Load Estimation Research Articles

Stochastic estimation of wake-induced loads in wind farms using conformalized graph neural networks

In the evolving landscape of sustainable energy, wind power has become a cornerstone in the transition towards renewable energy sources. With significant advancements in turbine technology and simulation methods, wind farms are now a cost-effective alternative to traditional power plants. Nevertheless, optimizing the performance and lifespan of wind turbines remains challenging, especially when it comes to predicting and managing the cumulative load on turbine structures. Wake effects, which result from the complex aerodynamic interplay between turbines, reduce energy efficiency and increase mechanical stress on turbine components. A precise assessment of wake-induced loads is therefore vital for estimating the Remaining Useful Lifetime (RUL) of turbines. To address these challenges, we introduce a novel approach using Graph Neural Networks (GNNs) in combination with Conformal Predictors to model wind farms and evaluate wake-induced loads while estimating uncertainty. GNNs are particularly adept at capturing the complex interactions in a wind farm due to their ability to model graph-structured data. This enables a more accurate representation of the aerodynamic interactions between turbines. Alongside the GNN framework, we employ Conformal Predictors for uncertainty estimation. Conformal Predictors provide statistically valid prediction sets based on past data and minimal assumptions, allowing us to estimate uncertainty bounds with improved confidence. The fusion of GNNs with Conformal Predictors offers a robust framework for predicting turbine loads, while reliably quantifying the uncertainty associated with these predictions.

Engineering estimate of plastic collapse loads for cracked pipe bends under in-plane and out-of-plane bending

Nuclear power plant piping systems frequently encounter complex loads due to factors like internal pressure, self-weight, structural supports, and operational conditions. Within these systems, pipe bends play a crucial role in releasing energy through deformation. Cracks pose a risk to the integrity of the system, necessitating the calculation of the plastic collapse load for cracked pipe bends. This study presents estimations for the plastic collapse load of cracked pipe bends under pure out-of-plane bending moments and simultaneous in-plane and out-of-plane bending moments. Cracks located in the intrados, extrados, and crown of pipe bends were considered. The estimations for plastic collapse load are proposed based on the observed similarity between plastic collapse load and plastic deformation under both in-plane and out-of-plane bending conditions. For combined in-plane and out-of-plane bending moment conditions, circular and parabolic relationships have been proposed for a straightforward plastic collapse load prediction. Additionally, the estimations for plastic collapse loads were derived from the results of finite element analysis conducted on crack-free pipe bends, demonstrating its applicability to cracked pipe bends as well.

Assessing the feasibility and quality performance of a renewable Energy-Based hybrid microgrid for electrification of remote communities

Access to reliable energy is crucial for development, yet many rural areas in southern Bangladesh suffer from electricity shortages, impeding essential services and hindering social and economic progress. This paper proposes integrating renewable energy-based microgrids to provide sustainable and reliable electricity, thereby improving living conditions and boosting economic growth. A detailed survey in Ruma, Bandarban, was conducted for load estimation. Simulation results for on-grid and off-grid microgrids are obtained using HOMER Pro and PVsyst software. The off-grid system includes 21.8 kW of PV, 15 kW of hydro, and 222 kWh of battery storage, while the on-grid system includes a 200 kW PV system and a 15 kW hydro turbine. The levelized cost of energy (LCOE) is 0.15 USD/kWh off-grid and 0.03 USD/kWh on-grid. The on-grid system shows economic sustainability with a 6.8-year break-even point, 13 % IRR, and 8.7 % ROI. Environmental analysis shows significant greenhouse gas reductions, with CO2 emissions decreasing from 227,778 kg/year to 199,016 kg/year. Additionally, a sensitivity analysis is conducted, which underscores the resilience of the proposed hybrid microgrid system to weather variations and cost fluctuations. This paper provides a comprehensive foundation for policymakers to consider renewable microgrids as a solution for rural electrification in southern Bangladesh, utilizing solar and hydropower resources.

Structural load estimation of the wheel loader for customer usage profile monitoring

Structural load estimation of the wheel loader for customer usage profile monitoring

Prevalence of Human Papillomavirus Infection and its Association with the Risk of Cervical Cancer among Hiv-Positive Women in Plateau State, North-Central Nigeria

Study’s Novelty/Excerpt This study investigates the prevalence and risk factors of HPV among HIV-infected women in Plateau State, Nigeria, highlighting a significant correlation between low CD4+ counts, high viral loads, and increased HPV infection rates. By utilizing comprehensive diagnostic methods including ELISA for HPV detection and cytology for cervical abnormalities, the research offers robust data linking immunosuppression and HPV-related cervical pathology in a high-risk population. The findings emphasize the urgent need for targeted interventions to improve sexual health behaviors and further research on how low immunity accelerates cervical cancer progression in both HIV-positive women and the broader population. Full Abstract Human papillomavirus (HPV) is one of the most common sexually transmitted infections (STI) associated with cervical, uterine, and anogenital cancers. Persistent infection with HPV is associated with abnormal cervical cells, which can develop into cervical cancer if left untreated. Human papillomaviruses are the first viruses to have been acknowledged to prompt carcinogenesis, and they are linked with cancers of the uterine cervix, anogenital tumours, and head and neck malignancies. A hospital-based study of HIV-infected women across the three senatorial zones of Plateau State, Nigeria, was conducted between November 2018 to November 2020. Ethical approval for the study was first obtained from the ethical committee of Plateau State Specialist Hospital Jos, and informed consent to participate in the research was also obtained from each participant. HIV status confirmation was first done through standard rapid test procedures, followed by cytology testing via the Pap smear procedure to detect any precancerous or malignant changes in the cervix. Subsequent detection of HPV utilized the ELISA procedure, while CD4+ cell count and viral load estimations were done using flow cytometry and nucleic acid amplification techniques, respectively. Questionnaires were administered to obtain information on cervical cancer risk factors and clinical presentations. The overall prevalence of HPV was 28% among HIV-infected women. More HPV infection (31.9%) occurred in women with low CD4+ count (0-200 cells/mm3), and also highest (50.0%) among women with the highest HIV viral load (>100 copies/mL). The possible risk factors identified in this study include multiple sexual partnering, low condom usage, and coinfection with other STIs, among others. In conclusion, this study identified a high HPV prevalence, low CD4+ counts, and coinfection with other STIs among high-risk populations (HIV-infected women). We, therefore, recommend improved sexual behaviours and further research on the impact of low immunity on the rate of progression of cervical abnormality to cervical cancer, not just in HIV-positive women but in the general population.

Usefulness of hand sensor device for lumbar load estimation

Usefulness of hand sensor device for lumbar load estimation

Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor Based on RPLESO

In view of the problem of the low-speed jitter of household lawn mowers driven by a permanent magnet synchronous motor (PMSM) at low speeds and high torque, and the complicated parameters of traditional non-linear active disturbance rejection controllers, a partially optimized linear active disturbance rejection control (LADRC) driving PMSM strategy is proposed. First, the linear extended state observer (LESO), which bears a significant burden in terms of speed and load estimation in active disturbance rejection control, is optimized by reducing its order to improve the anti-disturbance performance of the active disturbance rejection controller within a limited bandwidth. Secondly, the reduced-order parallel linear extended state observer (RPLESO) is obtained by optimizing the parallel structure of the order-reduced LESO, which improves the control precision and robustness of the system. Through a simulation and experimental verification, the optimized LADRC control of the PMSM system is shown to improve the parameter adjustability, speed estimation precision and system robustness.

Vision-based estimation method of crowd-induced vertical dynamic loading and vibration analysis

Most existing studies concentrating on human-induced loads on structures often rely on approximate formulas based on the loading from a single gait cycle, which inherently possess certain limitations. In this paper, a novel approach utilizing a multi-person 2D posture estimation model rooted in computer vision technology to real-time identify the posture of moving human bodies on construction sites is proposed. This approach works to deduce the reaction forces of moving bodies upon the floor by analyzing the motion characteristics of different body parts during their movement, ensuring an accurate estimation of structural loading. To validate the model’s accuracy, a series of tests were conducted, leveraging force measurement equipment to scrutinize the precision of load estimation during single-person motion. This examination seeks to ascertain the potential of the load prediction model in forecasting multi-person loads within actual project environments.

Hyperbolic load–displacement analysis of helical and expanded piles: database approach

Recent years have witnessed a focus on improving geotechnical systems by implementing and constructing new deep foundations such as helical and expanded piles. In this study, the effects of parameters such as embedment depth, pile geometry and axial loading direction on the load–displacement behaviour of these piles were examined. To this end, a database was compiled consisting of 80 axial loading test records for different piles. The embedment depth of the piles was in the range 2.4–36.8 m and the diameter of the helices (DH) or expanded parts (DEP) was in the range 254–1500 mm. The ultimate load of the piles was determined using the 2.5% and 5% displacement ratio criteria and the Brinch Hansen 80% method. Hyperbolic functions were fitted to the load–displacement curves, allowing for consistent estimation of the limit load and the initial tangent modulus. Analysis of the results from the database revealed that the dominant factors influencing the ultimate load, limit load, maximum measured load, initial stiffness and load–displacement behaviour were the ratio of DH or DEP to the shaft diameter, the shaft area and the toe area, and the load direction. Correlations derived from the database were validated using measurements from eight full-scale helical and expanded piles.

Load aware active low energy adaptive clustering hierarchy for IoT-WSN

Clustering is a primary process that takes place in an IoT based wireless sensor network environment commences from the deployment phase. Due to the heterogeneity and resource constrained nature of internet of things (IoT) networks, dynamic clustering, cluster head selection, and routing are required to optimize the network and to improve the overall network performance. Load aware active low energy adaptive clustering hierarchy (LAALEACH) work is an attempt to introduce novel components to the standard LEACH protocol. The main objective of LAALEACH work is to achieve a load aware active routing in IoT based wireless sensor network environments. Rapid load estimator, load pattern tracker, and load aware active routing are the contributed modules introduced in this LAALEACH work. Most recent related works are analyzed and the proposed modules are devised in a way to overcome the issues in the existing methods. Standard network performance parameters such as throughput, packet delivery rate, communication delays, and energy consumption are measured by the OPNET based simulation during the experiments. Obtained improvements in the overall performance is the accomplishment of LAALEACH work.

Low frequency load identification under high noise level using weighted total least squares

Load identification in structural dynamics is an ill-conditioned inverse problem, and the errors exist in both the transfer function matrix and the response vector, which have a great influence on the accuracy of the identification, especially at the low frequency and high noise. Although the Total Least Squares (TLS) method can consider the errors in the transfer function matrix and the response vector, the problem of unequal precision between them has not been properly solved. In this article, the Weighted Total Least Squares (WTLS) method for load identification is presented. First, the variance–covariance matrices are constructed using stochastic information, which can consider the unequal precision of the data and make the solution less biased and more precise. Then, Tikhonov regularization is used to regularize the WTLS method to reduce the ill-condition of the transfer function matrix. To validate the performance of the WTLS method under different noise levels, a load identification simulation with four excitation loads on a plate and a load identification experiment with three excitation loads on a steel plate were studied. The results show that when the noise levels of the transfer function and the response are different, the accuracy of the TLS solution is poor. Especially when the noise levels are high, the identification deviations are large. The WTLS method considers the unequal noise levels using the weight matrices, which provides more stable load estimates at different noise levels. And the identification deviations are greatly reduced compared to the TLS method.

Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection

Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection

Musculoskeletal spine modeling in large patient cohorts: how morphological individualization affects lumbar load estimation.

Introduction: Achieving an adequate level of detail is a crucial part of any modeling process. Thus, oversimplification of complex systems can lead to overestimation, underestimation, and general bias of effects, while elaborate models run the risk of losing validity due to the uncontrolled interaction of multiple influencing factors and error propagation. Methods: We used a validated pipeline for the automated generation of multi-body models of the trunk to create 279 models based on CT data from 93 patients to investigate how different degrees of individualization affect the observed effects of different morphological characteristics on lumbar loads. Specifically, individual parameters related to spinal morphology (thoracic kyphosis (TK), lumbar lordosis (LL), and torso height (TH)), as well as torso weight (TW) and distribution, were fully or partly considered in the respective models according to their degree of individualization, and the effect strengths of these parameters on spinal loading were compared between semi- and highly individualized models. T-distributed stochastic neighbor embedding (T-SNE) analysis was performed for overarching pattern recognition and multiple regression analyses to evaluate changes in occurring effects and significance. Results: We were able to identify significant effects (p < 0.05) of various morphological parameters on lumbar loads in models with different degrees of individualization. Torso weight and lumbar lordosis showed the strongest effects on compression (β ≈ 0.9) and anterior-posterior shear forces (β ≈ 0.7), respectively. We could further show that the effect strength of individual parameters tended to decrease if more individual characteristics were included in the models. Discussion: The induced variability due to model individualization could only partly be explained by simple morphological parameters. Our study shows that model simplification can lead to an emphasis on individual effects, which needs to be critically assessed with regard to in vivo complexity. At the same time, we demonstrated that individualized models representing a population-based cohort are still able to identify relevant influences on spinal loading while considering a variety of influencing factors and their interactions.

Research on Load Estimation for Commercial Trucks Based on Air Suspension State Parameters

For commercial trucks, accurate vehicle load information is crucial to preventing overloading and uneven load distribution, thereby improving transportation efficiency and reducing transportation costs. Existing research on vehicle load estimation primarily focuses on tires, axles, frames, and leaf springs, with limited studies on load estimation for vehicles equipped with air suspension. This paper presents the design of a vehicle onboard weighing system for civil commercial trucks based on the state parameters of air springs and proposes a matching method for vehicle load estimation. The system utilizes laser distance- and gas pressure sensors to collect the state parameters of the air springs. It estimates the vehicle load and displays the load information in real time on the onboard display. The vehicle load estimation method was explored through prototype testing and theoretical derivation. A fitting model was constructed using surface fitting techniques to establish the relationship between the effective contact area and the height variation and capsule pressure of the air spring. Subsequently, a load prediction model was built based on the force equilibrium equation of the air springs. Finally, the construction and accuracy of the load prediction model for a specific type of diaphragm air spring were verified using finite element analysis. The results demonstrated that the average relative error of the load prediction model could be controlled within 1%, meeting the high accuracy requirements of the vehicle weighing system.

Classification of Driver Cognitive Load in Conditionally Automated Driving: Utilizing Electrocardiogram-Based Spectrogram with Lightweight Neural Network

With the development of conditionally automated driving, drivers will be allowed to perform non-driving-related tasks. Under such circumstances, continuous monitoring of driver cognitive load will play an increasingly important role in ensuring that drivers have sufficient mental resources to take over control of the vehicle should the driving automation fail. However, estimation of cognitive load is challenging because of the difficulties in identifying high-level feature representation and accounting for interindividual differences. Physiological measures are believed to be promising candidates for cognitive load estimation in partially automated vehicles. However, current estimation methods are mainly based on the manual feature extraction of time- or frequency-domain indicators from physiological signals, which may not adapt to dynamic driving conditions. With the development of deep learning, the neural network has shown good performance in automatically capturing high-level features from input data. Inspired by this, we adopted a novel approach to classify driver cognitive load based on electrocardiogram (ECG) spectrograms, in which the driver’s ECG signal was collected and transformed into a 2D spectrogram by a short-time Fourier transform. A squeeze-and-excitation network-based deep-learning framework that can capture high-level features and pays more attention to the cognition-related features of the spectrogram was proposed for classification. Experiments on a publicly available dataset demonstrated that our model achieved an accuracy of 96.76% in differentiating two levels of cognitive load for a within-subject evaluation and 71.50% accuracy with an across-subjects evaluation. The results demonstrated the feasibility of detecting drivers’ cognitive load through deep learning using ECG spectrogram alone.

Estimating stream sediment loads to assess management options for a Southern Appalachian mountain lake

Enka Lake is a 25-ha reservoir in the Southern Appalachian mountains surrounded by the Biltmore Lake residential neighborhood. The aesthetic and recreational value of Enka Lake can be negatively affected by turbid water and sediment buildup, resulting from stormwater originating upstream in its 15.38 km2 watershed. In this study, we monitored streamflow and sediment loads for nearly 2 years to better understand sedimentation dynamics and evaluate management options. We collected nearly 500 water samples throughout the watershed, focusing on two tributaries that provide the majority of streamflow into Enka Lake. During baseflow conditions, those tributaries are very clear (turbidity ranging from 1 to 20 NTU), but sediment-laden stormflow (turbidity > 1000 NTU) from dirt roads, gravel driveways, and poorly vegetated yards is common and, during extreme storm events, causes lake discoloration. Discharge-sediment data pairs were used to construct sediment rating curves and estimate sediment fluxes through these tributaries. The predicted sediment fluxes were tested with high-frequency sampling during and after three storms in different seasons; predicted values underestimated the peak sediment fluxes (0–400%) but generally matched total measured sediment loads. Estimates of annual sediment load from the two tributaries (540–900 tonnes/year) suggest that a dredging effort commissioned by the neighborhood association in 2016 may have removed only about 1–3 years’ worth of sediment. Installing sediment forebays or other retention structures is likely the preferred sediment management strategy moving forward (over a regular dredging schedule) since they can be maintained more easily without affecting lake levels. Designing these retention structures will benefit from the estimates of stormflow and sediment fluxes, and the study design presented here—including the help of community-based volunteer samplers—provides a model that could be used at other sites in the Southern Appalachians and elsewhere.

Cognitive Workload Estimation in Conditionally Automated Vehicles Using Transformer Networks Based on Physiological Signals

Though driving automation promises to improve driving safety, drivers are still required to be ready to retake control in conditionally automated vehicles, which are defined by the Society of Automotive Engineers (SAE) as SAE L3 vehicles. Thus, drivers’ states can still affect driving safety in SAE L3 vehicles. Previous research found that a high cognitive load may impair drivers’ takeover performance. Thus, it is still necessary to estimate drivers’ cognitive load in SAE L3 vehicles. However, existing driver cognitive load estimation algorithms mostly focus on vehicles with a lower level of driving automation (e.g., SAE L0), which may not be relevant when estimating driver states in SAE L3 vehicles, given that drivers’ responsibilities are different, and several commonly used measures (e.g., driving performance) are unavailable when drivers are not continuously controlling the vehicle. Further, previous driver cognitive load estimation algorithms rarely considered the temporal information in the input features. Thus, we proposed a deep-learning algorithm to estimate driver cognitive load in SAE L3 vehicles, which integrated multiple physiological features (i.e., electrocardiogram, electrodermal activity, respiration) and considered the temporal correlation of the data using a transformer-encoder-based network. The performance of our algorithm was compared with baseline models on an open data set. Results showed that our algorithm outperformed baseline models and achieved an accuracy of 94.4% using within-subject data partition (proportionally splitting data from the same subject into the training and testing data sets) and an accuracy of 89% using across-subjects data partition (dividing the training and testing data sets based on individual subjects).

Predicting Musculoskeletal Loading at Common Running Injury Locations Using Machine Learning and Instrumented Insoles.

Wearables have the potential to provide accurate estimates of tissue loads at common running injury locations. Here we investigate the accuracy by which commercially available instrumented insoles (ARION; ATO-GEAR, Eindhoven, The Netherlands) can predict musculoskeletal loading at common running injury locations. Nineteen runners (10 males) ran at five different speeds, four slopes, with different step frequencies, and forward trunk lean on an instrumented treadmill while wearing instrumented insoles. The insole data were used as input to an artificial neural network that was trained to predict the Achilles tendon strain, and tibia and patellofemoral stress impulses and weighted impulses (damage proxy) as determined with musculoskeletal modeling. Accuracy was investigated using leave-one-out cross-validation and correlations. The effect of different input metrics was also assessed. The neural network predicted tissue loading with overall relative percentage errors of 1.95 ± 8.40%, -7.37 ± 6.41%, and -12.8 ± 9.44% for the patellofemoral joint, tibia, and Achilles tendon impulse, respectively. The accuracy significantly changed with altered running speed, slope, or step frequency. Mean (95% confidence interval) within-individual correlations between modeled and predicted impulses across conditions were generally nearly perfect, being 0.92 (0.89 to 0.94), 0.95 (0.93 to 0.96), and 0.95 (0.94 to 0.96) for the patellofemoral, tibial, and Achilles tendon stress/strain impulses, respectively. This study shows that commercially available instrumented insoles can predict loading at common running injury locations with variable absolute but (very) high relative accuracy. The absolute error was lower than the methods that measure only the step count or assume a constant load per speed or slope. This developed model may allow for quantification of in-field tissue loading and real-time tissue loading-based feedback to reduce injury risk.

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I.

Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimation outside the laboratory in athletic tasks. The aim of this study was to investigate ML approaches in predicting knee joint loading during sport-specific agility tasks. We explored the possibility of predicting high and low knee abduction moments (KAMs) from kinematic data collected in a laboratory setting through wearable sensors and of predicting the actual KAM from kinematics. Xsens MVN Analyze and Vicon motion analysis, together with Bertec force plates, were used. Talented female football (soccer) players (n = 32, age 14.8 ± 1.0 y, height 167.9 ± 5.1 cm, mass 57.5 ± 8.0 kg) performed unanticipated sidestep cutting movements (number of trials analyzed = 1105). According to the findings of this technical note, classification models that aim to identify the players exhibiting high or low KAM are preferable to the ones that aim to predict the actual peak KAM magnitude. The possibility of classifying high versus low KAMs during agility with good approximation (AUC 0.81-0.85) represents a step towards testing in an ecologically valid environment.

Elucidating the failure of the Ideal Adsorbed Solution Theory for CO2/H2O mixture adsorption in CALF-20

The use of CALF-20 for CO2 capture from humid flue gases has attracted a vast amount of attention from both academia and industry. The focus of this article is on CO2/H2O mixture adsorption for which published experimental data demonstrate the severe limitations of the Ideal Adsorbed Solution Theory (IAST) in providing a quantitative estimation of the component loadings. We use Configurational-Bias Monte Carlo (CBMC) simulations for elucidating the origins of thermodynamic non-idealities. The CBMC simulations reveal that two distinct regimes prevail during mixture adsorption. For low relative humidities less than say 20 %, the CO2 loadings in the adsorbed phase is significantly higher than anticipated by the IAST. CBMC simulations of intermolecular distances for guest pairs reveal that failure of the IAST can be traced to the phenomenon of segregated adsorption. The H2O-H2O pairs are close together at typical distances of about 3 Å. The CO2-H2O pairs are typically 8 Å apart, occupying adjacent adsorption sites corresponding to the O atoms of the oxalate group. This implies that the CO2 molecules face a less severe competitive adsorption with H2O than is anticipated by the IAST, whose applicability mandates a uniform and homogeneous distribution of adsorbates in pore space. The situation changes dramatically at high values of relative humidities, typically larger than about 50 %; in this scenario, the adsorbed phase is significantly richer in H2O. The CO2 molecules are compelled to share same adsorption site with pairs of H2O molecules that are hydrogen-bonded with each other. Consequently, the competition faced by CO2 is significantly higher than anticipated by the IAST, resulting in significantly lower CO2 uptakes.The important message that emerges from this investigation is the need to incorporate the Real Adsorbed Solution Theory (RAST) for quantitative modelling of fixed-bed adsorbers in CO2 capture with CALF-20.