Numerical Prediction Research Articles

Short-term wind power combination forecasting method based on wind speed correction of numerical weather prediction

Short-term wind power combination forecasting method based on wind speed correction of numerical weather prediction

Machine Learning and Weather Model Combination for PV Production Forecasting

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

Three-dimensional Effects on Gap-Resonances in Twin-Hull Vessels in Time-Harmonic Vertical Oscillations

Three-dimensional effects induced by dimensional ratios on the gap resonances happening in twin hull vessels oscillating in forced vertical motion have been analyzed. They can lead to relevant consequences, such as the amplification of the inner radiated waves or the generation of standing waves in between the demi-hulls, that can have a direct effect on the operating profile of the vessel. The response of twin hull vessels in waves can be strongly affected by these resonant phenomena. Also, some of these behaviors can be exploited in the framework of wave energy conversion systems. The present analysis is carried out by using an open-source, linear, Boundary Element Method (BEM), based on the Green function approach. Mathematical backgrounds of the added mass and damping coefficients computation for a floating body under harmonic vertical oscillation are provided as well as details of the numerical discretization used in the BEM. A panel mesh sensitivity study is carried out and the numerical prediction is validated by comparison against available experimental data, another CFD solution obtained by a high-fidelity viscous solver based on the open-source libraries Open-FOAM and approximate analytic formulations. The effect of the beam ratio and the length-to-beam ratio on the resonant phenomena has been analyzed. This has been achieved by systematic variations of the geometric dimensions of the hull, focusing on the trends of the hydrodynamic coefficients, the amplitude of the radiated waves, and the location of the resonant frequencies over the analyzed range.

How well can global ensemble forecasts predict tropical cyclones in the southwest Indian Ocean?

AbstractThe southwest Indian Ocean (SWIO) recently experienced its most active, costliest and deadliest cyclone season on record (2018–2019). The anticipation and forecasting of natural hazards, such as tropical cyclones, are crucial to preparing for their impacts, but it is important to understand how well forecasting systems can predict them. Despite the vulnerability of the SWIO to tropical cyclones, comparatively little research has focused on this region, including understanding the ability of numerical weather prediction systems to predict cyclones and their impacts in southeast Africa. In this study, we evaluate ensemble probabilistic and high‐resolution deterministic forecasts of tropical cyclones in the SWIO from 2010 to 2020, using two state‐of‐the‐art global forecasting systems: one from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the other from the U.K. Met Office. We evaluate predictions of the track, assessing the location of the centre of each storm and its speed of movement, as well as its intensity, looking at maximum wind speeds and minimum central pressure, and discuss how the forecasts have evolved over the 10‐year period. Overall, ECMWF typically provides more accurate forecasts, but both systems tend to underestimate translation speed and intensity. We also investigate the impact of the Madden‐Julian Oscillation (MJO) on tropical cyclones and their forecasts. The MJO impacts where and when tropical cyclones form, their tracks and intensities, which in turn impacts forecast skill. These results are intended to provide an increased understanding of the ability of global forecasting systems to predict tropical cyclones in the SWIO, for the purpose of decision making and anticipatory action.

A new experimental and numerical simulation method for measuring ash slag fluidity using VOF based on OpenFOAM

A numerical computational model of heated ash flow is proposed using the Volume of Fluid (VOF) method based on OpenFOAM. Established numerical model can accurately calculate the critical flow temperature (Tc) and critical flow velocity (Vc) of ash slag with the error of 1.5 % and 19 % from the experiment, respectively. Forty-five types of ash slag with widely varied properties in China were calculated based on the established model. Results show that high levels (>30 wt%) of Fe2O3 and CaO improve the flowability of the ash slag, and low levels of Al2O3 (10 wt%) also contribute to the flowability of the ash slag. Tc and Vc of the ash were distributed in the range of 1150 ∼ 1400 °C and 0.1 ∼ 1.0 mm/min, respectively. Tc and Vc change in opposite trends with the component content increases, indicating that a higher Tc corresponds to a lower Vc and fluidity reduction. Mathematical models for calculating the Tc and Vc of ash were obtained based on non-linear multiple regression and correlation coefficient, and prediction accuracy was within 10 % and 30 % for Tc and Vc, respectively. The proposed numerical simulation method and prediction model can provide an accurate reference for a wide range of ash fluidity. Also, it was demonstrated that elemental Na has a positive effect on ash flowability based on the model, which is important for measuring slag flowability in slag-tap furnaces.

Low-cycle fatigue crack growth in brittle materials: Adaptive phase-field modeling with variable-node elements

This work presents an adaptive phase field framework for the simulation of crack nucleation and propagation in brittle materials subject to low-cycle loading. We introduce a fatigue history strain parameter within the phase-field framework to capture the fatigue effect. The resulting coupled differential equations are solved using a staggered iteration scheme. In order to improve the computational efficiency of the phase-field model, an adaptive refinement scheme is introduced. This scheme utilizes an artificial threshold that takes into consideration of both phase-field variables and cumulative fatigue history field variables to determine the elements to be refined. The handling of the hanging nodes resulting from mesh refinement is accomplished through the application of the variable-node element technique, offering a flexible and efficient mesh integration technique. We validate our proposed numerical scheme through five representative numerical examples: single-edge cracked specimen, compact tension specimen, double-edge cracked panel, plate with a hole and plate with multi-cracks and hole. The results demonstrate that the adaptive mesh refinement scheme significantly reduces computational costs without compromising the accuracy of the numerical predictions.

Physics-based modeling of the effective gas transport properties of single jersey knitted fabrics based on images

In many processes and applications, the performance of textiles relies heavily on fluid transport; for example, the in-plane distribution of water and the through-plane permeation of water vapor and air. Prediction of knitted fabrics’ effective transport characteristics can enhance development workflows and bring them to new applications. Effective transport parameters that are particularly important are the permeability and diffusive mass transfer. Usually, experiments are used to determine these parameters. It is desirable to conduct a thorough investigation into how yarn structure and knitting gauge influence these properties to tailor knitted fabrics for a particular application. Our contribution in this context describes a consistent workflow to forecast the effective mass transfer characteristics of single jersey fabrics. Single jersey fabrics have been chosen for they are the simplest patterning, and are widely used in body-near worn garments. The proposed approach involves visualizing fabric samples with a light microscope, and subsequently determining relevant geometric parameters through automated image processing algorithms. With these parameters in hand, a representative elementary volume of the fabric is constructed. The yarn is modeled as an effective medium to reduce calculation time. The representative elementary volume is then used for numerical predictions of air permeability and the diffusive water vapor transport. The predicted through-plane gas transport properties are compared with experimental data to validate the approach. Six different single jersey polyester fabrics were analyzed, with different yarn structures and machine gauges. The comparison shows a good agreement between simulated and measured transport properties.

Deep learning-based ensemble forecast and predictability analysis of the Kuroshio intrusion into the South China Sea

Abstract The Kuroshio intrusion (KI) into the South China Sea (SCS) significantly affects the environment, ecology, and climate change of the SCS. However, due to the nonlinearity of KI, its numerical prediction often requires large ensemble size to measure prediction uncertainty. The huge computational costs of large numbers of members and high-resolution numerical models pose significant challenges for KI prediction. We, therefore, construct a Kuroshio ensemble deep learning prediction system (KurNet) through taking different values of parameters to predict KI paths because the deep learning models have high prediction skills and low computational cost. The KurNet containing 64 ensemble members can not only output ensemble mean forecast result of the Kuroshio path, but also estimate probability density functions for the path types. The KurNet illustrates a high predictive ability for the KI with the mean classification accuracy of 71.9% and root mean square error of 0.913 on the testing set, which is superior to the single control prediction by ∼1.0–2.9%, although the control prediction model is selected as one of the ensemble members with the best predictive ability on the validation set. Furthermore, the predictability analysis of 10 KI events indicates that when the lead time is 3 days, the most important areas are in the east of Luzon Island due to the upstream Kuroshio transport. As the lead time increases, the most important area is in the Luzon Strait due to the eddy activity. Observing system simulation experiments reveal that the KI forecast skill can be enhanced by ∼12–18%, when uncertainties of the input data in these important regions are removed.

Numerical Prediction Uncertainty and Data Worth Analysis of Solute Transport in an Agricultural Clay Till Setting With Preferential Flow

AbstractWith modern agricultural practices, it is essential to quantify flow and solute transport fluxes by numerical models and associated predictions. A major challenge in modeling preferential flow settings is the ability to constrain the often numerous parameters needed to physically represent these systems. Following this, there is a lack of understanding of what parameters and observations carry the most worth for a model to reduce its prediction uncertainty. Here, first‐order second moment (FOSM) analyses were used for a heavily monitored clay till field with preferential flow to investigate which parameters and observation types contribute the most to reducing the uncertainty of bromide predictions at various depths. Using a multi‐objective regularized optimization approach, a 1‐D preferential flow model was calibrated and subjected to FOSM analyses. Key parameters contributing to the prediction uncertainty of bromide concentrations in 0.25–3 m were limited to the lower boundary condition, the mass transfer coefficient, the hydraulic conductivity of the micro‐ and macropore, the macropore porosity, and the water content at wilting point. The data with the largest worth and ability to reduce the pre‐calibration prediction uncertainty were concentration observations closest to the sought prediction depth, drain concentrations, and averaged water table measurements from the entire field. The post‐calibration prediction uncertainty was increased primarily by removing concentration observations closest to the prediction depths. While this study provided new insights into parameter importance and data worth further research is required to understand if these findings apply broadly to clay till settings (or any soil setting) with preferential flow.

A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0

Abstract. The increasing resolution of numerical weather prediction models makes inter-column three-dimensional (3D) radiative transport more and more important. However, 3D radiative-transfer solvers are still computationally expensive, largely preventing their use in operational weather forecasting. To address this issue, Jakub and Mayer (2015) developed the TenStream solver. It extends the well-established two-stream method to three dimensions by using 10 instead of 2 streams to describe the transport of radiative energy through Earth's atmosphere. Building upon this method, this paper presents the dynamic TenStream solver, which provides a further acceleration of the original TenStream model. Compared to traditional solvers, this speedup is achieved by utilizing two main concepts. First, radiation is not calculated from scratch every time the model is called. Instead, a time-stepping scheme is introduced to update the radiation field, based on the result from the previous radiation time step. Secondly, the model is based on incomplete solves, with just the first few steps of an iterative scheme towards convergence performed every time it is called. Essentially, the model thereby just uses the ingoing fluxes of a grid box to update its outgoing fluxes. Combined, these two approaches move radiative transfer much closer to the way advection is handled in the dynamical core of a numerical weather prediction (NWP) model, as both use previously calculated results to update their variables and thereby just require access to the neighboring values of an individual grid box, facilitating model parallelization. To demonstrate the feasibility of this new solver, we apply it to a precomputed shallow-cumulus-cloud time series and test its performance in terms of both speed and accuracy. In terms of speed, the dynamic TenStream solver is shown to be about 3 times slower than a traditional 1D δ-Eddington approximation but noticeably faster than currently available 3D solvers. To evaluate the accuracy of the dynamic TenStream solver, we compare its results as well as calculations carried out using a 1D δ-Eddington approximation and the original TenStream solver, to benchmark calculations performed with the 3D Monte Carlo solver MYSTIC. We demonstrate that at the grid box level, dynamic TenStream is able to calculate heating rates and net irradiances at domain boundaries that are very close to those obtained by the original TenStream solver, thus offering a much better representation of the MYSTIC benchmark than the 1D δ-Eddington results. By calling the dynamic TenStream solver less frequently than the δ-Eddington approximation, we furthermore show that our new solver produces significantly better results than a 1D δ-Eddington approximation carried out with a similar computational demand. At these lower calling frequencies, however, the incomplete solves in the dynamic TenStream solver also lead to a buildup of bias with time, which becomes larger the lower the calling frequency is.

Numerical generation and verification of a two-dimensional virtual asphalt mixture

This paper aims at finding a robust method to generate a two-dimensional virtual asphalt mixture and to verify the suitability of the generated virtual mixture. First, three methods namely stereological method, Walraven formula and phase separation method are adopted to convert the actual three-dimensional gradation curves into two-dimensional ones. Then, an approach called semi whole scale cross section (SWSCS) method is developed to verify the results obtained and to determine the most robust procedure to follow. Third, the numerical two-dimensional geometry shape data library (GSDL) of different aggregates and voids and a robust method to assemble representative volume elements (RVEs) are developed using Matlab. Finally, a gradation curve from the literature is considered to generate a virtual asphalt mixture specimen, which is then imported in the finite element (FE) software Abaqus to simulate the dynamic modulus test. The virtual test results are further verified using available literature and another homogenization numerical prediction method based on the Mori-Tanaka's approach. The results show that the phase separation method is effective to convert the three-dimensional gradation curve to a two-dimensional one. The simulation results of the virtual asphalt mixture developed in this study turn out to be in good agreement with both previously presented literature data and Mori-Tanaka's theory.

A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors

Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions.

Experimental and Numerical Prediction of Slamming Impact Loads Considering Fluid–Structure Interactions

Slamming impacts on water are common occurrences, and the whipping induced by slamming can significantly increase the structural load. This paper carries out an experimental study of the water entry of rigid wedges with various deadrise angles. The drop height and deadrise angle are parametrically varied to investigate the effect of the entry velocity and wedge shape on the impact dynamics. A two-way coupled approach combing CFD method software STAR-CCM+12.02.011-R8 and the FEM method software Abaqus 6.14 is presented to analyze the effect of structural flexibility on the slamming phenomenon for a wedge and a ship model. The numerical method is validated through the comparison between the numerical simulation and experimental data. The slamming pressure, free surface elevation, and dynamic structural response, including stress and strain, in particular, are presented and discussed. The results show that the smaller the inclined angle at the bottom of the wedge-shaped body, the faster the entry speed into the water, resulting in greater impact pressure and greater structural deformation. Meanwhile, studies have shown that the bottom of the bow is an area of concern for wave impact problems, providing a basis for the assessment of ship safety design.

Identifying causes of errors between two wave-related data using performance metrics

Recently, numerous prediction methods of time-series data of wave-related phenomena have been developed. Quantitative evaluation of an error of a predicted result against a reference result is important to improve prediction accuracy. Many performance metrics are engaged to evaluate their accuracy. However, it is difficult for them to identify the error causes because all errors, no matter what the cause, are combined together. This paper presents new representations of performance metrics to separate errors by causes. Performances are evaluated in a frequency domain instead of a time domain. A frequency domain’s amplitude and phase are respectively evaluated using performance metrics. In addition, to detect errors due to instantaneous phenomena and changes in an error trend over time, mean errors are defined by three-time intervals. The original metric uses all time-series data. On the other hand, a finite interval mean error at the present time is calculated by the mean of the preceding data. In addition, cumulative mean error at the present time is calculated by the mean of the data up to the present time. These new representations of performance metrics could help to identify error causes. Benchmark tests are carried out to demonstrate the validity of the proposed representations.

Numerical prediction for life of damaged concrete under the action of fatigue loads

High-speed railways have become a critical component of modern transportation, and ensuring the stability and durability of high-speed railway concrete under environmental and train fatigue loads is essential for ensuring project safety. This paper employs a numerical simulation method to predict the performance and lifespan of high-speed railroad concrete under fatigue loading with environmental damage. The study analyzes the effects of stress level (0.5–0.8), stress ratio (0.1–0.9), and load frequency (5 Hz–40 Hz) on the fatigue performance and lifespan of railway concrete during bending fatigue loading. A four-point bending numerical model of concrete is developed in this paper, which considers elastic modulus decay and surface cracking, in order to quantify the degradation of concrete properties in response to environmental and load-induced cracks. The numerical model is well-validated with experimental results, and the study establishes a correlation between concrete degradation and environmental and load-induced cracks, as well as the fatigue resistance of high-speed railroad concrete. The bending fatigue frequency of concrete should not exceed 25 Hz at high stress levels of 0.7. This study also shows that environment-induced initial damage will greatly reduce the fatigue resistance of concrete, and fatigue life degradation and elastic modulus decay have a nearly linear relationship. This research provides a crucial foundation for the fatigue design of concrete materials for high-speed railroads.

Life cycle dynamics of Greenland blocking from a potential vorticity perspective

Abstract. Blocking over Greenland stands out in comparison to blocking in other regions, as it favors accelerated Greenland Ice Sheet melting and has substantial impacts on surface weather in adjacent regions, particularly in Europe and North America. Climate models notoriously underestimate the frequency of blocking over Greenland in historical periods, but the reasons for this are not entirely clear, as we are still lacking a full dynamical understanding of Greenland blocking from formation through maintenance to decay. This study investigates the dynamics of blocking life cycles over Greenland based on ERA5 reanalysis data from 1979–2021. A year-round weather regime definition allows us to identify Greenland blocking as consistent life cycles with an objective onset, maximum, and decay stage. By applying a new quasi-Lagrangian potential vorticity (PV) perspective, following the negative, upper-tropospheric PV anomalies (PVAs−) associated with the block, we examine and quantify the contribution from different physical processes, including dry and moist dynamics, to the evolution of the PVA− amplitude. We find that PVAs− linked to blocking do not form locally over Greenland but propagate into the region along two distinct pathways (termed “upstream” and “retrogression”) during the days before the onset. The development of PVAs− differs more between the pathways than between seasons. Moist processes play a key role in the amplification of PVAs− before the onset and are linked to midlatitude warm conveyor belts. Interestingly, we find moist processes supporting the westward propagation of retrograding PVAs− from Europe, too, previously thought to be a process dominated by dry-barotropic Rossby wave propagation. After onset, moist processes remain the main contribution to PVA− amplification and maintenance. However, moist processes weaken markedly after the maximum stage, and dry processes, i.e., barotropic, nonlinear wave dynamics, dominate the decay of the PVAs− accompanied by a general decrease in blocking area. Our results corroborate the importance of moist processes in the formation and maintenance of Greenland blocking and suggest that a correct representation of moist processes might help reduce forecast errors linked to blocking in numerical weather prediction models and blocking biases in climate models.

Performance improvement of photovoltaic-thermal collector equipped with copper metal foam air channel

ABSTRACT Avoiding high temperatures in photovoltaic cells with appropriate cooling is necessary to increase their conversion efficiency. Passive cooling systems using metal foam in the air channel were investigated, but an overall assessment is necessary for its viable application. In this numerical study, the thermal, electrical, and exergy efficiencies, as well as the entropy generation of a PVT are analyzed in the presence of copper metal foam in the air channel. The investigation is performed using a CFD code based on the pressure-based finite volume method. The non-Darcy effects of the porous foam are taken into account. Validation of the numerical model is made by comparison of the numerical predictions with experimental results of the literature. The outcomes indicate that varying the porosity from 1 to 0.89 increases the thermal energy, electrical energy, and exergy efficiency from 44.50% to 67.01%, 10.64% to 11.54%, and 12.01% to 13.19%, respectively, while the entropy generation decreases from 1.3116 W/K to 1.2942 W/K. Furthermore, the optimum thickness of the cooling channel is determined (30 cm for 0.93 copper foam porosity).

An Ice Protection System Based on Phased Piezoelectric Transducers

This study focuses on a system constituted of two piezoelectric transducers installed on a slat representative element, with ice protection purposes. The waves generated by these actuators can cause, in fact, shear actions between the slat panel and the ice accretion, with the final effect of breaking and detaching it. A property of the system is, however, the possibility of regulating the phase between the excitation signals of the two transducers. This capability can be exploited to produce local advantageous wave interference with a consequent amplification of the shear actions. Benefits can be obtained in terms of: (1) reduction of needed power; (2) recovery of signal intensity losses due to distance, geometric, and mechanic discontinuities; (3) recovery of non-optimal functionality due to off-design conditions. The work starts with an overview of the impact of the ice on the aeronautic and other sectors. Then, attention is paid to the systems currently used to protect aircraft, with a specific focus on ultrasounds generated by piezoelectric transducers. The concept proposed in this work is then presented, illustrating the main components and the working modality. On this basis and considering the specific nature of the physical phenomenon, the modeling approach was defined and implemented. At first, the impact of some critical parameters, such as the temperature and the thickness of the ice, was investigated. Then, the impact of the phase delay parameter was considered, estimating the increase of magnitude potentially reachable by means of optimal tuning. Finally, a preliminary experimental campaign was organized and a comparison with the numerical predictions was performed.

Development of an Emulator for Radiation Physics Simulator Using Stochastic Variational Gaussian Process Model

This paper describes an emulator that uses a Stochastic Variational Gaussian process (SVGP) regression model to parameterize radiation in a numerical weather prediction (NWP) model that meteorologically models the Earth's weather system. The computation of radiative processes is very large, accounting for most of the total NWP model computation. Statistical emulators are surrogate models that represent simulators and can overcome the computational limitations of very complex simulators such as radiative processes. Recently, artificial neural network-based radiative transfer emulators have been developed, and in this study, a statistical model, GP, is used to develop a radiative transfer emulator. The GP model has the advantage of calculating the uncertainty of the prediction along with the prediction, so the uncertainty of the prediction can be utilized appropriately. However, the computational complexity of the conventional GP model is very high, making it difficult to apply to large data. To solve this problem, an approximate approach, the SVGP model, was utilized. To further reduce the dimensionality of the input variables, we used a combined neural network and SVGP model. As a result, the SVGP-based radiative physics emulator improved its accuracy by about 20% compared to the artificial neural network emulator. However, the computation speed was about 3 to 9 times slower than the neural network emulator, but it was faster than the computation speed of the NWP model. This suggests that statistical emulators can be used to replace NWP model simulators.

Numerical simulation of a thermal energy storage system using sunrise and sunset transient temperature models

The temperature of the sun was modeled in this study using two transient solar temperature equations for sunrise and sunset that were developed for designing a latent heat thermal energy storage (LHTES) system for a concentrated solar power (CSP) plant. The derivation of the equations was based on the existing solar hour angle and the fundamental periodic function equations. Ansys' computational fluid dynamics code was used to investigate numerically the transient response of the conjugate melting and solidification of a phase change material (PCM) in a cylindrical shell and helical heat pipe (HHP) thermal system. The models for both equations were applied as user-defined functions (UDFs). The heat transfer medium was air. The predicted liquid and solid fractions provide quantitative data on the temperature and the stored solar energy. The effectiveness of both models in enhancing heat transfer and their suitability as real-time transient solar temperature models in heat transfer engineering is demonstrated by comparisons between their outputs. With high agreement, experimental data from the open literature was used to validate the numerical model's predictions. The solar temperature models aim to contribute to heat transfer enhancement for a reduced PCM energy storage time in designing a high-temperature solar thermal storage that is adequate to maintain a steady supply of electricity and energy for domestic and commercial applications and to accelerate the global transition to low-carbon energy.