AVS) provided by leadless pacemakers requires investigation. We aimed to assess the correlation between mitral inflow echocardiographic parameters and p-wave morphology with the accelerometer A4 signal amplitude. We also sought to identify clinical and echocardiographic predictors of optimal ambulatory AVS (≥85% of cardiac cycles). Forty-three patients undergoing Micra AV implant from June 2020 to March 2023 were prospectively enrolled. Baseline echocardiogram and 12-lead resting ECG were performed. Device follow-up was scheduled at 24 h, 1, 3, and 6 months and yearly after the implant. Ambulatory AVS was studied with a 24 h Holter monitor performed at 3 months follow-up in 35 patients who remained in VDD mode. A4 signal amplitude at 1 month correlated to peak A wave velocity (r = .376; p = .024) at echocardiogram, but no relationship was found with peak A' wave velocity, E/A, or E'/A' ratio. P-wave amplitude in lead I and aVF correlated to A4 signal amplitude at 1- and 3-months follow-up, respectively. Median AVS during 24 h of daily activities was 85.6 ± 7.6% and remained stable up to 100 bpm. Twenty-three out of 35 patients (65.7%) reached optimal ambulatory AVS. There was no association between mitral inflow echocardiographic parameters and optimal AVS. Diabetes (OR: 0.05, 95% CI: 0.01-0.47; p = .009) and chronic obstructive pulmonary disease (COPD) (OR: 0.06, 95% CI: 0.01-0.63; p = .019) strongly predicted ambulatory AVS<85%. Diabetes and COPD should be considered when selecting candidates for Micra AV. Measurements of pulsed wave Doppler mitral inflow do not systematically reflect the behavior of the A4 signal amplitude.

Deep learning phase error correction for cerebrovascular 4D flow MRI

Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the potential of a convolutional neural network (CNN), a form of deep learning, to directly infer the correction vector field. With IRB waiver of informed consent, we retrospectively identified 96 MRI exams from 48 patients who underwent cerebrovascular 4D Flow MRI from October 2015 to 2020. Flow measurements of the anterior, posterior, and venous circulation were performed to assess inflow-outflow error and the benefit of manual image-based phase error correction. A CNN was then trained to directly infer the phase-error correction field, without segmentation, from 4D Flow volumes to automate correction, reserving from 23 exams for testing. Statistical analyses included Spearman correlation, Bland–Altman, Wilcoxon-signed rank (WSR) and F-tests. Prior to correction, there was strong correlation between inflow and outflow (ρ = 0.833–0.947) measurements with the largest discrepancy in the venous circulation. Manual phase error correction improved inflow-outflow correlation (ρ = 0.945–0.981) and decreased variance (p < 0.001, F-test). Fully automated CNN correction was non-inferior to manual correction with no significant differences in correlation (ρ = 0.971 vs ρ = 0.982) or bias (p = 0.82, Wilcoxon-Signed Rank test) of inflow and outflow measurements. Residual background phase error can impair inflow-outflow consistency of cerebrovascular flow volume measurements. A CNN can be used to directly infer the phase-error vector field to fully automate phase error correction.

A spatio-temporality-enabled parallel multi-agent-based real-time dynamic dispatch for hydro-PV-PHS integrated power system

Integrated power system has emerged as a powerful alternative to penetrate renewables, due to its ability to reconcile energy discrepancy. However, due to limited mainstreams and complex mountainous meteorology, the dispatch of hydro-photovoltaic-pumped hydro storage (Hydro-PV-PHS) integrated power system (IPS) which are predominantly composed of cascaded daily-regulation and uncontrollable runoff hydropower stations and PVs still miss the expected clean energy utilization rates. To conquer the issue, a novel spatio-temporality-enabled parallel multi-agent-based dynamic dispatch method is proposed. At the outset, a temporal dispatch model fed by dynamic measurements of available PV generation and inflow is presented. To master the uncertainties, such model must be solved in real-time upon the incoming measurements. Whereupon the presented model is recast as Markov decision process for learning dispatch policies, parameterized by neural network agents. To manage the enormous spatial-temporal operating space of the hydro-PV-PHS IPS and to prevent conflict policies, a long short-term memory auto-encoder (LSTM-AE) combined unsupervised learning scheme is used to alleviate divergence and decrease stochasticity of renewables to pre-uncouple policies, which are then distributed to multiple parallel agents. Finally, distributed proximal policy optimization is conducted to produce dispatch policies in an offline parallel manner, with each agent responsible for dispatching the hydro-PV-PHS IPS within the respective operating subspace. The numerical studies in a real-world case demonstrate that the proposed scheme enables real-time and near-optimal dynamic dispatch for the concern IPS, and outperforms other rivals in terms of adaptability, robustness, and efficiency.

Electrocardiogram to Determine Mitral and Aortic Valve Opening and Closure.

Knowledge of the timing of cardiac valve opening and closing is important in cardiac physiology. The relationship between valve motion and electrocardiogram (ECG) is often assumed, however is not clearly defined. Here we investigate the accuracy of cardiac valve timing estimated using only the ECG, compared to Doppler echocardiography (DE) flow imaging as the gold standard. DE was obtained in 37 patients with simultaneous ECG recording. ECG was digitally processed and identifiable features (QRS, T, P waves) were examined as potential reference points to determine opening and closure of aortic and mitral valves, as compared to DE outflow and inflow measurement. Timing offset of the cardiac valves opening and closure between ECG features and DE was measured from derivation set (n = 19). The obtained mean offset in combination with the ECG features model was then evaluated on a validation set (n = 18). Using the same approach, additional measurement was also done for the right sided valves. From the derivation set, we found a fixed offset of 22 ± 9 ms, 2 ± 13 ms, 90 ± 26 ms, and -2 ± -27 ms when comparing S to aortic valve opening, Tend to aortic valve closure, Tend to mitral valve opening, and R to mitral valve closure respectively. Application of this model to the validation set showed good estimation of aortic and mitral valve opening and closure timing value, with low model absolute error (median of the mean absolute error of the four events = 19 ms compared to the gold standard DE measurement). For the right-sided (tricuspid and pulmonic) valves in our patient set, there was considerably higher median of the mean absolute error of 42 ms for the model. ECG features can be used to estimate aortic and mitral valve timings with good accuracy as compared to DE, allowing useful hemodynamic information to be derived from this easily available test.

Time-Lapse Resistivity Monitoring of a Simulated Runoff Test in a Bioswale, Philadelphia

As part of the reconstruction of Interstate-95 in Philadelphia, a series of vegetated stormwater infiltration basins (bioswales) have been installed to manage highway runoff. To assess these bioswales, we used simulated runoff tests (SRTs), where the bioswale is flooded from a fire hydrant to simulate a major storm event. SRT monitoring relies on point measurements of inflow, outflow, and soil moisture to determine the volume of stormwater the bioswale can handle and the time to recovery. To provide better spatial coverage for site assessment, we tested the use of time-lapse electrical resistivity tomography (ERT) during a SRT. Using an onsite geophysical monitoring station, we performed ERT surveys every 4 h before, during, and after the SRT test. Inflow and outflow measurements taken during the SRT found that a majority of the water did not exit the bioswale via the outlet box. The time-lapse inversion results indicated that runoff uniformly spread throughout the basin before infiltrating into the heterogeneous urban soil below. The ERT demonstrated that the underlying native soil contributed to the overall performance of the bioswale, a contribution which was previously assumed to be minimal. Recovery to pretest soil moisture levels took roughly 2–3 days, according to both soil moisture sensors and time-lapse geophysical data. The SRT results were consistent with natural storm events recorded by our geophysical monitoring station, which have been monitored continuously for over a year. Use of ERT during SRTs characterize the pattern of infiltration and recovery rate of the soil beneath a stormwater control. Additionally, the heterogeneous infiltration observed during the SRT suggested that ERT surveys preconstruction may improve future planning of stormwater controls by guiding the location of infiltration measurements.

Large Permeameter for Continuing Erosion Filter Tests

Abstract The Continuing Erosion Filter (CEF) test and criteria were developed by Foster and Fell to assess severities of internal erosion of an existing embankment dam whose filter does not satisfy modern filter design criteria developed by Sherard and Dunnigan. Those criteria were developed from laboratory tests on materials that may not be directly applicable to the widely graded filter materials. To address this issue, a large diameter (300 mm) rigid-wall permeameter was constructed to assess the internal erosion of zoned embankment dams constructed of widely graded filter materials. The large permeameter allows for testing of embankment dam fill materials up to a maximum particle size of 37.5 mm. A commissioning test program was conducted to verify the test equipment and testing methodology used in this study. Two CEF test results from the test program are reported in this paper to demonstrate the novel features of the permeameter cell design and its test setup. These features include a split-cell design for ergonomic function, real-time outflow rate measurement, inflow and filter specimen water pressure measurements, and erosion loss measurement. A wet method to estimate erosion loss was proposed as an alternative to the traditional oven-drying method.

P-058 IMMEDIATE USE OF PERITONEAL DIALYSIS AFTER SIMULTANEOUS LAPAROSCOPIC PLACEMENT OF PERITONEAL DIALYSIS CATHETER AND INGUINAL HERNIA REPAIR

Abstract Background Peritoneal dialysis is accepted renal placement therapy for end-stage renal disease (ESRD). Managing inguinal hernia in peritoneal dialysis (PD) patient is not standardized. Moreover, the occurrence of occult hernia post PD placement needs surgical planning. This work aims to report the outcome of simultaneous laparoscopic placement od PD catheter and transabdominal preperitoneal (TAPP) repair of inguinal hernia. Methods All patients underwent laparoscopic PD catheter placement for ESRD from September 2016 to July 2021 were reviewed. Patients who underwent Concurrent Laparoscopic Inguinal Herniorraphy and Laparoscopic PD catheter placement were involved. PD with inflow measurement of the amount and should be below the incised peritoneum by 1 inch and smooth inflow and outflow without leakage. The amount raised gradually in two weeks to reach the targeted PD. Results Laparoscopic PD catheter placement was inserted for 13 patients with inguinal hernia. Two patients had bilateral inguinal hernia, one patient with umbilical hernia, and one patient diagnosed with hernia intraoperatively. The safe amount of dialysate fluid was from 400 to 600ml. There was no mortality, intraoperative complications or fluid dialysis leak. One patient had catheter displacement which was repositioned laparoscopically. Three catheters were removed due to non-compliance. No hernia recurrence observed. Conclusion Simultaneous laparoscopic PD catheter placement and TAPP repair herniorrhaphy with immediate use of PD is safe and effective. Our surgical approach allows using minimal invasive technique in the management of both PD catheter placement and inguinal hernia repair without delaying PD.

소규모 하수처리장 유입 유량의 신뢰성에 관한 연구

To provide the basic data necessary to establish maintenance plans for existing sewage treatment plants in severely depopulated areas, the reliability of the inflow measurements for small-scale sewage treatment facilities was investigated. It was found that the population trends in sewage treatment districts and those in administrative districts did not coincide with each other, so the reliability of population-related data was very low. In addition, despite the low possibility of the inflow of water from unknown sources, more than 78% of the surveyed sewage treatment plants reported an inflow that was higher than the amount of tap water used within the treatment area. In particular, for sewage treatment plants with a capacity of 50 ㎥/day or less, the reliability of the flow rate measured using a flow meter installed in the sewage treatment plants was very low because the value calculated based on tap water consumption ranged from about 25% to 120% of the flow rate measured by the flow meter. These results indicate that, in small-scale sewage treatment plants where the reliability of the flowmeter is questionable, it is important to estimate the inflow rate based on the tap water consumption within the treatment area.