Numerical Prediction Research Articles

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.

Role of midtropospheric heating and surface forcing on monsoon intraseasonal transitions

AbstractMonsoon intraseasonal transitions are intrinsically chaotic; hence, their predictability is limited. In the past the majority of studies were conducted solely keeping in account of dynamic prospective like boreal summer intraseasonal oscillation or Madden–Julian oscillation; however its thermodynamic aspect got limited attention. The current study looks at the internal thermodynamic mechanisms pertaining to monsoon intraseasonal transition using two independent sets of reanalysis data (European Centre for Medium‐range Weather Forecasts Reanalysis version 5 and Indian Monsoon Data Assimilation and Analysis), along with the observed cloud type from Met Office Integrated Data Archive System Land and Marine surface station data. A lead–lag composite analysis shows that the midtropospheric warming by diabatic heating is a thermal control to restrict the deepening of convective cloud (cumulonimbus) as the wet spell progresses. This midtroposhperic heating cuts off the formation of deep convective clouds and results in the formation of relatively shallow clouds, such as stratocumulus and stratus. On the other hand, surface forcing during dry spells humidifies the troposphere by detrainment from the shallow convection with the formation of cumulus and cumulus congestus clouds. This facilitates the preconditioning of the atmosphere for deep convection and results in the onset of wet spells by the formation of cumulonimbus clouds. The comprehensive approach used in this work may further help in identifying the limitation of the predictability in monsoon transition period from the numerical weather prediction models.

Improved Orographic Gravity Wave Drag Parameterization Accounting for the Nonhydrostatic Effect in the Weather Research and Forecasting Model: Tests for Short-Range Forecast of Northeast China Cold Vortices

Abstract The parameterization of orographic gravity wave drag (OGWD) is essential for accurate numerical weather prediction in regions of complex terrain. Current OGWD schemes assume hydrostatic OGWs but the parameterized OGWs in fine-resolution models with narrow subgrid-scale orography can be significantly affected by the nonhydrostatic effects (NHE). In our recent work, the OGWD scheme in the Model for Prediction Across Scales (MPAS) was revised by accounting for the NHE on the surface wave momentum flux of upward-propagating OGWs. Herein, the revised OGWD scheme is implemented in the Weather Research and Forecasting (WRF) model to evaluate its performance in short-range weather forecast. Two sets of 36-hr WRF simulations are conducted for nine Northeast China cold vortices (NECVs) that occurred in the warm season of 2011 using the original and revised OGWD schemes. Results show that the WRF model tends to underestimate the intensity of the NECVs, producing too high geopotential height. When accounting for the NHE in the OGWD scheme, the NECV intensity biases are significantly reduced. Analyses reveal that the NHE act to weaken the lower-tropospheric OGWD by decreasing the surface wave momentum flux, which strengthens the NECV in the lower troposphere. Consequently, the strengthened low-level cyclonic circulation increases the post-trough cold advection to the southwest of the NECV which in turn enhances the NECV in the mid-upper troposphere with reduced geopotential height. The NHE are found to increase as the model horizontal resolution increases, suggesting greater importance of the NHE in the OGWD parameterization of high-resolution numerical models.

Towards a 2DEnVar surface data assimilation approach within the convective scale numerical weather prediction model AROME‐France

AbstractSurface data assimilation (DA) plays a key role in the initialisation of soil variables in coupled surface–atmosphere numerical weather prediction (NWP) models. Météo‐France, as many other NWP centres, uses an operational surface DA method based on an optimal interpolation (OI), implemented more than 30 years ago. The current surface DA system implemented at Météo‐France in the operational limited‐area AROME‐France model operates in two steps. First, a two‐dimensional OI method analyses the diagnostic screen‐level parameters, which are then used to initialise the prognostic soil variables according to a one‐dimensional OI method. This article presents the implementation and the results of a new two‐dimensional ensemble‐based variational (2DEnVar) system, using the object‐oriented prediction system framework, for the diagnostic screen‐level variable analyses (2‐m temperature and 2‐m relative humidity) within the AROME‐France model. These analysed variables are then used to initialise the soil temperature and soil moisture of the first and second soil layers. A two‐dimensional variational system is initially implemented and compared with an equivalent system that uses an OI surface analysis. The comparison of 2‐m temperature increments shows that the two systems behave in a similar way. From this two‐dimensional variational approach, a 2DEnVar scheme is developed and compared with the OI screen‐level analysis used operationally (reference system). The results are encouraging even though a degradation in forecast quality is noticed at short ranges for 2‐m temperature and 2‐m relative humidity. This is mainly due to a low spread near the surface in the ensemble‐based data assimilation that is used to compute background‐error covariances in 2DEnVar. Sensitivity tests have been performed to improve the initial version of the 2DEnVar scheme. A positive and significant impact is noticed when a multiplicative inflation is applied to the background perturbations coming from the ensemble‐based data assimilation for the computation of 2‐m temperature and 2‐m relative humidity background‐error covariances. Finally, when comparing the inflated 2DEnVar system with the reference, the two systems behave similarly. This suggests that the 2DEnVar scheme, based on ensemble statistics, is capable of properly reconstructing background‐error structures derived from the empirical correlations described in OI.

An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

ABSTRACTAiming at the problem that the existing ultra‐short‐term wind power prediction methods lack consideration of the spatial correlation characteristics of wind farms, resulting in insufficient prediction accuracy, an ultra‐short‐term wind power prediction method based on spatiotemporal characteristics fusion is proposed in this article. First, the fluctuation difference of the time window of wind power is input into the K‐means clustering algorithm to cluster wind farms into several clusters based on power fluctuation similarity. Then, the principal component analysis algorithm is used to reduce the dimensionality of numerical weather prediction data combinations in different regions to reduce the impact of redundant information on modeling accuracy. Finally, a convolutional long‐short‐term memory neural network is designed to extract spatiotemporal features of wind power data and output prediction results. The experimental verification on 18 wind farms in a province in China shows that the proposed wind power prediction method has an average root mean square error of only 0.1257 and has certain applicability.

Numerical Weather Prediction of Sea Surface Temperature in South China Sea Using Attention-Based Context Fusion Network

Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study, we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time, the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination (R2) of 0.972. In addition, in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time, with a corresponding R2 of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability.

Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.