Numerical Correction Research Articles

ПОТОЧЕЧНЫЕ И КОМПЛЕКСНЫЕ МЕРЫ КАЧЕСТВА В ИССЛЕДОВАНИЯХ АТМОСФЕРЫ И ОКЕАНА: ОБЗОР МЕТОДОВ И ПОДХОДОВ

In the oceanic and atmospheric sciences, various general quantitative indicators, or quality metrics, describe the quality of the various modeling products, including numerical weather prediction, statistical correction, and downscaling. Metrics provide the level of accuracy of model processes reproduction and allow for comparison of models by estimating the uncertainty of their results. This paper presents a classification of the most frequently encountered quality metrics in the scientific literature. Examples are given for each group of quality metrics. In addition to assessing traditional point-by-point metrics, complex metrics that consider various aspects of modeling results are studied. Such specific metrics have an emphasis on the spatial structure, internal correlations, and heterogeneity of the predicted variable fields, ensemble forecasts etc. Special attention in this paper is also devoted to the object-oriented metrics or metrics based for rare and extreme events.

Research on modulation aberration numerical correction method of interference signals for high aspect ratio samples utilizing coherence scanning interferometry

Research on modulation aberration numerical correction method of interference signals for high aspect ratio samples utilizing coherence scanning interferometry

A high-performance ray tracing particle tracking model for the simulation of microplastics in inland and coastal aquatic environments

In this study, we present a high-performance Particle Tracking Model (PTM) designed for simulating any type of particles, with a focus on microplastics. The PTM is efficient compared to existing models, parallelized, and utilizes a ray tracing algorithm incorporating both ray reflection and ray refraction in order to traverse particles as well as find the location of each particle over three-dimensional unstructured grids. Various numerical corrections are implemented in the model to address computational round-off errors and discontinuities in the water surface level of the input hydrodynamic models. To increase the accuracy of the model, partially reflective boundary conditions are imposed as well as the capability to simulate microplastics beaching and washout in very shallow areas or dry computational cells. Several tests are conducted to study the performance, scalability, and accuracy of the model. The proposed model is tested with over 3.88 billion double-precision particles on three-dimensional computational grids with up to approximately one million cells. The tests show that the ray tracing approach is efficient, achieves over 17× faster runtime, and offers greater accuracy compared to using an auxiliary grid for particle location finding. For larger timesteps, the ray tracing PTM with refraction shows improved accuracy compared to the ray tracing PTM without refraction. The model's capabilities are tested in a real-world case study over the Saguenay Fjord, Quebec, Canada. The model is utilized to reproduce the paths of five surface drifters. A second numerical test is conducted in the Fjord and high particle concentration areas are identified.

An actuator space optimal kinematic path tracking framework for tendon-driven continuum robots: Theory, algorithm and validation

Path tracking of continuum robots is a fundamental and crucial problem across various applications. In this article, we address this problem by focussing on three aspects. Firstly, we propose an efficient multi-solution inverse kinematics solver for three-section constant curvature robots by bridging the theoretical reduction and the numerical correction. Secondly, we derive a linear tendon-driven actuation model, establishing the connection between the robot configuration space and the actuator space. With this model, we achieve optimal distance planning and optimal time allocation considering the constraints of actuator velocity and acceleration, generating a continuous trajectory directly in the actuator space. Finally, we present our kinematic path tracking framework, which includes offline optimal trajectory planning and online feedforward and feedback control. Experiments are conducted both in simulations and in the real world on our three-section tendon-driven continuum robot. The experiments validate the increased efficiency, higher success rates, and accessibility of multiple solutions offered by our inverse kinematics solver, as well as the optimality in distance planning and time allocation in the actuator space. Performance improvements in tracking accuracy are demonstrated through comparative experiments and the application of our framework in path tracking tasks with obstacles is presented through a case study.

A gas-surface interaction algorithm for discrete velocity methods in predicting rarefied and multi-scale flows: For Maxwell boundary model

The discrete velocity method (DVM) for rarefied flows and the unified methods (based on the DVM framework) for flows in all regimes, from continuum one to free molecular one, have worked well as precise flow solvers over the past decades and have been successfully extended to other important physical fields. Both DVM and unified methods endeavor to model the gas-gas interaction physically. However, for the gas-surface interaction (GSI) at the wall boundary, they have only use the full accommodation boundary up to now, which can be viewed as a rough Maxwell boundary with a fixed accommodation coefficient (AC) at unity, deviating from the real value. For example, the AC for metal materials typically falls in the range of 0.8 to 0.9. To overcome this bottleneck and extend the DVM and unified methods to more physical boundary conditions, an algorithm for Maxwell boundary with an adjustable AC is established into the DVM framework. The Maxwell boundary model splits the distribution of the bounce-back molecules into specular ones and Maxwellian (normal) ones. Since the bounce-back molecules after the spectral reflection does not math with the discrete velocity space (DVS), both macroscopic conservation (from numerical quadrature) and microscopic consistency in the DVS are hard to achieve in the DVM framework. In this work, this problem is addressed by employing a combination of interpolation methods for mismatch points in DVS and an efficient numerical error correction method for micro-macro consistency. On the other hand, the current Maxwell boundary for DVM takes the generality into consideration, accommodating both the recently developed efficient unstructured velocity space and the traditional Cartesian velocity space. Moreover, the proposed algorithm allows for calculations of both monatomic gases and diatomic gases with internal degrees in DVS. Finally, by being integrated with the unified gas-kinetic scheme within the DVM framework, the performance of the present GSI algorithm is validated through a series of benchmark numerical tests across a wide range of Knudsen numbers.

Investigating the impact of nano copper on the electrical conductivity of aluminum: a comprehensive study utilizing genetic algorithms methods

Nanotechnology has emerged as a transformative field in material science, offering unprecedented opportunities to enhance the electrical properties of conventional materials through the incorporation of nano-sized particles. In this extensive study, we explore the effects of varying weight percentages (1%, 2%, and 3%) and lengths (30 nm, 60 nm, 150 nm, and 250 nm) of nano copper on the electrical conductivity of aluminum (Al) across different temperatures (20°C, 50°C, 80°C, and 100°C). Additionally, GA method and Numerical correction factor and constitutive equation using Zener –Holloman parameter were employed for modeling the rheological behavior of Al based on these parameters.

Robust baseline correction for Raman spectra by constrained Gaussian radial basis function fitting

Accurate baseline correction is a fundamental requirement for extracting meaningful spectral information and enabling precise quantitative analysis using Raman spectroscopy. Although numerous baseline correction techniques have been developed, they often require meticulous parameter adjustments and yield inconsistent results. To address these challenges, we have introduced a novel approach, namely constrained Gaussian radial basis function fitting (CGF). Our method involves solving a curve-fitting problem using Gaussian radial basis functions under specific constraints. To ensure stability and efficiency, we developed a linear programming algorithm for the proposed approach. We evaluated the performance of CGF using simulated Raman spectra and demonstrated its robustness across various scenarios, including changes in data length and noise levels. In contrast to standard methods, which frequently require complicated parameter adjustments and may exhibit varying errors, our approach provides a simple parameter search and consistently achieves low errors. We further assessed CGF using real Raman spectra, leading to enhanced accuracy in the quantitative analysis of the Raman spectra of chemical warfare agents. Our results emphasize the potential of CGF as a valuable tool for Raman spectroscopy data analysis, significantly advancing sophisticated analytical techniques.

Unified momentum model for rotor aerodynamics across operating regimes

Despite substantial growth in wind energy technology in recent decades, aerodynamic modeling of wind turbines relies on momentum models derived in the late 19th and early 20th centuries, which are well-known to break down under flow regimes in which wind turbines often operate. This gap in theoretical modeling for rotors that are misaligned with the inflow and also for high-thrust rotors has resulted in the development of numerous empirical corrections which are widely applied in textbooks, research articles, and open-source and industry design codes. This work reports a Unified Momentum Model to efficiently predict power production, thrust force, and wake dynamics of rotors under arbitrary inflow angles and thrust coefficients without empirical corrections. The Unified Momentum Model is additionally coupled with a blade element model to enable blade element momentum modeling predictions of wind turbines in high thrust coefficient and yaw misaligned states without using corrections for these states. This Unified Momentum Model can form a new basis for wind turbine modeling, design, and control tools from first principles and may enable further development of innovations necessary for increased wind production and reliability to respond to 21st century climate change challenges.

Chondrule sizes within the CM carbonaceous chondrites and measurement methodologies

AbstractThe sizes of chondrules are a valuable tool for understanding relationships between meteorite groups and the affinity of ungrouped chondrites, documenting temporal/spatial variability in the solar nebula, and exploring the effects of parent body processing. Many of the recently reported sizes of chondrules within the CM carbonaceous chondrites differ significantly from the established literature average and are more closely comparable to those of chondrules within CO chondrites. Here, we report an updated analysis of chondrule dimensions within the CM group based on data from 1937 chondrules, obtained across a suite of CM lithologies ranging from petrologic subtypes CM2.2 to CM2.7. Our revised average CM chondrule size is 194 μm. Among the samples examined, a relationship was observed between petrologic subtype and chondrule size such that chondrule long‐axis lengths are greater in the more highly aqueously altered lithologies. These findings suggest a greater similarity between the CM and CO chondrites than previously thought and support arguments for a genetic link between the two groups (i.e., the CM‐CO clan). Using the 2‐D and 3‐D data gathered, we also apply numerous stereological corrections to examine their usefulness in correcting 2‐D chondrule measurements within the CM chondrites. Alongside this analysis, we present the details of a standardized methodology for 2‐D chondrule size measurement to facilitate more reliable inter‐study comparisons.

AMÉLIORATION DE LA CORRECTION DU FACTEUR DE PUISSANCE DANS UN CONVERTISSEUR AC-DC AVEC UN CIRCUIT BOOST-FLYBACK INTÉGRÉ

Due to advancements in semiconductor technology, modern controllers, and nonlinear characteristics, loads are increasingly used in industrial applications. Because of nonlinear traits, harmonics are introduced in the main supply, and the supply current is distorted. Numerous power factor correction (PFC) converter topologies are established for proper wave shaping. The integration of conventional PFC converters produces superior results in high voltage on the output side and improves efficiency. A single-stage boost flyback integrated PFC converter is investigated in this paper, which provides high efficiency, high output voltage, and less voltage stress. This converter uses a lesser number of components, which results in a compact size and low cost. The enhancement of power factor, efficiency, and wave shaping of the current waveform is realized through various control techniques. A novel current sensorless control for the integrated topology is investigated, and it is shown that it provides better total harmonic distortion and current wave shaping than other control techniques. A 48 W, 12/48 V prototype model is implemented to validate the simulation results practically.

Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the NRMSE by 3.82 % and NMAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system.

The characteristics and corrections of ventral support interferences in the transonic-speed wind tunnel for the blended-wing-body aircraft

For the problem of ventral support interference in a transonic-speed wind tunnel with the blended-wing-body aircraft NPU-BWB-300 installed, the numerical simulation method based on Reynolds-averaged Navier–Stokes (RANS) equations is used to study the influence law of aerodynamic characteristic interference with the variation of Mach numbers and angles of attack. Moreover, the characteristics of ventral support interference for blended-wing-body aircraft and conventional aircraft are compared. The relevant mechanism of the generation and change of ventral support interference is revealed by employing analysis of the body surface pressure, the shock wave of the strut, and the separation area between the strut and the aircraft. The aerodynamic characteristic interference obtained from the numerical simulation is linearized based on the principle of the least square method. Afterward, a numerical simulation correction method of ventral support interference in the transonic-speed wind tunnel for the blended-wing-body aircraft is developed. Finally, the test results after the corrections of ventral support interferences in the transonic-speed wind tunnel for NPU-BWB-300 are obtained, which is significant for the evaluation of current aerodynamic performances and subsequent optimization designs.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BackgroundComprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. ResultTo tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SignificanceData analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Forensic geoscience non-invasive detection and characterisation of underground clandestine complexes, bunkers, tunnels and firing ranges

Recent events in conflict zones have emphasized that the successful detection and characterisation of buried clandestine complexes, bunkers and tunnels is vitally important for forensic investigators globally, to reduce or solve criminal activities, address national security threats and avoid potential terrorist attacks. However, this can often prove very difficult, particularly in urban areas, with potentially both below-ground non target items and above-ground infrastructures present, that can interfere with detecting target(s).Here we provide selected successful case studies where forensic geoscience techniques were used to detect and characterise buried clandestine complexes, bunkers and tunnels using different geophysical techniques. Generally, desktop studies assessing pre-existing information, including local geology, soils, historical/modern remote sensing, maps and photographs inform appropriate geophysical survey technique(s) selection. Subsequent near-surface geophysical techniques are then employed to produce accurate plans of sub-surface targets, with numerical modelling and correction for the interfering effects of above ground infrastructure, enabling the calibration of geophysical datasets to provide confidence in their respective interpretations.All forensic investigations are, of course, unique to every site, and thus require an individual approach to their respective ground conditions. Investigations should be both phased and iterative, with techniques tailored to local conditions: the selection of geophysical method(s) is crucial to improve successful detection rates of such important buried targets.

Short-Term Forecasting of Wind Power Based on Error Traceability and Numerical Weather Prediction Wind Speed Correction

Short-Term Forecasting of Wind Power Based on Error Traceability and Numerical Weather Prediction Wind Speed Correction

Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions

Accurate solar irradiance forecasts can make solar power forecasts more reliable, which can help the power grid dispatch reasonably. In practice, Numerical Weather Prediction (NWP) is widely applied in solar irradiance forecasts. In this paper, the statistical NWP Global Horizontal Irradiance (GHI) error analysis shows that the characteristics of NWP GHI error vary obviously under different weather conditions. However, existing correction methods are not designed contrapuntally for different weather conditions, resulting in poor correction performance. To solve this problem, a hybrid method is proposed to get day-ahead correction results for NWP GHI. Specifically, the hybrid method consists of three key parts, including Deep Clustering (DC), Variational Mode Decomposition (VMD), and an Encoder–Decoder based Correction model (EDC). DC is used to categorize the historical samples into three clusters, the input of which is the multi-dimensional information series containing observed data and NWP data. After a feature selection by VMD, the correction models are trained on each cluster respectively. The performance of the proposed model is evaluated with a public dataset and an actual field dataset, and the results demonstrate that the accuracy has been effectively improved by the proposed method compared with other models. In addition, we find that adopting VMD is effective in improving the correction accuracy, and the root mean square error is reduced by 5.70% and 9.32% compared with models without it.

Modeling of Styl'One Evolution Correction Factors for Multicomponent Mixtures Scaling-up to Roller Compaction

Roll compaction (RC) is a cost-effective dry granulation method, widely implemented in the pharmaceutical industry. In early formulation development however, when the material availability is limited, being able to predict the most important parameters in RC, like gap width and specific compaction force (SCF), to obtain a target ribbon solid fraction (SF) would significantly improve the formulation development efficiency as it would avoid the need of performing experiments on the roller compactor itself. However, at the present state of things, experiments on RC mechanical simulators present an overestimation of the target SF, when compared to roller compactor SF values. Although numerous correction approaches have been developed to improve the predictive performance of different mathematical models applied to the simulation experimental results, no study has collected a database wide enough to demonstrate the validity of a correction factor that allows to accurately simulate the compaction behavior of multicomponent mixtures. Here, 25 different formulations at 40 % drug load are compacted at different SCFs, both on a RC mimicking device (Styl'One Evolution) and on an actual roller compactor (Gerteis Mini-Pactor): following a similar approach as Reimer et al. and implementing a simplified version of the Johanson's mathematical model, 4 different correction factors are calculated, depending on how their material properties and pressure dependencies are considered. In conclusion, one correction factor is identified as the optimal trade-off between the SF prediction accuracy on the Gerteis Mini-Pactor and its applicability to a wide range of formulations, as it is independent of the material properties. This finding is particularly relevant when applied to scale-up to this specific roller compactor or early development processes of new formulations that have not been mechanically characterized yet.

Fragile points method for Euler–Bernoulli beams

In this paper, the recently introduced Fragile Points Method (FPM) is extended to study the static bending, free vibration, and mechanical buckling of isotropic and homogeneous case as well as functionally graded Euler–Bernoulli beams. The beam kinematics is based on the Euler–Bernoulli theory that assumes plane sections remain plane and perpendicular to the neutral axis of the deformed beam. The salient feature of the FPM is that it is a truly meshless method that employs simple local point-based polynomial test and trial functions. The key distinction is that the polynomial test and trial functions are discontinuous and constructed using radial basis functions, in contrast to the conventional Galerkin framework. Further, as the trial and test functions are discontinuous, the continuity requirement imposed by the continuous Galerkin framework is circumvented. The discontinuous trial and test functions lead to inconsistency; to alleviate this, we employ numerical flux corrections inspired by the discontinuous Galerkin method. The efficiency and robustness of the approach are tested with a few standard benchmark examples.

Numerical 1-loop correction from a potential yielding ultra-slow-roll dynamics

Single-field models of inflation might lead to amplified scalar fluctuations on small scales due, for example, to a transient ultra-slow-roll phase. It was argued by Kristiano & Yokoyama in ref. [1] that the enhanced amplitude of the scalar power spectrum on small scales has the potential to induce a sizeable 1-loop correction to the spectrum at large scales. In this work, we repeat the calculation for the 1-loop correction presented in ref. [1]. We closely follow their assumptions but evaluate the loop numerically. This allows us to consider both instantaneous and smooth transitions between the slow-roll and ultra-slow-roll phases. In particular, we generate models featuring realistic, smooth evolution from an analytic inflationary potential. We find that, upon fixing the amplitude of the peak in the power spectrum at short scales, the resulting 1-loop correction is not significantly reduced by considering a smooth evolution. In particular, for a power spectrum with a tree-level peak amplitude potentially relevant for small-scale phenomenology, e.g. primordial black hole production, the 1-loop correction on large scales is a few percent of the tree-level power spectrum.

High-Sensitivity Polarization Sensitive Optical Coherence Tomography Based on Numerical Correction for Perfect Circularly Polarized Light

Polarization sensitive optical coherence tomography (PSOCT) makes use of the birefringence information of the sample to compensate for the lack of internal tissue-specific contrast in conventional optical coherence tomography (OCT). Circularly polarized light is always used as an incident beam in PSOCT, but it is difficult to have perfect in practice. The manual calibration method of circularly polarized light suffers from the problems of complicated calibration operation and lack of stability. This study proposes a simple method to enhance the imaging of PSOCT without altering the system. A numerical calibration of circularly polarized light can be implemented using the original system setup, ensuring that the system’s complexity remains unchanged. Enhancements in imaging quality can be achieved through an algorithmic analysis of the captured interference fringe data. This calibration is applied in the field map of interference data before being quadrature-assembled. Notably, the proposed approach achieves high sensitivity in PSOCT. The birefringence image shows a more obvious layered structure. Improvements in the signal-to-noise ratio (SNR) were demonstrated for chicken breast, pork, and beef imaging at about 20 dB.