Field Measurement Data Research Articles

Verification and application of the aircraft noise simulation framework J-FRAIN

We newly developed a framework for aircraft noise simulation, J-FRAIN, that can precisely predict the noise from aircraft at ground observation points during the landing approach phase. J-FRAIN enables the incorporation of component-wise regression sound source models created from field measurement data using a microphone array system and the precise prediction of the time history of noise from each aircraft component. To verify the noise prediction accuracy of J-FRAIN, noise prediction of the landing approach phase around an airport was performed. The component-wise regression sound source models created from the data measured at the location just before touch-down was used since its regression model can estimate the sound power levels at flight parameters not only actually measured but also not measured. The predicted result and noise measured by aircraft noise monitoring systems were verified to be in good agreement. As examples of the application using J-FRAIN, two case studies were also discussed; the determination of the contribution of each noise source at observation points and the evaluation of the influence of flight parameter differences on noise levels at observation points.

A machine learning-based approach to predict groundwater nitrate susceptibility using field measurements and hydrogeological variables in the Nonsan Stream Watershed, South Korea

Identifying and predicting the nitrate inflow and distribution characteristics of groundwater is critical for groundwater contamination control and management in rural mixed-land-use areas. Several groundwater nitrate prediction models have been developed; in particular, a nitrate concentration model that uses dissolved ions in groundwater as an input variable can produce accurate results. However, obtaining sufficient chemical data from a target area remains challenging. We tested whether machine learning models can effectively determine nitrate contamination using field-measured data (pH, electrical conductivity, water temperature, dissolved oxygen, and redox potential) and existing geographic information system (GIS) data (lithology, land cover, and hydrogeological properties) from the Nonsan Stream Watershed in South Korea, an area where nitrate contamination occurs owing to intensive agricultural activities. In total, 183 groundwater samples from different wells, mixed municipal sites, and agricultural activities were used. The results indicated that among the four machine learning models (artificial neural network (ANN), classification and regression tree (CART), random forest (RF), and support vector machine (SVM)), the RF (R2: 0.74; RMSE: 3.5) and SVM (R2: 0.80; RMSE: 2.8) achieved the highest prediction accuracy and smallest error in all groundwater parameter estimates. Land cover, aquifer type, and soil drainage were the primary RF and SVM model input variables, representing agricultural activity-related and hydrogeological infiltration effects. Our research found that in rural areas with limited hydro-chemical data, RF and SVM models could be used to identify areas at high risk of nitrate contamination using spatial variability, GIS-aided visualization, and easily accessible field-measured groundwater quality data.

Implementing a Dual-Spectrometer Approach for Improved Surface Reflectance Estimation

Surface reflectance measurement is an integral part of the vicarious calibration of satellite sensors and the validation of satellite-derived top-of-atmosphere (TOA) and surface reflectance products. A well-known practice for estimating surface reflectance is to conduct a field campaign with a spectrometer and a calibration panel, which is labor-intensive and expensive. To address this issue, the Radiometric Calibration Network, RadCalNet, has been developed, which automatically collects surface reflectance over several selected sites. Neither of these approaches can continuously track the atmosphere, which limits their ability to compensate for atmospheric transmittance change during target measurement. This paper presents the dual-spectrometer approach that uses a stationary spectrometer dedicated to continuously tracking changes in atmospheric transmittance by staring at a calibrated reference panel while the mobile spectrometer measures the target. Simultaneous measurement of the reflectance panel and target help to transfer calibration from the stationary spectrometer to the mobile spectrometer and synchronize the measurements. In this manner, atmospheric transmittance changes during target measurement can be tracked and used to reduce the variability of the target surface reflectance. This paper uses field measurement data from combined field campaigns between different calibration groups at Brookings, South Dakota, and Landsat 8 and Landsat 9 underfly efforts over Coconino National Forest, Arizona, and Guymon, Oklahoma. Preliminary results show that even in a clear sky condition, where atmospheric transmittance changes are minimal, the precision of target surface reflectance estimated using the dual-spectrometer approach is 2–6% better than the single-spectrometer approach. The dual-spectrometer approach shows the potential for a substantial improvement in the precision of the target spectral profile when the atmospheric transmittance is changing rapidly during field measurement. Results show that during non-optimal atmospheric conditions, the dual-spectrometer approach improved the precision of the surface reflectance by 50–60% compared to the single-spectrometer approach across most spectral regions. The ability to estimate surface reflectance more precisely using the dual-spectrometer approach in different atmospheric conditions improves the vicarious calibration of optical satellite sensors and the validation of both TOA and surface reflectance products.

Time-averaged flow field reconstruction based on a multifidelity model using physics-informed neural network (PINN) and nonlinear information fusion

PurposeCurrent methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.Design/methodology/approachA multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.FindingsTwo experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.Originality/valueIn this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

Flow resistance co‐efficient of meandering river in alluvial plain and its prediction using artificial neural network

AbstractA proper estimation of flow resistance coefficient of river is essential for precise simulations of river hydraulics. In addition to the cross‐sectional geometry and hydraulic parameters, the alignment of the channel affects the flow resistance coefficient in case of meandering rivers. In the present study, a rigorous field study of 131 km along the Barak River was conducted to assess the influence of meandering on the flow resistance coefficient. The values of flow resistance co‐efficient were calculated using Chezy and Manning's equations with measured field data and the values from both are compared. However, the variation in the flow resistance co‐efficient along the channel calculated from Manning's equation is significantly less as it does not consider the undulation and meandering. Using these field data, an artificial neural network (ANN) model has been developed to predict the cross‐sectional averaged flow resistance for meandering river. The model considered the influence of relative curvature, depth of flow, bed particle size, Froude number and Reynolds number including water temperature for accurate predictions of flow resistance coefficient. The ANN model was tested and validated using 237 field data sample. The values of the statistical parameters indicate a very good fit to the training dataset with coefficient of determination (R2) = 0.9566 for training and good fit for testing with R2 = 0.8131. The developed ANN model has been compared with other model with the same data set to check its applicability.

Analysis of the performance and operation of a photovoltaic-battery heat pump system based on field measurement data

Photovoltaic-heat pump (PV-HP) combinations with battery and energy management systems are becoming increasingly popular due to their ability to increase the autarchy and utilization of self-generated PV electricity. This trend is driven by the ongoing electrification of the heating sector and the growing disparity between growing electricity costs and reducing feed-in tariffs in Germany. Smart control strategies can be employed to control and optimize the heat pump operation to achieve higher self-consumption of PV electricity. This work presents the evaluation results of a smart-grid ready controlled PV-HP-battery system in a single-family household in Germany, using 1-minute-high-resolution field measurement data. Within 12 months evaluation period, a self-consumption of 43 % was determined. The solar fraction of the HP amounts to 36 %, enabled also due to higher set temperatures for space heating and domestic hot water production. Accordingly, the SPF decreases by 4.0 % the space heating and by 5.7 % in the domestic hot water mode. The combined seasonal performance factor for the heat pump system increases from 4.2 to 6.7, when only considering the electricity taken from the grid and disregarding the locally generated electricity supplied from photovoltaic and battery units.

Quantifying the cooling effect of river and its surrounding land use on local land surface temperature: A case study of Bahe River in Xi’an, China

Rapid urbanization and unplanned development have posed a threat to the thermal environment in a country like China. The urban heat island (UHI) phenomenon is one of the most serious issues because of its strong relation to thermal comfort, air pollution, and public health. The water bodies, as an important component of the urban ecosystem, are generally considered a vital resource to mitigate the UHI. This study provides direct evidence with the help of satellite observation and field measurement data using the mono-window algorithm, spatial buffer analysis, and linear regression methods to explore how the stream water affects the local temperature variation. The results showed that Land use/cover change (LUCC) and Land surface temperature (LST) on both banks of the Bahe River changed significantly. The LST of the river was significantly massively reduced within the distance range of 300 to 500 m, and 400 to 600 m, with an average temperature dip from 3.7 to 2.8 °C, and from 3.2 to 1.7 °C respectively were during the summer on east and west river banks. In addition, the surrounding LUCC composition and configuration could strongly affect the maximum cooling scale. The results provide insights for urban planners and managers to arrange the LUCC composition between the river design in urban areas and the cooling effect demands.

Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data

Nitrogen is an essential nutrient in crop growth cycle and directly affects the photosynthesis of crops. The leaf chlorophyll content (LCC) and leaf area index (LAI) are important for characterizing photosynthetic capacity of crops and are critical indicators for diagnosing nitrogen status of crops. Hyperspectral remote sensing technology provided a means to achieve crop LCC and LAI estimation. However, the redundancy of spectral data and canopy structure effect can cause poor robustness of the estimating models, further hindering the development and application of estimating models across different crop species. In this study, a method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops. First, for the spectral dataset generated by the PROSAIL model, a deep neural network was used to construct LCC and LAI estimation models (called DNNCA model). Secondly, for the hyperspectral data collected from field crops (soybean, canola and wheat), incremental learning using regularization (LwF algorithm) was used to update the DNNCA model parameters. Finally, the dilution curve model based on the LCC-LAI anisotropic growth relationship was developed to assess crop nitrogen status. The results showed that: (1) The constrained bi-objective optimizated DNNCA model can consider the interactive effect of LAI and LCC on spectral reflectance, and achieved reliable estimation on PROSAIL simulation data set (LAI:R2 = 0.82, RMSE = 0.77 m2/m2; LCC:R2 = 0.91, RMSE = 6.4 ug/cm2). (2) By incremental learning, DNNCA model has continuous learning capability and stable estimation on cross-crop (canola, soybean and wheat) field-measured data (LAI:R2 = 0.64–0.82, RMSE = 0.58–1.02 m2/m2; LCC:R2 = 0.56–0.82, RMSE = 3.9–10.5 ug/cm2). (3) The relationship between the NNILCC and the NNILNC was significant. The NNILCC derived from the anisotropy relationship between crop LAI and LCC was an effective tool for crop nitrogen status diagnosis. (4) The process of crop LAI, LCC and NNILCC reflected the effect of water and nitrogen supply on crop growth. The appropriate water-nitrogen treatments contributed to LAI increase and LCC accumulation. The study demonstrated that hyperspectral remote sensing technology combined with incremental learning is an effective method for cross-crop growth monitoring and nitrogen diagnosis. These results provide a reference and basis for filed water-nitrogen supply and management.

Optimization Model for Timing of Preventive Maintenance of Asphalt Pavement Based on Decision Trees

The timing of preventive maintenance for asphalt pavement determines the effectiveness and cost-effectiveness of preventive maintenance measures. Firstly, a weighted average method combining subjective and objective factors is used to evaluate the performance index of road sections and select preventive maintenance measures. Secondly, a decision tree model for preventive maintenance of highways in Gansu Province is established using field measurement data to determine the timing of preventive maintenance for road sections. Finally, the model is validated using the example of the G22 Qingdao-Lanzhou Expressway by evaluating the pavement service performance index and the timing of preventive maintenance. The results show that compared to directly establishing decision trees based on uniform standards, the decision tree established using field measurement data reflects the differences in the importance of various decision indicators and respects the objectivity of road data. It improves the poor portability of the original decision tree model and enables more accurate determination of the timing of preventive maintenance.

Experimental Study of Hardening Small Strain Model Parameters for Strata Typical of Zhengzhou and Their Application in Foundation Pit Engineering

The hardening small strain (HSS) model, which considers the non-linear characteristics of the shear modulus of soil in the small strain range, is commonly utilized in the numerical analysis of excavations in sensitive environments. In order to ascertain the parameters of an HSS model for strata typical of Zhengzhou, an extensive series of laboratory tests was executed. Subsequently, a statistical analysis was utilized to establish the relationships between the principal parameters. A three-dimensional finite element model was established based on the excavation of representative strata in Zhengzhou. The accuracy and applicability of the model parameters were verified using the field measurement data. In addition, an analysis of the sensitivity of the main HSS model parameters to the foundation pit deformation was also carried out. The results show that the HSS model can analyze excavation deformation with greater precision than the hardening soil (HS) model or the Mohr–Coulomb (MC) model. The small strain parameters G0ref and γ0.7 have a great influence on the horizontal displacement of the support and the vertical displacement of soil behind the wall, respectively. The research results serve as a reliable foundation for both the analysis of excavation deformation and the determination of HSS model parameters for strata typical of Zhengzhou.

A theory to model pseudo steady state water production and predict long-term water recovery from fractured reservoirs

The environmental and economic impact of hydraulic fracturing technology in the energy industry remains a growing concern due to large water consumption and poor water recovery in return. In this study, we propose a novel theory to describe and model the drive mechanisms (fracture closure and fluid expansion) responsible for long-term water recovery from hydraulically fractured reservoirs. In the literature, pseudo steady state (PSS) flow of water has been explained and modelled under the assumption of negligible fluid influx from matrix, referred to as "supercharge conditions". The preliminary flowback data analysis of 8 different unconventional reservoirs from North America in this study shows 1) water PSS flow signature is also observed after fluid breakthrough into fractures, and 2) it can be modelled mathematically, verified with long-term field data, and validated with numerical simulation results. These observations suggest that oil and water develop different networks or flow paths through fractures. With time, oil network expands and water network shrinks. Therefore, water continues to be produced from the fractures as oil saturation increases in the fractures with expansion of oil network due to fracture pressure decline. Based on this theory, an integrated semi-analytical workflow is developed by accounting for the changes in the volumes and compressibilities of both fracture and fluid. The workflow was applied on early production data (150–200 days) from six multi-fractured horizontal wells in Eagle Ford. The results show that the workflow can use the early-time data to accurately estimate long-term water recovery (325–475 days) from fractured reservoirs (within 6 % of measured field data). However, frequent long shut-ins during the course of long-term production increases the uncertainty in water recovery prediction.

Determination of average growth rate based on statistical relationships using geomorphological and geotechnical variables in predictive debris flow simulations

Growth rate is a crucial constituent parameter for determining basal entrainment rates in debris flow numerical modeling frameworks. This study presents the development of a multiple-regression model to estimate site-specific average growth rates in advance of debris flow event occurrences. A range of geomorphological and geotechnical variables influencing entrainment was selected through an in-depth literature review. A comprehensive dataset from 35 debris flow events in the Gyeonggi province of South Korea was employed to identify statistically significant variables related to the average growth rate. Through correlation analysis, three variables were ultimately applied as predictors for the multiple regression analysis: channel path length, basin area with slope inclinations of 30 % or greater, and median of soil grain-size distribution. The proposed model was statistically validated by demonstrating high coefficients of determination and meeting key assumptions of no-multicollinearity, no-autocorrelation, normality, and homoscedasticity. In a case study of a historical debris flow event that occurred during the 2011 Mt. Woomyeon landslide disaster, the practical relevance of the developed model was tested by comparing two parallel simulation results using the DAN3D numerical model. Different average growth rates were applied to the two parallel simulations, respectively; the target average growth rate calculated using field-measurement data and the predicted average growth rate derived by applying the multiple regression model. The results revealed that the proposed model provided reasonable predictions of debris flow velocities and heights, with only minor discrepancies between the target and predicted average growth rates. Notably, measured average growth rates of immediately adjacent debris flow events significantly differed from that of the case study event. This highlights that adopting entrainment data from neighboring events for debris flow simulations can result in significantly erroneous debris flow hazard predictions. Finally, the limitations and contributions of this study and future research directions are discussed.

How to adequately determine the top height of forest stands based on airborne laser scanning point clouds?

Top height (TH) is one of the most important features of forest stands that is used in forest resource management. Thus far, the TH has been mainly determined in forestry on the basis of field measurements. There are certain accepted theoretical guidelines on how the TH should be determined. However, due to high cost and time consumption, it is difficult to meet these assumptions with traditional field measurement methods. Currently, remote sensing is widely used in forest inventory, also for the determination of TH. However, it is still usually conducted by linking the remote sensing data—most often airborne laser scanning (ALS) point clouds—with ground-based measurements using the area-based approach. Different plot sizes and point cloud metrics are used to determine TH, making it difficult to compare the results. There is therefore a need for a universal methodology for determining TH directly from ALS data – one that is consistent with the methodology used for field measurement data. This paper presents the results of an experiment that was aimed at assessing the accuracy of TH determination on the basis of ALS data. A comparison of the six considered ALS-derived proxies of TH showed that the individual tree detection approach was the most accurate. The bias of this method, when calculated for 48 artificial forest stands, amounted to 0.15 m. The approach based on the lower percentiles of the distribution of point heights significantly underestimated the TH from −0.67 m to up to −1.9 m. On the basis of the obtained results, we claim that TH can be determined, based on ALS data without ground measurements, with an accuracy that is acceptable for forest practitioners and researchers.

Numerical analysis for supporting and deformation of complex foundation pit groups in unstable areas of karst strata

Due to the extensive karst development zone in karst areas, the stability of the underground soil layer is poor. The support of foundation pits in this area will be affected by complex environmental factors, and if not handled properly, it will cause significant safety hazards and economic losses. In this paper, the three-dimensional finite element model of the complex foundation pit and adjacent foundation pit group was established with the help of Midas GTS NX numerical software, and numerical simulation was carried out for the whole foundation pit excavation and construction process, and the deformation results of the adjacent foundation pit support structure of the complex foundation pit group and the soil outside the foundation pit were obtained. The results show that the underground wall of the complex foundation pit shifted to the north under the action of buttresses, with a similar “cantilever” displacement pattern on the south side of the underground wall in the center, a “push-back displacement” on the north side of the underground wall in the center, and a “ventral” displacement pattern at the corners of the grounded wall and on the east and west sides. Combined with the field measurement data, the support piles in the internal support system have a “cantilever” displacement pattern under the condition of massive soil unloading in the vicinity of the foundation pit, while the supporting piles show a “parabolic” displacement pattern under the action of the corner internal support. The deformation characteristics of the foundation pit in the presence of adjacent pits are significantly different from the results based on empirical laws. The results of the above study will provide useful technical guidance for the safety of excavation support for foundation pit groups in complex environments and the reasonable control of the surrounding environment.

Super-resolution reconstruction framework of wind turbine wake: Design and application

Complete and clear global wind turbine wake data is very important for the study of wind turbine wake characteristics in increasingly large offshore wind farms. Existing wake measurement techniques can only obtain local high-resolution (HR) wake flow field, or sacrifice accuracy to obtain larger measurement area, which is insufficient for accurate modeling of wake effect. To overcome this challenge, this paper proposes a novel super-resolution (SR) reconstruction approach that can reconstruct the global HR wake flow field from low-resolution (LR) wake flow field measurement data effectively. The proposed approach utilizes a deep learning framework called down-sampled skip-connection and multi-scale network. The performance of the SR approach is evaluated by enhancing the resolution of the wake flow field at different scale factors, and its potential application is demonstrated by assessing the prediction accuracy of three typical wake models. The results indicate that the resolution of the global wind turbine wake can be improved by 16 times using the SR model, and the reconstructed global SR wake flow fields are consistent with the ground truth in terms of both the spatial distribution and the temporal variation. By comparing the prediction results of three different wake models with the LR or SR wake data, it is shown that the SR flow reconstruction method can be applied to more accurately evaluate the wake model prediction performance, which has the potential to improve wake models. Overall, this study presents an innovative solution to the problem of incomplete and inaccurate wake flow measurement in the wind energy industry, which could reduce the workload of experimental measurements and the cost burden of accurate measuring equipment for engineering applications.

Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis.

Leaf dark respiration (Rd ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax ) at 25°C (Rd,25 , Vcmax,25 ) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25 /Vcmax,25 reflecting night-time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5-yr warming experiment, and spatially using an extensive field-measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25 /Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2 = 0.74) and space (R2 = 0.68) than the alternatives. Night-time temperature dominated the seasonal time-course of Rd , with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.

Predicting Tunnel-Induced Settlement in Cohesionless soils in Greenfield Condition

Rapid population growth and increasing congestion worldwide are placing greater demands on existing infrastructure. A solution to this problem is the use of tunnels to construct underground transportation systems. However, tunnelling induces settlement troughs that may result in severe or possibly irreparable damage to nearby infrastructure. For this reason, the effects of tunnelling must be well understood and predicted in the tunnel design stage, to prevent structural damage due to design oversights. Although tunnelling in clay has been extensively studied by previous researchers, the literature lacks reliable settlement estimation techniques for tunnels embedded in granular soil media. In this study, an extensive parametric analysis is conducted to examine the effects of the tunnel diameter, embedment depth, soil density, and shield conicity (tail void gap) on tunnel-induced settlement. Based on the results of the parametric analysis, the authors propose an effective, user-friendly solution that utilizes artificial intelligence software to predict tunnel-induced surface subsidence. The reliability and accuracy of the proposed solution are demonstrated by comparisons with 12 different sets of measured field data.

Synergy of random balance design method and intelligent optimization technique for model updating of the 4 MW offshore wind turbine benchmark

The technical challenges for model updating of real marine engineering structures include the extraction of modal parameters associated with incomplete measurement information and the correction of a large number of structural parameters. As it is difficult to verify the effectiveness of model updating methods for engineering applications, the representative models have to be adopted in finite element (FE) analysis or lab-scale tests, leading to the incomplete reflection of actual structural performances. Based on these factors, a practical model updating method is developed in this study to make full use of field measurement data from a 4 MW offshore wind turbine for the accurate estimation of structural parameters using the random balance designs-Fourier amplitude sensitivity test (RBD-FAST) strategy and an improved particle swarm optimization (IPSO). Leveraging the measured acceleration signals from offshore wind turbines under the conditions including operation, shutdown, collision and typhoon scenarios, the complex exponential decomposition method is applied to accurately extract the time-varying acceleration components (TVAC) for the construction of the frequency response function (FRF). Following that, RBD-FAST is implemented into IPSO with adaptive inertia weights and asynchronously varying learning factors to enable efficient selection of numerous updated physical parameters, thus improving time cost and computational accuracy. The correctness of the proposed method is verified by a numerical jacket platform model. Furthermore, the 4 MW offshore wind turbine benchmark is developed to assess the feasibility of the proposed model updating method using field measured data from different scenarios. Results show that the synergy of RBD-FAST and IPSO in the proposed method can accurately update parameters and minimize a maximum discrepancy of 0.8970% for the first-three orders of natural frequencies between the benchmark and the updated models in the collision scenario. Summarily, the present work provides a potential technique and practical engineering references for model updating of offshore wind turbines subject to harsh marine environments.

Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass

Forest canopy mean height (CMH) and aboveground biomass (AGB) are key indicators for evaluating forest ecosystem productivity. In this study, we proposed a new approach to integrate field measurement data, GEDI LiDAR, sentinel, and terrain data to construct multi-source data-driven forest CMH and AGB models at a 30-m resolution. First, we employed the RFE-SVM (Recursive Feature Elimination- Support Vector Machine) method to determine the features sensitive to forest height and AGB. Second, we used three regression models to construct the CMH model to extend the GEDI point data to wall-to-wall CMH maps thereby providing sensitive features for AGB estimation. Third, we jointly selected the features and field measurement data to build a model to estimate AGB. The CMH and AGB models, evaluated within the study area, achieved R2 values of 0.64 and 0.89, respectively. Fourth, we performed transferability tests for the AGB model. The AGB model built based on data from study area was applied to three other test areas, resulting in R2 values of 0.66, 0.76, and 0.91, respectively. Overall, this study presented a method that utilizes extensive open data with great potential for mapping forest CMH and AGB over large areas.

Comparison of a Dynamic Model of Electric Arc Furnace with Actual Operation Data for Voltage Flicker Analysis in Electrical Power Network

Electric arc furnaces (EAFs) used in the iron and steel manufacturing industry for melting and refining scrap metals are one of the most disturbing loads that exhibit unbalanced and highly nonlinear characteristics. Serious voltage fluctuations occur in the power system as a result of the rapid change in the current drawn from the grid by the EAF. Voltage fluctuations lead to a power quality problem known as flicker, which is defined as observable changes in light sources that affect the production environment, cause eye fatigue in personnel, and lower the work concentration levels. To investigate the voltage flicker problem, an accurate mathematical model describing the behavior of the EAF load is required. In this study, a dynamic EAF model that can be adjusted to different operating conditions has been developed in the time domain. The electric arc voltage has been modeled as an externally controllable voltage source. The instantaneous arc voltage has been expressed as a function of the arc length independent of the current. The arc resistance, which varies with time and is nonlinear, has also been calculated with differential equations using the instantaneous arc voltage value. To measure the short-term flicker severity index caused by the EAF in the power system, a flicker meter in compliance with the International Electrotechnical Commission (IEC) 61000-4-15 standard has been designed. The current-voltage characteristics of the EAF, its effect on the power system, and the flicker severity occurring at the point of common coupling (PCC) have been analyzed with simulation studies using the PSCAD/EMTDC software. Besides, the simulation results of the dynamic model of the EAF have been compared with the results obtained from the model based on the measured field data.