Indirect Measurement Method Research Articles

Detailed workflow for estimating bedload transport based on dune tracking method

ABSTRACT Accurate assessment of bedload transport is a challenging task due to its variable nature in space and time. Bedload transport measurement techniques can be divided into two categories of direct and indirect methods. The focus of this study is on dune tracking as an indirect measurement method that has received much attention in recent years. However, a comprehensive workflow that allows the integration of this method into a routine measurement operation instead of or jointly with the direct sampling method has not been implemented yet. During a three-day measurement campaign conducted in the Rhine River, a detailed workflow for performing bedload transport measurements using the dune tracking method was introduced. Dune tracking validation was thoroughly evaluated, with significant efforts to simultaneously collect data using direct sampling. Direct measurements of bedload transport showed higher and more variable rates compared to dune tracking estimates. It was assumed that finer sediment fractions, moving as patches or small-scale superimposed bedforms were presumably not captured by the dune tracking. However, their significant contribution to bedload transport suggests that dune tracking estimates may have been underestimated. Further research is needed to explore how varying flow and sediment conditions affect bedload transport modes and measurement methods comparability.

Experience with the high mobility reception plate for source characterization according to EN 15657

EN 12354-5 describes methods for the prediction of noise from technical equipment based on the airborne sound radiation and the structure-borne sound excitation. This paper considers methods to characterize the source in terms of structure-borne-sound (sbs) emission. Laboratory methods for sbs source characterization are given in EN 15657. In current research and development projects at Rosenheim Technical University of Applied Sciences these methods are applied to fully characterize technical equipment such as heat pumps or ventilation systems. This includes the determination of the equivalent blocked force, the source single equivalent free velocity and the source single equivalent mobility. EN 15657 describes various methods to determine the required properties. Recent project experience showed that this is essentially necessary because the most suitable method has to be applied depending on the nature of the technical equipment. This paper presents experiences with indirect measurement methods on the high-mobility plate and the direct measurement method given in EN 15657.

Accelerometer-assisted computer vision data fusion framework for structural dynamic displacement reconstruction

Accurate displacement measurement provides crucial information for evaluating the structural health condition. It is important to note that both direct and indirect displacement measurement methods have their own limitations, which can be remedied by leveraging each other’s strengths. In this study, a novel accelerometer-assisted computer vision data fusion framework is developed for accurately reconstructing structural dynamic displacement. The framework does not require a specific mathematical model or prior knowledge about the data’s characteristics, allowing it to be applied more broadly across different applications without the constraints of model assumptions. The core of this framework is to integrate successive variational mode decomposition (SVMD) and Bayesian optimization approaches to adaptively determine the weight factors of the fusion components. Importantly, an enhanced optical flow approach is presented for converting pixel movement to structural displacement from natural targets. This approach can effectively reduce the selection of mis-matched points within the ROI (Region of Interest), thereby reducing drift errors. The developed framework is verified via shaking table tests of a reinforced concrete frame structure under seismic excitation. Results indicate that the developed framework excels in accurately estimating structural dynamic displacement. Compared to single-vision displacement identification results, the proposed framework demonstrates lower peak error (< 1.65 mm) and normalized root mean square error (< 0.30). Meanwhile, the reconstructed displacement, by introducing dynamic displacement component from acceleration measurement, presents a wider frequency range than the vision-based displacement.

Developing a reference method for indirect measurement of pasture evapotranspiration at sub-meter spatial resolution

To establish an indirect method for estimating and partitioning pasture evapotranspiration, it is vital to develop a direct reference method that aligns with the required temporal and spatial resolution. An evapotranspiration chamber offers an effective solution as it is easy to deploy and operates at an appropriate measurement scale. In this study, we prepared and tested a closed hemispherical chamber for on-site measurements of evaporation and/or transpiration. Advanced data monitoring and logging techniques were integrated to enhance the precision and reliability of direct in-field evapotranspiration measurements. During laboratory testing, vapor accumulation within the chamber was monitored to identify the best representative segment of the vapor accumulation curve. Results indicated that the instrument stabilizes its readings within 5 to 10 s post-deployment in laboratory settings. The subsequent 15 s produce stable readings that best represent actual vapor accumulation. The optimal fan speed, producing an air speed of 5.36 ms−1 at the vicinity of the fan within the chamber, paired with a wire mesh above the vapor-producing surface, yielded the best results. The study established a calibration factor (C) of 1.02 based on the actual water loss and vapor accumulation readings from the sensors at this fan speed. Advanced data analytics were applied to derive the calibration factor and to calculate the values of evapotranspiration from the changing microclimate within the chamber. Direction towards complete automation and the limitations of the chamber in field measurement are provided. The chamber was also tested under field conditions, and the paper examines its practical application and necessary adjustments for field measurements.

Comparison of directly and indirectly estimated entrance skin dose (ESD) for diagnostic radiation qualities (RQR, RQA and RQT) using water phantom and shadowfree diagnostic chamber (SFD)

BackgroundThe aim of this study was to find the entrance skin dose (ESD) for diagnostic radiation qualities RQRs, RQAs and RQTs given in IAEA technical report series No. 457 using direct and indirect methods of measurement. Measurements were done for 5 × 5, 10 × 10, 15 × 15, 20 × 20 and 25 × 25 cm2 field sizes and 70, 80, 90 and 100 cm source to surface distance (SSD) using shadow-free diagnostic (SFD) chamber and water phantom having dimension 30 × 30 × 30 cm3. ESD direct measurements were done by placing SFD chamber on the surface of water phantom, while in the case of indirect measurements, air kerma values were obtained.ResultsESD values for different selected radiation qualities RQR2, RQR5, RQR8, RQR10, RQA2, RQA5, RQA8, RQA10, RQT8, RQT9 and RQT10 were found to be in the range of 0.0045–5.11 mGy per examination.ConclusionsResults obtained were found to be comparable with ESD values published in the literature. The obtained results in this research would help in establishing the national diagnostic reference levels (DRLs) which would help in the optimization of diagnostic imaging procedures. It would also help the radiographers to optimize field sizes and SSDs in order to reduce dose to the patients thereby ensuring good radiological practices, and this would reduce the stochastic risk to the patients caused by the ionizing radiations.

MEASURING THICKNESS OF FLAX FIBRES IN A CLUSTER USING COMPUTER VISION

Authors analyse a method of indirect measurement of flax fibre linear density using its thickness, based on computer vision approach and “distance transform” algorithm in particular. A number of flaws of this method are identified. The main reason for the discrepancy is the effect of crossed fibres within the analysed sample. The secondary reason is the specific shape of fibre’s topological skeletonnear its tips. Based on these observations, a software model of the problem has been created. The software produces a simplified image of crossed fibres with specified properties, and measures this image using the above method. The effect of various factors on the discrepancy in the distribution of thickness values appears to match well with theoretical analysis. Additionally, a number of ways to improve the software model are outlined, related to how typicallyfibres are oriented within the analysed sample.

Geothermal heat source estimations through ice flow modelling at Mýrdalsjökull, Iceland

Abstract. Geothermal heat sources beneath glaciers and ice caps influence local ice-dynamics and mass balance but also control ice surface depression evolution as well as subglacial water reservoir dynamics. Resulting jökulhlaups (i.e., glacier lake outburst floods) impose danger to people and infrastructure, especially in Iceland, where they are closely monitored. Due to hundreds of meters of ice, direct measurements of heat source strength and extent are not possible. We present an indirect measurement method which utilizes ice flow simulations and glacier surface data, such as surface mass balance and surface depression evolution. Heat source locations can be inferred accurately to simulation grid scales; heat source strength and spatial distributions are also well quantified. Our methods are applied to the Mýrdalsjökull ice cap in Iceland, where we are able to refine previous heat source estimates.

Development of an indirect measurement method for the Contact Tube to Workpiece Distance (CTDW) in the Direct Energy Deposition – Arc (DED-ARC) process for different arc types

During the layer-by-layer build-up in the Direct Energy Deposition (DED) - Arc additive manufacturing (AM) process, the distance between the contact tube and the workpiece, effectively the welded layer, changes. Since the weld paths are predefined by the path planning software, a constant Contact Tube to Workpiece Distance (CTWD) and weld bead height is assumed. However, even small changes in geometry, such as crossovers of weld paths, result in higher weld beads than assumed. Similarly, an incorrectly assumed bead height as input to the path planning will result in a change in the CTWD. The sum of the deviations of the real weld geometries from the assumed ones in the path planning can greatly influence the CTWD. This implies that the dimensional accuracy may be significantly compromised. This research presents an approach for a general indirect measurement method using the welding current to obtain the CTWD during the actual welding process. A real-time process control method is implemented and validated using the mechanically controlled short arc and the pulsed arc process. Varying process parameters are used to validate the general applicability for a specific material. For the mechanically controlled short arc process, the model underestimates the measured CTWD by a mean error of 3.4 mm. The pulse process is overestimated by a mean error of 2.2 mm. The standard deviation for the pulse process with 1.3 mm is slightly smaller than for the short arc process with 1.7 mm.

Artificial intelligence applications for accurate geothermal temperature prediction in the lower Friulian Plain (north-eastern Italy)

Geothermal energy as a sustainable and clean energy source depends on the accurate estimation of reservoir temperatures. Understanding aquifer temperatures is crucial for optimizing low-enthalpy geothermal system exploitation. Advances in predictive algorithms can improve geothermal efficiency, while conventional methods of indirect temperature measurement and assumptions in geochemical analysis lead to uncertainties. As a solution, this study presents a comprehensive evaluation of six machine learning algorithms including eXtreme gradient boosting (XGBoost), decision tree, generalized regression neural network, extreme randomized trees, radial basis function, and elastic net. We employed essential performance metrics including coefficient of determination (R2) score, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and variance accounted for (VAF) to elucidate their predictive accuracy and generalization potential in the lower Friulian Plain (north-eastern Italy) where a geothermal reservoir is present. Among the algorithms scrutinized, XGBoost emerges as a predictive exemplar, achieving a remarkable R2 score of 0.9930 on the test dataset, with consistently low RMSE of 0.788, MAE of 0.587, MAPE of 1.909, and high VAF of 99.30, reaffirming its exceptional precision and robustness. It is worth noting that the other four models show slightly weaker performance than XGBoost, while elastic net shows moderate predictive power, which illustrates the complexity of the database. The Wilcoxon signed-rank test confirmed the superior performance of XGBoost in estimating geothermal temperatures compared to other algorithms, with statistical evidence supporting its precision and reliability. A Monte Carlo simulation for uncertainty analysis underlined the importance of model selection, accuracy and uncertainty management in the planning of geothermal projects in the lower Friulian Plain. A sensitivity analysis was performed to identify the main factors influencing the temperature prediction. Among the parameters considered, bicarbonate the highest significance at 0.51, which is essential for accurate temperature prediction because of its buffering capacity which directly influences water's thermal properties. Magnesium and electrical conductivity each contribute with 0.11, also play significant roles due to their impact on the water's heat retention and distribution capabilities. Water depth, with a value of 0.08, also has a significant influence on the temperature profiles in prediction models. In summary, the accurate prediction of XGBoost for the temperature of aquifer in carbonate reservoirs in the lower Friulian Plain, underline its value for optimizing geothermal resources and highlight most important influences on temperature.

Estimation of unfrozen water content of saturated sandstones using nuclear magnetic resonance, mercury intrusion porosimetry, and ultrasonic tests

The unfrozen water content (UWC) is a crucial parameter that affects the strength and thermal properties of rocks in relation to engineering construction and geological disasters in cold regions. In this study, three different methods were employed to test and estimate the UWC of saturated sandstones, including nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP), and ultrasonic methods. The NMR method enabled the direct measurement of the UWC of sandstones using the free induction decay (FID). The MIP method was used to analyze the pore structures of sandstones, with the UWC subsequently calculated based on pore ice crystallization. Therefore, the MIP test constituted an indirect measurement method. Furthermore, a correlation was established between the P-wave velocity and the UWC of these sandstones based on the mixture theory, which could be employed to estimate the UWC as an empirical method. All methods demonstrated that the UWC initially exhibited a rapid decrease from 0 °C to −5 °C and then generally became constant beyond −20 °C. However, these test methods had different characteristics. The NMR method was used to directly and accurately calculate the UWC in the laboratory. However, the cost and complexity of NMR equipment have precluded its use in the field. The UWC can be effectively estimated by the MIP test, but the estimation accuracy is influenced by the ice crystallization process and the pore size distribution. The P-wave velocity has been demonstrated to be a straightforward and practical empirical parameter and was utilized to estimate the UWC based on the mixture theory. This method may be more suitable in the field. All methods confirmed the existence of a hysteresis phenomenon in the freezing-thawing process. The average hysteresis coefficient was approximately 0.538, thus validating the Gibbs–Thomson equation. This study not only presents alternative methodologies for estimating the UWC of saturated sandstones but also contribute to our understanding of the freezing-thawing process of pore water.

Electrical measurement method of static friction force on rough surface

Friction plays a key role in the assessment of the safety and stability of mechanical systems (such as superconducting magnet quench explosion, aerospace vehicle bearing wear, etc.). Due to the closeness of the interfaces in engineering structures and the randomness of the contact surfaces, existing methods for measuring static friction force are unable to measure it at the contact interfaces of engineering structures under service conditions. In this paper, a new method for measuring the static friction force at the interface based on electrical signals is proposed. This method enables the measurement of the static friction force at interfaces of complex engineering structures under service conditions solely through electrical signals. The results indicate that the contact resistance gradually decreases with the increase in tangential load during the static friction stage until a monotonic behavior of macroscopic sliding occurs. The evolution of contact resistance is linked to the evolution of the real contact area, and this monotonic behavior can be explained as the deformation form of contact points. The accuracy of the proposed electrical measurement method is verified by comparison with experimental results (with an error of less than 9%). The indirect measurement method of friction force proposed in this paper can effectively measure the static friction force at the interfaces of engineering structures under service conditions, and it is expected to be applied to the detection of friction performance at engineering structure interfaces in extreme service environments.

Indirect measurement of process parameters in peripheral end milling based on acoustic emission signals

In machining, current methods for online estimation of cutting parameters heavily rely on piezoelectric dynamometers. Albeit their high precision, integration into industrial environments is limited due to high cost and practical machine mounting constraints. To address this challenge, this study presents an indirect measurement method of the cutting depths in peripheral milling based on acoustic emission sensors. The proposed method involves extracting time-based features from a tailor-made envelope of the acoustic emission signal, identifying transitions between cutting phases from its waveform, and using these features to estimate radial and axial depths of cut through a geometric model of the tool-workpiece contact zone. Experiments show that this method can achieve relative errors below 5% in measuring the depths of cut, approaching the precision levels of piezoelectric dynamometers. This suggests that acoustic emission is an alternative process variable, enriching the spectrum of monitoring systems traditionally dependent on the measurement of cutting forces.

Indirect load measurement method and experimental verification of floating offshore wind turbine

Accurate load data of wind turbines operating in real sea conditions is crucial for real-time control and offline structural optimization. However, the sheer size of these structures and the complexities of their working environments challenge the direct load measurement through strain gauges and fiber Bragg grating sensors. To address these problems, this paper introduces an indirect measurement method to acquire the thrust and pitch moment loads of the turbine. First, load identification techniques based on the frequency domain separation and aerodynamic damping were applied to decouple the wind and wave loads and establish the inverse load calculation model. Then, numerical simulations were carried out to calibrate and verify this model under a range of steady and unsteady conditions. Preliminary conclusions indicated the strong viability of this method, revealing a mean relative error of 2.32% for thrust and a corresponding equivalent high-frequency error of 24.66% for pitch moment under the designed operating conditions. Finally, a hybrid scaled model was constructed, combining a virtual wind turbine with a physical platform. Experimental results in the wave basin demonstrated the effectiveness of this indirect method in measuring thrust loads of wind turbines, achieving a mean relative error of less than 10%.

An indirect flow measurement method for bi-tandem axial piston pump based on leakage flow estimation

In pump-controlled hydraulic systems, configuring a flowmeter at a variable piston pump to monitor output flow causes additional losses. A method for indirect flow measurement of the bi-tandem axial piston pump based on flow leakage estimation is proposed to address this problem. The method first installs a displacement transducer at the actuator piston to collect data on the actuator piston position, outlet pressure, and shaft speed, then estimates leakage of key kinematic pairs, and finally considers flow ripple and fluid compressibility to form an indirect measurement for output flow of the pump. The indirect measurement accuracy under various typical operating conditions is analyzed. The results demonstrate that the mean absolute percentage error of the proposed indirect flow measurement method is less than 2.10%, and the method is effective with high measurement accuracy. The indirect measurement method avoids extra costs and energy consumption associated with configuring flowmeters in engineering hydraulic systems.

CPV System Optical Performance Evaluation by Means of Direct Experimental Measurement Procedure

The optics is the component that most affects the concentrating photovoltaic (CPV) system performance, depending above all on the concentration factor and optical efficiency. Hence, a basic aspect is the concentrated solar flux measure on the receiving area, the evaluation of which is principally realized by indirect measurement methods. First, a literature review on indirect and direct methods used for the evaluation of concentrated solar flux and optical parameters is presented in this paper. The experimental measurement procedure, which is able to evaluate the optical parameters and concentrated solar flux in CPV systems, is also presented. The main steps of this procedure are represented by experimental system setup, sensor selection for concentrated solar flux estimation, identification of all the factors affecting optical performances, and development of an experimental campaign and output analysis. In particular, the optical characterization results of a CPV system are obtained by means of in-depth experimental analysis using Triple-Junction (TJ) solar cells with areas of 5.5 × 5.5 mm2 and 10 × 10 mm2. Three different setups have been analyzed related to primary and secondary optics composition. The main aim of this paper is the determination of a direct measuring technique, rarely adopted in literature in comparison to the established techniques, that is able to evaluate experimentally the optical parameter values and that can be standardized for other CPV systems. In particular, equations that link the optical concentration factor (C) and efficiency (ηopt) with focal distance (h) represent the fundamental results. They can be used for similar point-focus configurations presenting the same TJ cell size and ranges of C, ηopt and h. Finally, the experimental results of the direct method are compared with those of an indirect method adopting the same CPV system and operational conditions.

Evaluation method for the impact of digital finance on the environmental performance of manufacturing industry

ABSTRACT A new evaluation method for the impact of digital finance on the environmental performance of the manufacturing industry is proposed to improve the accuracy of evaluation results and provide reliable data for enterprise development. Establish a scientific and comprehensive evaluation index system that includes environment, cost-effectiveness, life cycle, and sustainable development. Collect sample data through indirect measurement and direct measurement methods through questionnaire surveys and standardize each data through the measurement of digital economy development level to eliminate differences between different indicators. Based on the constructed evaluation indicator system and sample data collection results, the weights of each evaluation indicator are determined by establishing a projection pursuit model. The fuzzy comprehensive evaluation method and the analytic hierarchy process are used to obtain the fuzzy comprehensive evaluation results of environmental performance based on the projection pursuit model to evaluate the impact of digital finance on the environmental performance of the manufacturing industry. The experimental results show that compared with traditional business models, digital business models can effectively reduce energy consumption, operating costs, and logistics transportation costs of enterprises, improve resource utilization efficiency, and outperform traditional methods in evaluation accuracy, recall rate, and efficiency, verifying the effectiveness of this method.

Variational mode decomposition-based multirate data-fusion framework for estimating structural dynamic displacement by integrating vision- and acceleration-based measurements

Dynamic displacement is a crucial parameter in structural health monitoring (SHM) for assessing the safety, dependability, and suitability of structures under various types of excitations. Computer vision-based methods for dynamic displacement estimation have attracted much interest owing to their cost-effectiveness and convenience. However, these methods are limited by their low sampling rates and high data sensitivity. To compensate for these limitations, methods for combining data obtained from other sensors have been proposed. In this study, an experimental data-fusion framework for displacement estimation based on variational mode decomposition (VMD) was developed to leverage the advantages of vision- and acceleration-based measurements. The measurements were decomposed into ensembles of modes and recomposed to reconstruct the displacement with a higher accuracy and over a wider frequency range. An optimal mode recomposition method was proposed to achieve optimal mode combinations. Furthermore, this study introduced an improved vision-based displacement measurement method and a VMD-based indirect acceleration measurement method. The proposed framework was validated through four-story RC structure tests, which demonstrated that the method could enhance the accuracy of displacement estimation and extend the feasible frequency range compared with single-source displacement measurements. The method provides a promising solution for more effective health monitoring of modern structures subjected to a wide variety of dynamic loads.

Research on performance state evaluation of circuit breaker energy storage spring based on intelligent algorithm

The performance state evaluation method of circuit breaker energy storage spring mainly judges its performance state indirectly by measuring the pre-tightening force or pre-pressure of the spring. However, there may be some errors in this indirect measurement method, which will affect the accuracy of the evaluation results. Therefore, the performance state evaluation based on intelligent algorithm is proposed. Select the evaluation characteristic quantity of performance state, calculate the energy storage spring impulse according to the momentum theorem, and obtain the pressure value of the closing energy storage spring through the pressure sensor as the evaluation quantity reflecting the energy storage spring performance state. The BP neural network is established, and the fireworks algorithm is applied to the BP neural network to optimize the initial weight and threshold, so as to realize the performance state evaluation of energy storage spring based on BP neural network. The experimental results show that the spring energy release speed of the proposed method is in the range of 0 - 1.0m/s, and the estimated spring pressure value is basically consistent with the actual value.

BPF-WT combined filtering method for indirect identification of bridge dynamic characteristics

The indirect measurement method of extracting the dynamic characteristics of the bridge structure from the dynamic response of passing vehicles is a new method for bridge structure health monitoring, which has the advantage of cost-effectiveness. However, how to improve the accuracy of the indirect measurement method to identify the dynamic characteristics of the bridge is the current research focus. In this paper, combining the characteristics and advantages of bandpass filter (BPF) and wavelet transform (WT), a simple and effective joint filtering method BPF-WT is proposed for indirect identification of bridge dynamic characteristics. This method can eliminate the peak caused by the known vehicle frequency, and realize the layer-by-layer decomposition and reconstruction of the signal through the advantage of WT, to achieve the purpose of automatically executing the packet filtering task. This paper takes a simple trolley as an example to conduct a scaled test study to identify the dynamic characteristics of a bridge and proves that the BPF-WT joint filtering method can perform bridge dynamics under three conditions: different vehicle weights, different driving speeds, and driving on bridge decks with different roughness. Characteristic identification results and their influence rules. According to the research findings, the method of combined filtering suggested in this paper can efficiently filter out the noise produced by the vehicle’s frequency and the surroundings. This method can also distinctly detect the initial three frequencies of the bridge. Therefore, it demonstrates the practicality and efficiency of the BPF-WT combined filtering technique, which has a broad range of potential applications.

A Review of the State of the Art of Torque Ripple Minimization Techniques for Permanent Magnet Synchronous Motors

Industrial applications such as automation, robots, and electric vehicles are widely employing permanent magnet synchronous motors (PMSM) due to inherent characteristics such as high torque to power ratio, low maintenance, high efficiency, and wide operating range. However, PMSM drives can suffer from unwanted torque ripples caused by periodic disturbances of the electromagnetic effect. First, this paper analyses the sources of torque ripple in PMSMs. Second, the torque measurement techniques of the PMSM including torque sensors, torque observers, and indirect measurement methods are discussed to highlight their shortcomings. Third, this paper categorizes and summarizes advanced control-based torque ripple minimization (CB-TRM) techniques for the PMSM by critically evaluating their impacts. These CB-TRM techniques actively control the current waveforms to ensure smooth torque generation and mitigate the effects of torque ripples. Finally, the paper concludes by discussing remaining research questions and opportunities for future research on TRM techniques for PMSMs.