Numerical Prediction Research Articles

Evaluation of forecasted wind speed at turbine hub height and wind ramps by five NWP models with observations from 262 wind farms over China

AbstractAccurate wind speed forecasts are essential for optimizing the efficiency of wind energy operations. Currently, there is limited research on nationwide assessment of predictive performance in multiple numerical weather prediction (NWP) models for wind speed at turbine hub height over China, especially concerning wind ramp events. Utilizing observed measurements from 262 wind farms, this study evaluated the performance of five NWP models in forecasting the mean state and spatiotemporal variations of wind speed as well as wind ramps. The results indicated that the European Center for Medium‐Range Weather Forecast Integrated Forecasting System (ECMWF–IFS) performed the best in forecasting climatological wind speed with a temporal correlation coefficient (TCC) of 0.74 and root mean square error (RMSE) of 2.34 m s−1. Although not widely utilized in China, the model from Meteo‐France (MF–ARPEGE) showed promising potential for wind energy forecasting with a TCC of 0.72 and RMSE of 2.45 m s−1. In terms of temporal variations of wind speed, all the models could reasonably predict the seasonal variations of wind speed, whereas only three NWP models were able to capture the characteristics of the observed diurnal variation. An error decomposition analysis further revealed that the primary source of predicted error for wind speed was the sequence error component (SEQU), indicating the model errors were mainly attributed from the temporal inconsistency between forecasts and observations. Furthermore, the occurrences of wind ramps were generally underestimated by NWP models, while this shortcoming can be partly overcome by improving the time resolution of NWP models.

New Prediction Of Cracks Propagation In Repaired Steel Plate With Bonded Composite Patch At Cyclic Loading

Abstract This study presents a numerical prediction of the fatigue life of steel panels repaired by a composite patch. The effect of length cracks, the stress ratio R and properties of the patch is presented. The obtained results show that the bonded composite repair significantly reduces the stress intensity factors at the tip of repaired cracks. The results are in a good agreement with those in the literature. The Monte Carlo method is used to predict the distribution function governing crack propagation in fatigue analysis. In computing the failure probability of the structure, we consider the statistical uncertainty associated with key variables, along with the previously discussed model uncertainty. The results obtained highlight the considerable impact of variations in crack length and stress ratio on the distribution function. Notably, uncertainty in these parameters significantly amplifies the probability of structural failure in plates, thereby diminishing overall structural durability.

Weather Prediction with Feature Selection and Random Forest

Accurate weather prediction is crucial for various sectors, including agriculture, disaster management, and aviation. Traditional weather prediction relies heavily on numerical weather prediction (NWP) models that simulate atmospheric processes using mathematical equations. While these methods have been the backbone of forecasting for decades, they often require significant computational resources and may need help capturing localized weather events. In contrast, machine learning models can quickly analyze large datasets to identify patterns and relationships that traditional methods might miss. This paper employs a Random Forest model to investigate the impact of feature selection on weather prediction accuracy. This paper identifies those critical to model performance by systematically excluding individual features, offering valuable insights into how ML can enhance traditional forecasting techniques. The results underscore the importance of features such as “Present_Tmin” and “LDAPS_LH”, which significantly influence model performance as measured by Root Mean Square Error (RMSE). This study contributes to a better understanding of feature selection in developing accurate weather prediction models. It lays the groundwork for future research to improve prediction accuracy through advanced techniques and expanded datasets.

Adjoint‐based observation impact on meteorological forecast errors in the Arctic

AbstractAccurate weather forecasting using numerical weather prediction in the Arctic remains a challenge owing to the complex atmospheric dynamics and relatively sparse observations of the region. Nevertheless, the impact of observations used for data assimilation (DA) on forecast errors (observation impacts, OIs) in the Arctic has not been fully explored. This study evaluated the OIs on 24 hr forecast errors in the Arctic by the adjoint‐based forecast sensitivity OI (FSOI) method using the Polar Weather Research and Forecasting (WRF) model, three‐dimensional variational DA, and adjoint model of WRFPLUS. The time‐averaged FSOI during August 2018 was analyzed in terms of the type, variable, satellite channel, altitude, and horizontal location of the observations. The results showed that the total FSOI was greatest for aircraft observations (AIRCRAFT), followed by sonde observations, scatterometer sea‐surface winds, advanced microwave sounding unit‐A radiance data from each satellite, and surface synoptic observations. The impact of AIRCRAFT in reducing forecast errors was greater in the Arctic than in other regions of the globe. The total FSOI of the advanced microwave sounding unit‐A radiance data from all four satellites was the greatest among the total FSOI of the individual observation types. For both AIRCRAFT and sonde observations, the FSOI per observation increased at higher latitudes, indicating that forecast errors in the Arctic could be reduced when upper atmospheric in‐situ observations at higher latitudes are used for DA. The beneficial ratio of the Global Positioning System precipitable water in the Arctic was greater than that in the midlatitudes with relatively moist atmospheres that cause greater uncertainties in converting zenith wet delay. The results of this study can contribute to configuring an optimal observing system in the Arctic by presenting the relative importance of observations for reducing forecast errors.

Numerical Prediction of Inlet Geometry Influence on the In-Duct Acoustics of Small Centrifugal Compressors

Abstract Small centrifugal compressors are employed to boost automotive internal combustion engines or hydrogen fuel cells, amongst other applications. In many scopes, the overall intensity and quality of noise emission is a major concern, so many researchers develop and assess numerical models to predict acoustic spectra. In this work, detached Eddy simulations (DES) of a vaneless centrifugal compressor are conducted to assess the impact of intake geometries on its aeroacoustic performance. Experimental measurements are employed as a means of validation. Flow field analysis and Dynamic Mode Decomposition allow us to analyze the underlying mechanisms of the most relevant acoustic features. This work provides insight into the implications for noise generation of employing tight elbows due to packaging constraints.

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Thermo-hydraulic performance characteristics of novel G-Prime and FRD Triply Periodic Minimal Surface (TPMS) geometries

Efficient thermal management is crucial for numerous applications, from electronics cooling and automobile systems to aerospace systems, necessitating the exploration of advanced heat transfer technologies like TPMS structures. Compared to conventional heat exchangers and heat sinks, lattice structures have improved by around 20–30 % performance. However, the variation in performance of the various lattice structures, such as FRD, G-Prime, etc., is yet unknown. Thus, this study investigates the thermo-hydraulic performance of various Triply Periodic Minimal Surface (TPMS) structures, including novel G-Prime and FRD geometries, through numerical simulations and experimental validation. The TPMS geometries were modeled using LattGen software and voxelized with 20 % relative density. Computational Fluid Dynamics (CFD) simulations were performed using Ansys Fluent with the k-epsilon turbulence model, and experiments were conducted on 3D-printed samples for validation. The numerical results reveal that G-Prime-2 and FRD Prime exhibit the highest heat transfer performance while demonstrating higher pressure drops than other TPMS geometries. Experimental validation agrees with numerical predictions, confirming the superior thermo-hydraulic performance of G-Prime-2 and FRD Prime for heat transfer enhancement applications. The results contribute to understanding TPMS thermo-hydraulic performance and enable informed geometry selection for thermal management applications.

Ancient Channel-Mouth Bifurcation Angles on Earth and Mars

Channel mouth bifurcation angles on modern river deltas are remarkably consistent with a theoretical prediction of 72°. However, the persistence of this angle through channel evolution and preservation into the stratigraphic record remains untested. Ancient channel mouth bifurcations were measured using stratal slices from 3D seismic volumes as well as outcropping delta deposits in Mars orbital imagery. We find that channel mouth bifurcations interpreted from terrestrial strata exhibit a mean angle of 71.9° ± 3.8° (95% confidence interval), consistent with modern deltas as well as theoretical and numerical predictions. Angles from martian strata preserved as inverted topography exhibit a mean angle of 80.1° ± 4.8°. A larger angle on Mars may be biased by measurements on eroding outcrops, or possibly the signature of altered sediment transport processes on Mars. Expanding channel network analysis into the stratigraphic archive for the first time provides a new mechanism interpreting paleohydraulics on Earth and Mars.

High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations

Wind speed (and direction) estimated from numerical weather prediction (NWP) models is essential to wind energy applications, especially in the absence of reliable fine scale spatio-temporal wind information. This study evaluates four high-resolution wind speed numerical datasets (UERRA MESCAN-SURFEX, CERRA, COSMO-REA6, and NEWA) against in situ observations from coastal meteorological stations in the eastern Mediterranean basin. The evaluation is based on statistical comparisons of long-term wind speed data from 2009 to 2018 and involves an in-depth statistical comparison as well as a preliminary wind power density assessment at or near the meteorological station locations. The results show that while all datasets provide valuable insights into regional wind variability, there are notable differences in model performance. COSMO-REA6 and UERRA exhibit higher variability in wind speed but tend to underestimate extreme values, particularly in the southern coastal areas, whereas CERRA and NEWA provided closer fits to observed wind speeds, with CERRA showing the highest correlation at most stations. NEWA data, where available, overestimate average wind speeds but capture extreme values well. The comparison reveals that while all datasets provide valuable insights into the spatial and temporal variability of wind resources, their performance varies by location and season, emphasizing the need for the careful selection and potential calibration of these models for accurate wind energy assessments. The study provides essential groundwork for leveraging these datasets in planning and optimizing offshore wind energy projects, contributing to the region’s transition to renewable energy sources.

Numerical analyses of spherical indenter intrusion into sandstone: From laboratory tests to numerical simulations

To explore the mechanical mechanisms of rock fracturing under compression during ball milling. The study investigates the effects of intrusion depth and indenter diameter on the evolution of stresses and plastic strains, integrating laboratory tests and numerical predictions. The results reveal a reduction in both axial and radial stresses with increasing intrusion depth into the rock. Shear stress experiences an initial rise followed by a decline, while circumferential stress demonstrates an initial increase followed by a rapid decrease in the radial direction. As the intrusion deepens, the predominant influence on plastic strain of rock shifts from shear stress dominance to a combined dominance of shear and circumferential stresses. The influence of the indenter diameter on the stress field diminishes following an exponential decay pattern. In terms of the plastic strain field, smaller indenters are more likely to induce plastic failure due to shear and circumferential stresses, larger indenters are more prone to plastic failure induced solely by shear stresses. As the number of indenters increases, the integration of the stress and plastic strain fields is enhanced. This implies that introducing more spherical indenters amplifies the collaborative effect, leading to a more cohesive fracturing of the rock.

RUFCO: a deep-learning framework to post-process subseasonal precipitation accumulation forecasts

Abstract Post-processing is a critical step in attaining calibrated and reliable probabilistic forecast output from numerical weather prediction models. A novel deep-learning framework is proposed to post-process 20 years of 7- and 14-day precipitation accumulation reforecasts from the Global Ensemble Forecast System at subseasonal timescales (week 1, week 2, and combined weeks 3-4 forecasts) over the contiguous United States. The network builds upon previous studies and is a combination of three parallel-trained components suitable for subseasonal prediction. The first is a ResUnet architecture which learns non-linear relationships between binned observed precipitation and input images of weather and geographical variables. The second conditions the network to the month-of-year via a Feature-wise Linear Modulation (FiLM) layer. The third helps the network learn when to revert the forecast to that of climatology. The RUFCO (named for its components ResUnet, FiLM, and Climatological-Offramp) forecasts are compared against raw and climatological forecasts as well as those from a state-of-the-art distributional regression post-processing model, “CSGD,” and a simple bias-corrected model. At week 1, every method exhibited a competitive advantage over climatological forecasts. At week 2, RUFCO generated forecasts with statistically significant improvement over climatology at 82-94% of the domain, beating CSGD’s coverage of 76-90% of the grid points. At week 3, RUFCO’s skillful coverage was 65-85% while CSGD’s dropped to only 12-37%. At the longer lead times, RUFCO achieved the highest domain-averaged skill scores across seasons. However, the network tends to “smooth” forecast skill making it less competitive with CSGD in limited areas with strongly spatially-varying biases.

Flexural behaviour of BFRP grid/bar reinforced UHPC beams and slabs

This study investigates the integration of Basalt Fiber Reinforced Polymer (BFRP) and Ultra-High Performance Concrete (UHPC), which exhibit significant potential for a durable and sustainable solution in marine and offshore infrastructure. An experimental program of BFRP grid/bar-reinforced UHPC beams and slabs subjected to four-point bending is presented. A total of nine beams and ten slabs were prepared and tested to investigate the effect of BFRP reinforcement ratio (0 %–3.33 %), type of reinforcement (grid and bar), and steel fiber content of UHPC (0 %, 1 %, 2 % and 3 %). The results revealed that the inclusion of BFRP reinforcement, even at a small reinforcement ratio, is feasible to enhance structural ductility due to the large rupture strain and superior strength characteristics of BFRP material. Moreover, the steel fibers played a crucial role in preventing shear failures of UHPC and creep failures of BFRP reinforcement. Within a range of 0–2 %, the steel fiber content not only benefited the load-resistance and deformation capacity but also effectively improved the cracking load. However, high fiber content (3 %) had a minimal contribution to load-bearing capacity and negatively affected ductility. A theoretical model based on the deflection method was employed to forecast the general behaviour of the flexural members. Comparisons between experimental results and numerical predictions confirm its accuracy.

Simulation of the Neutral Atmospheric Flow Using Multiscale Modeling: Comparative Studies for SimpleFoam and Fluent Solver

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design.

Weather Prediction with Machine Learning

Weather forecasting is the technical process of predicting atmospheric conditions at a location. Many scientists have also been trying to predict the weather, both formally and informally, for years. Weather forecasting is done by collecting data from specific locations with different climate attributes. Traditional numerical weather prediction models have made significant progress, but there are still limitations in their accuracy, especially in detecting local weather events. In recent years, machine learning techniques have become a powerful tool for improving weather forecasts by exploiting the large amounts of data and complex patterns inherent in atmospheric systems. We propose a machine- learning model that uses historical data to train the model. The model is then used to predict weather with better accuracy than traditional models.

Leak-Rate Through Carbon Brush Seals: Experimental Tests Versus Predictions From a Porous Medium Approach

Abstract This study presents a detailed comparative analysis between experimental leakage flow rates and numerical predictions for carbon brush seals with long bristles, utilizing a porous medium model approach. A series of tests were carried out on a static rig (without rotor rotation). The experimental setup allows tests under various interference conditions, revealing significant insights into the flow behavior through the brush seal. A numerical model based on the Darcy-Forchheimer equation is developed to interpret the complex flow dynamics within the brush seal, accounting for viscous, compressible and inertial effects. The study evaluates the impact of brush deformation and porosity on flow resistance, leveraging experimental data to refine the numerical model parameters. This investigation not only deepens the understanding of brush seal flow physics but also improves the predictive accuracy of the numerical model in simulating operational conditions.

Investigation on load - transfer characteristics of spherical hinge structure of swivel bridge based on scale distortion model

The mechanical properties of the swivel spherical hinge, a critical component in swivel bridge structures, remain inadequately understood. This study precisely examines the load-transfer characteristics of the swivel spherical hinge using a combination of finite element simulation and scaled model loading tests. An optimized hinge model, accounting for distortion effects and strategic placement of measurement points, was constructed and tested in the laboratory. Results indicate that the scaled model exhibited consistent elastic deformation, ensuring structural integrity and stability. The results indicate that under a 122 kN load, the average discrepancy between the experimental data and numerical predictions was only 1.53 %, highlighting the numerical model's accuracy and reliability. Additionally, the center of the slider experienced a compressive stress of approximately 2.7 MPa, while the edge endured 5.3 MPa, reflecting a non-uniform load distribution. Stress concentrations at the slider's projection were 2–5 times greater than at other measurement points on the steel-concrete interface of the hinges. The lower spherical hinge surface displayed a complex stress pattern combining tensile and compressive stresses, resulting from slider compression and hinge surface expansion. This research provides valuable insights into the load-transfer mechanisms of swivel spherical hinges, offering guidance for the design and construction of swivel bridges.

How inaccurate offshore wind observations impact the quality of operational numerical weather prediction

AbstractAt Norwegian offshore platforms, wind speed is observed at heights between approx. 35 and approx. 150 m a.s.l. These observations are used to estimate 10‐m wind speed, by a power law with a constant wind shear coefficient. The resulting 10‐m wind speed is then distributed to the Global Telecommunication System (GTS) and used for assimilation and verification purposes by the meteorological community. However, multiple methods with varying results exist for the reduction of wind speed from the original measurement height to 10 m. The resulting estimate of 10‐m wind speed is therefore sensitive to the choice of method. The use of such observations may therefore be suboptimal in forecast analysis and make the forecast verification difficult to interpret. This study investigates how the use of these observations impacts forecasts from the operational regional forecast system used at MET Norway and the Global Integrated Forecast System operated by ECMWF for the period 2017–2023. Both forecast systems assimilated the observations over major parts of the period and the presented results indicate that the use of these observations increased the initial errors in the forecasts. This is confirmed in a data denial experiment over a shorter period of time. The verification results show that both forecast systems underestimate the wind speed at the original measurement heights, while the results for 10‐m verification vary with the interpolation method between original sensor height and 10 m. The forecasts show in general more wind than the GTS‐distributed estimates and are in better agreement with more advanced interpolation methods. The verification results show a strong dependency on the vertical stability of the atmosphere. The major differences between the forecast systems are that the regional system shows less bias, while the standard deviations of the errors are slightly lower in the global system.

Microscopic view on the polarization-resolved S-SHG intensity of the vapor/liquid interface of pure water.

Second harmonic generation (SHG) is a nonlinear optical phenomenon where two photons at the frequency ω combine to form a single photon at the second-harmonic frequency 2ω. Since that second-order process is very weak in bulk isotropic media, optical SHG responses of interfaces provide a powerful and versatile technique to probe the molecular structure and dynamics of liquid interfaces. Both local dipole contributions and non-local quadrupole contributions can be interesting to investigate different properties of the interface, such as the molecular orientation or the charge density. However, a major difficulty is to comprehend the link between the S-SHG intensity and molecular details. This article reports a numerical approach to model the polarization-resolved SHG intensities of a model vapor/liquid interface of pure water. The influence of the interfacial local environment on the hyperpolarizability is taken into account using quantum mechanical/molecular mechanics calculations. The numerical predictions are in very good agreement with experiments. We detail the hypotheses made during the modeling steps and discuss the impact of various factors on the modeled SHG intensities, including the description of the exciting field in the interfacial layer, the effect of neighboring molecules on the second-harmonic polarization, and the presence of an additional static electric field at the interface.

Numerical prediction on the fatigue debonding behaviour in a complex bi-material interface: A case study on wrapped composite joints

Debonding is one of the most critical failure modes for bonded joints under fatigue loads. Numerical prediction on the fatigue debonding behaviour of bonded interfaces with complex geometry still remains a problem. This paper proposes a numerical methodology based on fracture mechanics to predict crack growth in a complex bi-material interface and illustrates the prediction procedure by a case study on wrapped composite joints. Interface coupon tests provide the fatigue crack growth properties at the composite-to-steel interface used as inputs for finite element (FE) modelling. The FE model is calibrated against fatigue tests of small scale wrapped composite joints with different steel surface roughness subject to different load levels. A sensitivity analysis is conducted to investigate the influence of key modelling parameters. The calibrated model is validated against fatigue tests on upscaled joints. Good agreements are shown between the test and modelling results in terms of crack growth and stiffness degradation, demonstrating the potential of the proposed numerical methodology for predicting fatigue debonding behaviour of complex bi-material interfaces.

Numerical springback prediction for high-strength aluminum alloys based on the sheet metal upsetting test

Springback prediction continues to play a special role in the process design of sheet metal forming processes. During the forming process, elastic energy is stored in the sheet metal, which is released after the tool is opened. The dimensional accuracy and the function of the component in particular can be negatively affected by this. For this reason, the springback is predicted by means of numerical modeling in order to improve the process design. The simulation accuracy depends primarily on the used material model to map the stress-dependent material behavior and the quality of the experimental input data. High-strength aluminum alloys, which have a distinctive springback behavior, have a slightly pronounced tensile-compressive asymmetry. Since tensile and compressive stresses often occur in sheet metal forming processes, the consideration of this effect can lead to an improvement in springback prediction. Besides modeling the yield locus curve for describing the yield strength in the plane stress area, the yield curve data is also relevant for determining the strain hardening behavior. The work hardening is usually characterized by the tensile test or the hydraulic bulge test, which is used to describe the material behavior in the tensile stress state. A promising experimental method for the analysis of the material in the uniaxial compressive stress state is the sheet metal upsetting test. In addition to a local characterization of sheet metal materials, this test also enables the investigation of the material behavior at significantly higher plastic strains than in the tensile test. Thus, in this contribution it is examined to what extent the sheet metal upsetting test is suitable for improving the springback prediction quality of material models in sheet metal forming processes.