Calibration Coefficients Research Articles

A new proposal for an easy-to-calibrate DNI clear-sky model: a performance analysis and validation in different geographical locations

Clear sky models can be used as a reference for sizing solar technology applications and for solar irradiance forecasting purposes. Properly defining the parameters of each clear sky model implies knowing the altitude, latitude, longitude, as well as various climatic characteristics of the study site, which makes its implementation difficult. Derived from the above, in this work a new easy-calibration methodology was developed to obtain a functional clear sky model for any day of the year and any geographical location. By proposing calibration coefficients obtained from only a couple of DNI measurements, and without taking into account the geographical characteristics or climatic conditions of the study site, the developed clear sky model was tested in a global context using a period of 11 years (2010-2020) with databases of 71 solarimetric stations, obtaining an average RMSD that ranges between 6.48% (arid climate) to 7.97% (equatorial climate). In addition, the developed model was compared with a variety of clear sky models to determine the percentage of accuracy of these against clear and non-clear sky days, thus demonstrating that the accuracy of the proposed model can be superior in different scenarios.

China Aerosol Raman Lidar Network (CARLNET)—Part I: Water Vapor Raman Channel Calibration and Quality Control

Water vapor is an active trace component in the troposphere and has a significant impact on meteorology and the atmospheric environment. In order to meet demands for high-precision water vapor and aerosol observations for numerical weather prediction (NWP), the China Meteorological Administration (CMA) deployed 49 Raman aerosol lidar systems and established the first Raman–Mie scattering lidar network in China (CARLNET) for routine measurements. In this paper, we focus on the water vapor measurement capabilities of the CARLNET. The uncertainty of the water vapor Raman channel calibration coefficient (Cw) is determined using an error propagation formula. The theoretical relationship between the uncertainty of the calibration coefficient and the water vapor mixing ratio (WVMR) is constructed based on least squares fitting. Based on the distribution of lidar in regions with different humidity conditions, the method of real-time calibration and quality control based on radiosonde data is established for the first time. Based on the uncertainty requirements of the World Meteorological Organization for water vapor in data assimilation, the calibration and quality control thresholds of the WVMR in regions with different humidity conditions are determined by fitting real-time lidar and radiosonde data. Lastly, based on the radiosonde results, the calibration algorithm established in this study is used to calibrate CARLNET data from October to December 2023. Compared with traditional calibration results, the results show that the stability and detection accuracy of the CARLNET significantly improved after calibration in regions with different humidity conditions. The deviation of the Cw decreased from 12.84~18.47% to 5.41~11.54%. The inversion error of the WVMR compared to radiosonde decreased from 1.05~0.46 g/kg to 0.82~0.34 g/kg. The reliability of the improved calibration algorithm and the CARLNET’s performance have been verified, enabling them to provide high-precision water vapor products for NWP.

Research on attenuation rate correlation calibration method based on acoustic variable density logging

In order to solve the problem of logging calibration without a free pipe in the process of acoustic variable density logging and the subjective problem of the free pipe calibration method, this paper studies an attenuation rate calibration method based on acoustic variable density logging. Using the developed acoustic wave probe response relationship device and the acoustic wave probe calibration device, the response consistency of the receiving probe of the acoustic wave instrument and the frequency of the transmitting probe can be calibrated in the laboratory, and the response consistency and frequency calibration coefficient can be obtained. Through this coefficient, the acoustic wave attenuation rate can be derived. After converting the acoustic wave attenuation rate into a relative sound amplitude, the scale coefficients of the well can be obtained. The relative acoustic amplitude of each measuring point in the well can be obtained by using the scale coefficient to evaluate the cementing quality. The acoustic variable density field test of 18 wells in the oilfield shows that the relative acoustic amplitude calculation results obtained by the attenuation rate correlation calibration method are basically consistent with the theoretical values, and the maximum deviation is 6.9%. The deviation is caused by the presence of ahead fluid in the outer annulus of the casing, the presence of trace bubbles in the casing, and the incomplete centering of the instrument. Compared with the traditional free pipe calibration method, the attenuation rate correlation calibration method innovatively replaces the free pipe calibration in the well with the indoor calibration of the acoustic instrument and solves the problem of acoustic variable density calibration when there is no free pipe in cementing quality logging. This method has better accuracy and a greater application range, which lays a foundation for the quantitative interpretation of acoustic variable density.

Assessment of the Representativeness and Uncertainties of CTD Temperature Profiles

CTD profilers are used as reference instruments to qualify temperature and salinity data. Their metrological specifications can be controlled in a calibration bath, and calibration coefficients can be applied to correct the linearity of sensors and the trueness of measured data with a given uncertainty. However, in ocean areas with thermal gradients, the uncertainty of the measured data is questionable due to the thermal inertia of sensors and the movements of the CTD in relation to the roll or pitch of the boat. In order to evaluate these measurement uncertainties and in order to be able to use the upcast profiles, a double C–T sensor SBE 9 profiler was fixed under a carousel water sampler, the second C–T couple being at the top of the carousel frame. This configuration allows the evaluation of the temperature measurement deviations of recorded profiles. In order to quantify the different sources of instrumental uncertainties, the temperature signal has been modelled accounting for the movements induced by the boat. The result allows one to quantify what can be called the representativeness of CTD’s temperature measurements. This notion is very useful in the data assimilation process. A table quantifying the various sources of uncertainty has been created from profiles obtained during four offshore campaigns. In the future, it could be used to find the representativeness of similar profiles obtained with a single pair of sensors. Ship-based CTD profiles are generally considered as perfect or without uncertainty in data assimilation and in the qualification per comparison of other instruments (XBT, Argo profiles, etc.). Our findings imply that this hypothesis will have to be reconsidered.

Local Calibration of Joint Faulting Model by Using Resampling Techniques

The local calibration of the performance models in the Pavement-ME analysis and design procedure is a challenging task mainly because of (a) data needed to characterize the existing pavement sections, (b) nature of the performance models, and (c) inadequate number of pavement sections for a robust calibration. The transverse joint faulting model in the Pavement-ME uses a complex incremental approach. The model has eight (8) different calibration coefficients which can be adjusted to fit the measured faulting. Further, calculating predicted faulting using the published model equations is difficult. The determination of faulting outside the Pavement-ME is essential to calibrate all the model coefficients simultaneously. This paper documents the process of calculating faulting outside of the Pavement-ME by utilizing the inputs specific to the pavement sections. The first step consists of documenting the impact of each calibration coefficient on predicted faulting. This sensitivity assists in determining the most important calibration coefficient in the model. The second step is to calibrate the model by using all the available pavement sections in the calibration dataset. Multiple sampling approaches were utilized to calibrate the faulting model in the State of Michigan. First, the traditional approach uses about 70% of the data for calibration and the remaining 30% for validation. However, most of the States have a limited number of pavement sections for local calibration. Therefore, there is a need to employ statistical methodologies that are more efficient and robust for model calibrations. Second, bootstrapping and jackknifing resampling methods are used to efficiently estimate the model coefficients. The results in the paper show the effect of different sampling approaches on model parameter estimations and compare the role of such parameters in reducing the bias and the standard error of the faulting model.

Sensitivity Evaluation and Optimization of Calibration Coefficients of AASHTOWare Pavement ME Design Jointed Plain Concrete Pavement Faulting Models

The AASHTOWare Pavement ME Design software uses faulting predictions as one of the performance measures for Jointed Plain Concrete Pavement (JPCP) structural designs. Faulting prediction is calculated as the sum of incremental change (monthly) in mean transverse joint faulting during each month in design service life. The faulting prediction computation requires eight input variables along with eight calibration coefficients (C1 to C8). It is obvious that these input variables and calibration coefficients are needed to calibrate JPCP faulting model. However, in the computation of the incremental change in mean transverse joint faulting, three interdependent equations are used, which makes this computation long and complicated. This study evaluates the sensitivity of calibration coefficients associated with the JPCP faulting model for optimization of calibration coefficients under local conditions and presents discussions on the feasibility of developing of a simplified faulting prediction equation having comparable accuracy to AASHTOWare Pavement ME Design faulting prediction equation.

Calibration of backscattering coefficients with coherent heterodyne lidar utilizing molecular scattering.

Coherent heterodyne lidars are typically used for windspeed and attenuated backscattering measurements. The lack of molecular backscattering detection capability has limited the calibrated backscattering measurements until recent advances in coherent lidar technology. In this work, the simultaneous detection of aerosol and molecular backscattering is demonstrated with coherent heterodyne lidar, and the results are compared with a state-of-the-art Raman lidar PollyXT as a reference in a long-range for the first time. The molecular scattering is measured up to 3 km altitude and the extinction of laser power is calculated based on the attenuation. The essential corrections for the molecular scattering data are described and discussed. The backscattering coefficients are calculated with simple high spectral resolution lidar algorithms and the low altitude overlap is calibrated with on-site ceilometer data. A successful high spectral resolution measurement within coherent heterodyne lidar enables the self-calibration of the measured data and removes guessing of the backscatter to extinction ratio and initial values for the typical inversion algorithms. The self-calibration also enables basic aerosol classification based on the measured backscatter to extinction ratios.

A 0.9‐V, 747‐nW Capacitively Biased Diode‐Based Single BJT Branch Bandgap Circuit

ABSTRACTIn this paper, a bandgap reference (BGR) circuit that combines the capacitively biased diode (CBD) structure and the proportional‐to‐absolute‐temperature (PTAT) voltage‐embedded amplifier has been proposed. In order to enhance compatibility with digital domain supply voltage and achieve low power consumption, the supply voltage of the circuit is set at , benefiting from the fact that both the CBD structure and the PTAT‐embedded amplifier can operate at sub‐1 V supply voltages. The circuit generates a complementary‐to‐absolute‐temperature (CTAT) voltage through the CBD structure. PTAT voltage is achieved by a PTAT‐embedded amplifier in a unity‐gain feedback configuration. The calibration of temperature coefficient (TC) is realized by applying a time‐domain trimming method through an on‐chip RC delay circuit. The reference clock frequency is a typical 32‐kHz crystal oscillator clock frequency, enabling high versatility of the circuit. Implemented in 0.13‐m CMOS, measurement results show that the proposed BGR achieves an average temperature coefficient of 28 ppm/°C over −40°C to 125°C, voltage accuracy of 0.25%, power supply rejection (PSR) of , with an active area of , and a power consumption of .

A calibration method for accelerometer combination on centrifuge based on norm-observation method

Abstract To realise the overall calibration of the error model coefficients of accelerometers in an inertial combination and to improve the navigation accuracy of the inertial navigation system, a norm-observation method is applied to the calibration, especially for the quadratic coefficient of the accelerometer. The Taylor formula is used to expand the solution of the acceleration model, and the intermediate variables with error model coefficients are obtained using the least square method. The formulas for calculating the quadratic term coefficient, scale factor and bias of the accelerometer are given. A 20-position method is designed to calibrate the accelerometer combination, the effectiveness of the method is verified by simulation, and the effects of installation misalignment and rod-arm error on calibration accuracy are analysed. The results show that the installation misalignments and rod-arm errors have little influence on the coefficient calibration, less than 10−8, and can be neglected in a practical calibration process.

The importance of method selection when estimating diet composition with quantitative fatty acid signature analysis.

Quantitative fatty acid signature analysis (QFASA) is a common method of estimating the composition of prey species in the diets of consumers from polar and temperate ecosystems in which lipids are an important source of energy. A key characteristic of QFASA is that the large number of fatty acids that typically comprise lipids permits the dietary contributions of a correspondingly large number of prey types to be estimated. Several modifications to the original QFASA methods have been suggested in the literature and a significant extension of the original model published in 2017 allows simultaneous estimation of both diet proportions and calibration coefficients, which are metabolic constants in the model whose values must otherwise be estimated in independent feeding experiments. However, comparisons of diet estimates obtained using different estimation options have been limited. QFASA has been used to estimate the diet composition of several polar bear (Ursus maritimus) subpopulations, including the Southern Beaufort Sea (SBS) subpopulation. Prior QFASA estimates of SBS polar bear diet composition have most often been obtained using variations of the original QFASA model. We investigated the influence of variations in QFASA analytical methods on diet estimates by re-estimating the diet composition of polar bears from the Alaska portion of the SBS using three different methods and found that differences among the three sets of estimates were substantial. Our results illustrate how important the careful and deliberate selection of QFASA methods can be and we provide some guidance on techniques one might use to evaluate options.

Modeling the flow of Oued Cherraa Basin (northeastern Morocco) using the SWAT model and evaluating model performance

River flow modeling is essential for water resource management, particularly in poorly gauged basins such as the Oued Cherraa in eastern Morocco. In these areas, modeling serves as an alternative to direct monitoring, helping to understand hydrological responses to water challenges such as floods, droughts and irrigation needs. The study used the SWAT model (ArcSWAT 2012 version) to simulate river flow in the Oued Cherraa basin. A sensitivity analysis, calibration and validation process were carried out using the sequential uncertainty adjustment method (SUFI-2), which optimized model parameters and ensured accurate reproduction of flows. The model was refined to better capture the basin’s complex hydrological processes, providing critical data for water management and planning. Key parameters analyzed include the deep aquifer percolation fraction (RCHRG_DP), the calibration coefficient for normal flow, and the curve number (CN2). The results showed a strong correlation between observed and simulated flow data. The mean monthly flow at Tazarhine (on Oued Zegzel) was 0.99 m³/s, with a simulated value of 1.076 m³/s during calibration and validation.

Key comparison BIPM.RI(I)-K1 of the air-kerma standards of the NIST, USA and the BIPM in 60Co gamma radiation

Main textA new key comparison of the standards for air kerma of the National Institute of Standards and Technology (NIST), USA, and the Bureau International des Poids et Mesures (BIPM) was carried out in the 60Co radiation beam of the BIPM in October 2023. The comparison result, based on the calibration coefficients for two transfer chambers and expressed as a ratio of the NIST and the BIPM standards for air kerma, is 1.0044 with a combined standard uncertainty of 3.4 parts in 103. The result agrees within the uncertainties with the indirect comparison carried out in 2011, when updated with the changes implemented to the standards at each laboratory. The results are analysed and presented in terms of degrees of equivalence, suitable for entry in the BIPM key comparison database.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Calibration of Linear Muskingum Model Coefficients and Coefficient Parameters Using Grey Wolf and Particle Swarm Optimization

Calibration of Linear Muskingum Model Coefficients and Coefficient Parameters Using Grey Wolf and Particle Swarm Optimization

Applying Photoelectric Sand Meter for Monitoring of Suspended Solid Matter in Rivers

River ecosystems are integral to sustainable environmental development, playing a crucial role in understanding suspended solid matter (SSM) transport dynamics and soil conservation. Accurate monitoring of SSM concentrations in watersheds is foundational for these studies. This research introduces and evaluates a novel HHSW·NUG-1 photoelectric sand meter, specifically designed for SSM measurement. Its reliability was validated at three hydrological stations, including Xiaolangdi. The instrument, based on light scattering principles, is optimized for environments with high SSM loads and rapid flow rates. Laboratory tests indicate a measuring range of 0 to 730 kg/m3, and field trials show effective operation within 0 to 375 kg/m3, meeting the monitoring needs of hydrological stations. Through comparative analysis of measurement data, we established conversion relationships for various SSM concentration ranges, confirming that the instrument’s system error is less than 1%. The photoelectric sand meter adheres to standards outlined in the “Guidelines for SSM Test in Rivers”, demonstrating stability in reliability, calibration methods, observation accuracy, real-time monitoring, data storage, and continuous operation. For optimal use, adherence to relevant hydrological instrument standards is recommended, particularly in stations requiring SSM analysis. Standard sampling and calibration of conversion coefficients should be conducted, and proper sensor installation is crucial to avoid interference from flow conditions. In conclusion, the HHSW·NUG-1 optoelectronic sand meter exhibits stable and reliable performance in practical applications, with broad potential for rapid deployment in other river hydrological stations.

Portable Gamma Irradiation System with <sup>57</sup>Co, <sup>133</sup>Ba, <sup>137</sup>Cs, and <sup>60</sup>Co for On-site Calibration of Environmental Radiation Monitoring Devices

Background: A portable gamma irradiation system was developed for on-site calibration of environmental radiation monitoring devices. This system uses a portable collimator and gammaray sources such as <sup>57</sup>Co, <sup>133</sup>Ba, <sup>137</sup>Cs, and <sup>60</sup>Co, each with a nominal radioactivity of 10 MBq or less.Materials and Methods: The air kerma rate produced by the system was measured using a spherical ionization chamber with a 10 L sensitive volume. This chamber was calibrated against the air kerma rate measured with primary standard ionization chambers. The system’s performance was evaluated by comparing the calibration results of the monitoring device with those obtained using a conventional method involving a standard ionization chamber for outdoor use.Results and Discussion: The calibration coefficient of the spherical ionization chamber was within ±2% of the value of 3.029×103 Gy/C for photon energies ranging from 50 keV to 1,250 keV. The air kerma rate of the irradiation field produced by the portable system ranged from 0.17 μGy/hr to 4.7 μGy/hr, with an uncertainty between 2.4% and 10%. The calibration coefficients obtained using the portable system were consistent with those from the conventional method.Conclusion: The portable gamma irradiation system offers significant advantages, including preventing damage to standard ionization chambers used outdoors and reducing measurement time compared to conventional methods. Due to its ease of construction and implementation, the system is expected to be useful not only for on-site calibration of monitoring devices but also in various radiation safety management scenarios.

Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes

Abstract This work presents procedures for implementing machine learning methods into existing algorithms for multihole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1% in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both overfitting and underfitting.

Calibration coefficients of epitaxial diodes used in diagnostic radiology and computed tomography beams

The assessment of the calibration coefficients of an epitaxial diode, previously characterized for diagnostic radiology (RQR qualities) and computed tomography (RQT qualities) dosimetry, is reported in this work. The diode, with an n-type epitaxial layer (50 µm) grown on a thick (300 µm) Czochralski silicon substrate, is directly connected to an electrometer Keithley 6517B in the photovoltaic mode and exposed to X-ray beams from a Pantak/Seifert generator, model Isovolt 160 HS. Under this operational condition, the dosimetric quantity is the dose rate correlated with the output current signal from the diode when exposed to radiation. The corresponding collected charge (the integral of the current signal) is proportional to the dose. The repeatability of the current signals and the dose response of the diode are investigated in several RQR and RQT beam qualities spanning from 50 kV to 150 kV. The calibration coefficients (Nk) are assessed by linking the diode readings to those from the standard ionization chambers for reference beam qualities (RQR-5 and RQT-9) following the formalism recommended by Technical Reports Series No. 457. To take into account the variations of the X-ray beams related to the correspondent reference beam qualities, the correction factors are also calculated. Despite being close to 1, they evidence the energy dependence of the EPI diode for voltages above 100 kV.

BetaSigmaSlurryFoam: An Open Source Code for the Numerical Simulation of Pseudo-Homogeneous Slurry Flow in Pipes

In this study, we present, test, and make available to the scientific community the betaSigmaSlurryFoam solver, which is a two-phase model based on the Eulerian-Eulerian approach for the simulation of turbulent slurry transport in piping systems. Specifically, betaSigmaSlurryFoam is a fully open source implementation, within the OpenFOAM platform, of the existing β-σ two-fluid model, developed over a decade by researchers at Politecnico di Milano, which, as certified by scientific publications, proved an effective way to simulate the pipe flow of fine particle slurries in the pseudo-homogeneous regime. In this paper, we first provide the mathematical and coding details of betaSigmaSlurryFoam. Afterwards, we verify the new solver by comparison with the earlier β-σ two-fluid model for the case of slurry transport in a horizontal pipe, demonstrating not only that the two solutions are very close to each other, but also that the effects of the two calibration coefficients β and σ are the same for the two implementations. Finally, we apply betaSigmaSlurryFoam to the more complex case of slurry transport in a horizontal pipe elbow, which has never been subject to investigation using the earlier β-σ two-fluid model. We prove that the solution of betaSigmaSlurryFoam is physically consistent, and, after assessing the impact of β and σ through an extensive sensitivity analysis, we show that reasonably good agreement could be achieved against experimental data reported in the literature even for slightly different particle sizes than those considered in our previous research. The sharing of betaSigmaSlurryFoam as open source code promotes its further development by fostering collaboration between research groups worldwide.

A high-precision calibration method for nonlinear error coefficients of accelerometer components

Abstract With the continuous improvement of measurement accuracy requirements for inertial devices, how to accurately calibrate nonlinear error coefficients of accelerometer components has become an important factor affecting the accuracy of the inertial navigation system. Centrifuge calibration method can continuously provide a specific force greater than 1g, which can fully excite the nonlinear errors of accelerometer components and is a commonly used method for calibrating nonlinear error coefficients. However, on the one hand, traditional centrifuge speeds are often selected based on empirical experience, lacking a scientific determination method. This can lead to a decrease in the calibration accuracy of nonlinear error coefficients. On the other hand, the inability to accurately model the highly complex and time-varying test errors during actual calibration further reduces the calibration accuracy. Therefore, a high-precision calibration method for nonlinear error coefficients is proposed. Firstly, by introducing G-optimal experimental design criterion to minimize the maximum scaled prediction variance of output prediction values, the optimal speed combination is designed to achieve the highest accuracy in estimating nonlinear error coefficients. Based on the idea of semi-parametric regression, system errors caused by calibration test errors are treated as parameters to be estimated, and a high-precision nonlinear error coefficient calibration model is established. Then the influence of calibration test errors is eliminated by estimating and compensating the system errors. Centrifuge calibration test results show compared with the traditional method, the ranges and standard deviations of the repeated calibration results of the proposed method are reduced by more than 80.37% and 63.01%. This indicates that the proposed method can effectively eliminate the influence of calibration test errors and achieve high-precision calibration of nonlinear error coefficients.

High-throughput near-infrared spectroscopy for detection of major components and quality grading of peas

Pea (Pisum sativum L.) is a nutrient-dense legume whose nutritional indicators influence its functional qualities. Traditional methods to identify these components and examine the relationships between their contents could be more laborious, hindering the quality assessment of the varieties of peas. This study conducted a statistical analysis of data about the sensory and physicochemical nutritional attributes of peas acquired using traditional techniques. Additionally, 90 sets of spectral data were obtained using a portable near-infrared spectrometer, which were then integrated with chemical values to create a near-infrared model for the basic ingredient content of peas. The correlation analysis revealed significant findings: pea starch displayed a substantial negative correlation with moisture, crude fiber, and crude protein, while showing a highly significant positive correlation with pea seed thickness. Furthermore, pea protein exhibited a significant positive correlation with crude fiber and crude fat. Cluster analysis classified all pea varieties into three distinct groups, successfully distinguishing those with elevated protein content, high starch content, and low-fat content. The combined contribution of PC1 and PC2 in the principal component analysis (PCA) was 51.2%. Partial least squares regression (PLSR) and other spectral preprocessing methods improved the predictive model, which performed well with an external dataset, with calibration coefficients of 0.89–0.99 and prediction coefficients of 0.71–0.88. This method enables growers and processors to efficiently analyze the composition of peas and evaluate crop quality, thereby enhancing food industry development.