Numerical Prediction Research Articles

Maritime free space optical communications field test and link budget statistics

During sub-optimal weather, a free-space optical (FSO) link range degrades depending on attenuation (atmospheric extinction) and turbulence effects. The ability to predict the system level performance can be exceedingly challenging as the atmospheric variability in a maritime link can be large and difficult to model. Link budget estimation for FSO systems often takes a nominal view of atmospheric conditions; here, we use statistical atmospheric predictions specific to a geographic area of interest to enable performance trades to be evaluated through link budget analysis. We compare these models to field-collected data to show the utility of the statistical atmospheric analysis in predicting FSO link performance for specific parts of the world. We have performed shore-to-ship FSO communications field tests at 10 Gb/s with links reaching out to a horizon limit over 40 km away in times of moderate extinction to clear weather. We provide further analysis by describing the expected performance of the link using statistical probabilities via cumulative distribution functions of both extinction and turbulence. The atmospheric variability can be determined for nearly any region of interest through the implementation of numerical weather prediction data to calculate the atmospheric performance drivers. These conditions are specifically evaluated for the 2017 Trident Warrior field test off the coast of San Diego, California, USA.

Damage assessment of High-Stress brittle surrounding rock masses in a deep Large-Span High-Sidewall underground Cavern: In situ measurement and numerical simulation

Excavation within high-stress brittle rock masses often encounters severe problem of structural damage in deep underground engineering, necessitating a significant scientific demand for accurately assessing the damage to surrounding rock masses through in-situ measurement and numerical simulations. This study reports detailed findings from an exhaustive investigation on the damage characteristics and underlying mechanisms in the high-stress brittle surrounding rock masses during excavation at the Shuangjiangkou (SJK) underground caverns. These findings were obtained through a comprehensive long-term in situ measurement using multi-point extensometer monitoring, borehole digital optical televiewer observing, and acoustic testing. Based on a deep understanding of the damage and failure mechanisms peculiar to the SJK high-stress brittle surrounding rock masses, a new damage index specifically designed for high-stress brittle rock masses was developed. This index is formulated by incorporating the failure approach concept into a modified Hoek−Brown criterion adapted for high-stress brittle rock masses. Comparison between the numerical predictions and in situ measuring results of the excavation damage zones during layered excavation of the SJK underground powerhouse revealed remarkable concordance. This consistency validates that the newly proposed damage index has good applicability and reliability for quantifying excavation-induced damage in high-stress brittle surrounding rock masses. These research findings are poised to provide valuable insights into the damage identification of brittle surrounding rock masses subjected to high-stress excavation, contributing to optimize excavation design and enhance the safety and sustainability of underground engineering projects.

ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package

Abstract. Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across weather forecasting, disaster preparedness, and urban planning, but researchers lack the tools to run these models effectively and efficiently, limiting progress. One of the current challenges is to assimilate observations in highly non-linear dynamical systems, as the practical need is often to detect abrupt changes. We have developed a software platform to improve the use of real-time data in non-linear system representations where non-Gaussianity limits the applicability of data assimilation algorithms such as the ensemble Kalman filter and variational methods. Particle-filter-based data assimilation algorithms have been implemented within a user-friendly open-source software platform in Julia – ParticleDA.jl. To ensure the applicability of the developed platform in realistic scenarios, emphasis has been placed on numerical efficiency and scalability on high-performance computing systems. Furthermore, the platform has been developed to be forward-model agnostic, ensuring that it is applicable to a wide range of modelling settings, for instance unstructured and non-uniform meshes in the spatial domain or even state spaces that are not spatially organized. Applications to tsunami and numerical weather prediction demonstrate the computational benefits and ease of using the high-level Julia interface with the package to perform filtering in a variety of complex models.

The Beneficial Effect of a TPMS-Based Fillet Shape on the Mechanical Strength of Metal Cubic Lattice Structures.

The goal of this paper is to improve the mechanical strength-to-weight ratios of metal cubic lattice structures using unit cells with fillet shapes inspired by triply periodic minimal surfaces (TPMS). The lattice structures here presented were fabricated from AA6082 aluminum alloy using lost-PLA processing. Static and dynamic flat and wedge compression tests were conducted on samples with varying fillet shapes and fill factors. Finite element method simulations followed the static tests to compare numerical predictions with experimental outcomes, revealing a good agreement. The TPSM-type fillet shape induces a triaxial stress state that significantly improves the mechanical strength-to-weight ratio compared to fillet radius-free lattices, which was also confirmed by analytical considerations. Dynamic tests exhibited high resistance to flat impacts, while wedge impacts, involving a high concentrated-load, brought out an increased sensitivity to strain rates with a short plastic deformation followed by abrupt fragmentation, indicating a shift towards brittle behavior.

Physical and 3D numerical modelling of reinforcements pullout test

This paper reports results of laboratory and 3D numerical modeled pull-out tests with steel ladders and polymeric strip reinforcements. These types of reinforcement are commonly used in reinforced soil walls constructed with concrete facing elements. Laboratory pull-out tests are required to determine accurate and realistic pull-out strength values considering the interaction of specific reinforcement and backfill materials under different confining pressures (i.e., trying to simulate the different reinforcement layer arrangements and load conditions in actual reinforced soil walls). International design Codes for reinforced soil walls provide default values for pull-out strength. However, in many cases, default values are too conservative and/or are not strictly specified for particular reinforcement types. Pull-out tests can be difficult and expensive to perform, thus not being common nor worth for the vast majority of reinforced soil wall projects. Consequently, calibrated numerical models can be useful to predict pull-out response under site-specific conditions, and provide further understanding of the mechanisms involved in the soil-reinforcement interaction. Details of the numerical approach, including relevant aspects of the soil-reinforcement interfaces, are described. Examples of calibrated numerical predictions for pull-out loads, displacements, and soil-dilatancy effects are presented. The influence of reinforcement, soil and interface stiffnesses is shown. Numerical results provide useful insight for future modelling works of the complex interaction between type-specific backfill materials and reinforcement element, relevant for investigation and/or practical design of reinforced soil walls.

The Use of Spatially Multi-Component Plasma Structures and Combined Energy Deposition for High-Speed Flow Control: A Selective Review

This review examines studies aimed at the organization of energy (non-mechanical) control of high-speed flow/flight using spatially multi-component plasma structures and combined energy deposition. The review covers selected works on the experimental acquisition and numerical modeling of multi-component plasma structures and the use of sets of actuators based on plasma of such a spatial type for the purposes of control of shock wave/bow shock wave–energy source interaction, as well as control of shock wave–boundary layer interaction. A series of works on repetitive multiple laser pulse plasma structures is also analyzed from the point of view of examining shock wave/bow shock wave–boundary layer interaction. Self-sustained theoretical models for laser dual-pulse, multi-mode laser pulses, and self-sustained glow discharge are also considered. Separate sections are devoted to high-speed flow control using combined physical phenomena and numerical prediction of flow control possibilities using thermal longitudinally layered plasma structures. The wide possibilities for organization and applying spatially multi-component structured plasma for the purposes of high-speed flow control are demonstrated.

Numerical prediction and experimental validation of forming limit curves of laminated half-hard aluminum sheets

Numerical prediction and experimental validation of forming limit curves of laminated half-hard aluminum sheets

The essentials of the finite element method to solve differential equations: an illustrative case in physics

Here we review the basic steps of the modern and accurate finite element method in its application to solve ordinary differential equations in physics. To exemplify this well-in-vogue numerical technique, we have chosen the second order Poisson-Boltzmann equation, which is a classic equation of colloid science. Aiming to formulate a viable, but didactic, implementation of the finite element technique, we have combined a linear basis of functions, the Galerkin weighted residuals method, the Swartz-Wendroff approximation and the Picard iteration algorithm. In summary, the finite element method transforms a differential equation into a simpler system of algebraic equations for the coefficients of the approximate solution in terms of a set of basis functions. We describe the full computational realisation of the finite element procedure and, also, we examine the corresponding Poisson-Boltzmann numerical predictions for various representative conditions.

Gas-particle flow induced erosion of economizer in low-low temperature electrostatic precipitators: Numerical prediction and engineering application

In this work, the gas-particle flow and the induced erosion in the economizer region in low-low temperature electrostatic precipitator were investigated by CFD-DPM-EPM coupling approach. The results show that positioning the economizer on the vertical flue or employing a vertical tubes arrangement for the heat exchange tubes, utilizing a small expansion angle (less than 35°) in the expansion section, ensuring a more uniform distribution of inlet gas velocity and dust concentration, and incorporating short and densely positioned guide plates in the expansion section can effectively reduce erosion in the low-temperature economizer. The above results were applied to the economizer equipped with the low-low temperature electrostatic precipitator of a 1000 MW unit in a certain power plant, and the wear situation was significantly improved. The research findings can serve as a reference for diagnosing and analyzing erosion phenomena in low-temperature economizer, and can guide the improvement design to prevent erosion.

A comparative study of latent heat thermal energy storage (LTES) system using cylindrical and elliptical tubes in a staggered tube arrangement

Numerical analysis was performed to compare the thermal and hydraulic performance of the elliptical and circular tube geometries in the prototype-scale latent heat thermal energy storage (LTES) system. A staggered tube array configuration was used for the analysis where the tubes were placed horizontally. Commercial grade hexahydrate calcium chloride (CC6) was selected as a phase change material (PCM) due to its phase change occurring at room temperature. The study included a numerical analysis of the melting and solidification processes of the PCM within the tube array for both tube shapes, employing transient two-dimensional Navier-Stokes equations and a Realizable k-ɛ turbulence model to predict fluid flow and heat transfer. The enthalpy-porosity technique was used to model PCM melting and solidification. Numerical predictions indicate that the thermal performance for both tube shapes is almost indistinguishable during the melting and freezing processes. However, due to the aerodynamic shape of the elliptical tube, the pressure drop across the horizontal elliptical tube array is approximately 80% lower compared to that of the circular geometry. This reduced pressure drop implies that less pumping power will be required to achieve the desired flow rate across the tube bank, leading to economic advantages for the horizontal elliptical tube array. The proposed design can be used as a dry cooling technique employing an Air Cooled Condenser (ACC) in a thermal power plant.

Implementation of AI for forecasting of wind speed and solar irradiation using different Machine Learning techniques

High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and wind systems, they depend on the weather, as compared to the conventional generation. As we know, the balance in managing load and generated power in energy system is very import ant. If the power which is supplied from solar and wind perfectly predictable, the extra cost of operating power system with a large penetration of renewable energy will be reduced. Since, the accurate and reliable forecasting system for renewable sources represents an important topic as a major contribution for increasing non-programmable renewable on over the world. The target of this research is to describe the advanced hybrid evolutionary techniques of computational intelligence applied for PV as well as wind power forecast. The evaluation of this investigation is obtained by comparing different definitions of the forecasting error. Moreover, the meaning of NWP (numerical weather prediction) values based on meteorological information on solar and wind power forecasting at Indore has been highlighted in this research.

Numerical prediction of fog: A novel parameterization for droplet formation

AbstractA novel aerosol activation scheme was developed and implemented into the WRF Thompson–Eidhammer Aerosol‐Aware microphysical module. While in most of the numerical weather prediction (NWP) models the activation is based on updraft velocity, even in the fog, the new parameterization considers the local rate of cooling and water vapor flux in the evaluation of the aerosol activation rate. In addition, diagnostic variables are added to evaluate the visibility reduction due to formation of haze droplets. The impact of changing the activation scheme is demonstrated by a case study of a well‐observed fog event. Data observed during the fog event campaign at Budapest and observed at standard meteorological stations are compared with the output of the numerical model. The results are summarized as follows: (i) the inhomogeneous spatial and temporal distribution of the number concentration of droplets is an inherent characteristic of the new parameterization scheme. (ii) Compared to the prior parameterization based on updraft velocity, the new parameterization scheme increases the number concentration of the droplets significantly, especially at the top of the fog. As a consequence, it reduces the downward short‐wave radiation flux prolonging the lifetime of the fog by about 30–60 min. (iii) Analyses reveal that earlier dissipation of the fog comparing to the observed data cannot be explained only by overestimation of the downward short‐wave radiation flux. (iv) The new method is able to evaluate the reduction of visibility due to haze.

ECMWF and UK Met Office Offshore Blowing Winds: Impact of Horizontal Resolution and Coastal Orography

AbstractWe analyze the performance of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and UK Met Office (UKMO) meteorological models in predicting offshore blowing wind in coastal areas. Our attention is mainly on the Mediterranean coast, up to 200 km distance from the shore. We compare forecast neutral winds with Advanced Scatterometer measurements. The results indicate that the ECMWF forecasts systematically underestimate wind speed with respect to scatterometer data, while the UKMO model tends to overestimate. A cross‐analysis suggests that, better than fetch, model biases are a function of the model horizontal discretization, hence of the number of grid steps the wind runs over the sea. The steepness and roughness of the land orography before entering the sea, together with the related drag parameters, appear to have a strong role in determining the coastal offshore wind values. Surface drag over land tends to reduce the related wind speed, and it takes a few grid points to adjust to the smooth sea surface conditions. This is supported by a detailed study with a high resolution grid, but different orography resolutions. In these simulations, practically identical coastal wind speed distributions are found when the subgrid orography schemes are switched off. Vertical cross‐sections of potential temperature and wind in bora and mistral conditions illustrate the strong role of gravity waves, wind channeling and turbulent diffusion in coastal numerical weather prediction. Besides local wave modeling, this may be relevant for offshore wind farms, typically situated within 5–50 km from the coast.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

Numerical Evaluation of Vibration Induced by Explosive Blasting in the Bauxite Mines of Sangarédi, Guinea

During rocks blasting by explosives in mines and quarries, Vibration phenomena appear in the surrounding environment, often causing significant damage to neighboring structures such as: buildings, bridges, tunnels and dams. This is why mining companies using the technique of the explosion are often faced with constraints of limiting the vibration level in order to minimize or eliminate potential damage to neighboring structures or reduce neighbor complaints.
 This paper presents a new numerical prediction model for vibration level induced by explosive blasting in the Bauxite mines of Sangarédi, Guinea. The dynamic damage law is associated with vibration phenomena analyses to determine numerical value of the particle velocities of different points located at different distances from the explosion hole for the explosion of a single charge hole, in order to compare to the vibration level required by the specifications of the Guinea mining company, which authorizes a vibration level of 10 mm/s in an inhabited area. The prediction of the vibration level gives particle velocities of 11m/s at a distance of 10 m from the explosion hole. Based on the results of the model, one could estimate that this required vibration level would be reached at a distance of 80 m from the blast hole.

Multi-injection downdraft moving bed reactor for hydrolysis in thermochemical hydrogen production

This paper investigates a downdraft multi-injection moving bed reactor (MBR) for the hydrolysis reaction in the copper-chlorine (Cu–Cl) thermochemical cycle for hydrogen production. The numerical modelling is conducted for a variety of system configurations of the MBR, with a focus on reducing the energy requirement of the reactor while achieving high conversion rates. Several design variables are investigated, including reactor volume, injection point location, and quantity of steam injection. The numerical predictions show an approximate 23% increase in CuCl2 conversion for the multi-injection MBR compared to a single injection design. A maximum conversion of 66.5% is predicted through design parameter variations. Asymptotic behavior for the quantity of injection locations with respect to conversion extent is presented for several different steam to copper ratios. The results and new insights in this paper suggest opportunities for conversion improvement and steam to copper ratio reduction in the hydrolysis reactor through the MBR design.

Comparative Study of Numerical Schemes for Granular Combustion of Boron Potassium Nitrate

Multiple applications in aerospace utilize pyrotechnic charges for their operation, and these charges are predominantly in the form of granules. One of the most used charges is boron potassium nitrate (BPN), and the present study focuses on mathematically modeling granular combustion, its experimental recreation, and carrying out a comparative study on three upwind schemes for its numerical simulation. A customized version of the seven-equation compressible multifluid formulation is presented in this paper to model granular combustion mathematically. Three upwind schemes, namely HLLC, AUSM+-up, and HLLC-AUSM, are used for the numerical comparison. Utilizing these, an axisymmetric code is developed for the comparative study. To experimentally replicate granular combustion, granular BPN is fired in a closed bomb test facility, and the experimental pressure history is used for the numerical comparisons. The developed code can adequately predict the physics of granular combustion, and all three schemes are equally capable of numerical prediction.

Statistical post-processing of numerical weather prediction data using distribution-based scaling for wind energy

The performance of numerical weather prediction models has improved dramatically recently. However, model biases remain a fundamental limitation prohibiting the direct implementation of model results. There are several ways to describe wind speed data. The Weibull and lognormal distributions are used to obtain the best-fit model for the wind speed data. This study aims to develop a statistical post-processing method based on the distribution-based scaling (DBS) approach, which scales NWP data to fit the distribution derived using recorded wind speed at that site location. The performance of the suggested method was evaluated using four different error measures. The optimal model is anticipated to have the lowest Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean square Error (RMSE), and variance (s2) values. It was determined that employing a DBS strategy significantly improved the NWP by at least 75%.

The Impact of High-Density Airborne Observations and Atmospheric Motion Vector Observation Assimilation on the Prediction of Rapid Intensification of Hurricane Matthew (2016)

Tropical cyclone rapid intensification (RI) prediction still remains a big international challenge in numerical weather prediction. Hurricane Matthew (2016) underwent extreme and non-classic RI, intensifying from a Category 1 storm to a Category 5 hurricane within 24 h under a strong vertical shear environment. However, most models failed to capture this RI, and limited or no inner core, and outflow observations were assimilated in the NWS operational HWRF Model before the onset of RI for Matthew (2016). The goals of the study are to (1) explore the best way to assimilate the High-Density Observations (HDOB, including FL and SFMR) and AMV data; (2) study the impact of assimilating these observations on the analysis of both the inner-core and outflow structures; and (3) examine the impact of assimilating these data on the prediction of RI for Matthew. The main results are as follows: (1) With proper pre-processing of the HDOB observations and by using a 4DEnVar method, the inner-core structure analysis was improved. And the RI prediction is more consistent with the best track without spin-down for the first 24 h. Assimilating CIMMS AMV observations on top of the HDOB observations further improves both the track and intensity forecasts. Specifically, both the magnitude and timing of the peak intensity are further improved. (2) Diagnostics are conducted to understand how the assimilation of these different types of observations impacts RI prediction. Without assimilating HODB and AMV data, baseline experimentover-predict the intensification rate during the first 18 h, but under-predict RI after 18 h. However, the assimilation of FL and SFMR and CIMMS AMV correctly weakens the upper-level outflow and improves the shear-relative structure of the inner-core vortex, such as reducing the low-level moisture in the downshear left quadrant. The deep convection on the downshear side is weaker than baseline for the first 18 h but keeps enhancing, later moving cyclonically to the USL quadrant, and then causes more subsidence warming, maximizing in the USL quadrant and the maximum wind increases faster. Moreover, the rapid intensification rate is much more consistent with the best track and the forecast skill of RI is improved. Therefore, 4DEnVar assimilation with proper pre-processing of the high-density observations can indeed correct the shear-relative moisture and structural distributions of both the inner core and environment for TCs imbedded in the stronger shear, which is important for shear-TC RI prediction.

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

AbstractHigh temporal resolution geostationary hyperspectral infrared sounders can simultaneously profile atmospheric temperature and moisture, and track moisture features to provide 3D horizontal winds in clear and partially cloudy scenarios. The thermodynamic information obtained has been integral to enhancing tropical cyclone (TC) forecasts. However, the potential benefits derived from the dynamic information provided by geostationary hyperspectral infrared sounders (GeoHIS) are yet to be fully comprehended for numerical weather prediction (NWP). With the re‐estimated observation error and multivariate hydrometeor background error covariance, the 3D horizontal winds from the Geostationary Interferometric Infrared Sounder (GIIRS) are synergistically assimilated with the hydrometeor information. The impact of the GIIRS‐derived 3D horizontal wind assimilation on Typhoon Maria (2018) and Lekima (2019) analysis and forecast are evaluated. The results show that the assimilating GIIRS‐derived 3D horizontal wind notably reduces the RMSEs of analysis and forecast, particularly in the U and V components. The improvement in accuracy of large‐scale fields substantially enhances the forecasts of typhoons' track and maximum wind speed, as well as the central sea‐level pressure. Moreover, the prediction of the spatial distribution and intensity for landfall precipitation is also improved. The detailed diagnoses of the Maria show that a more southerly subtropical high generated by the additional horizontal wind assimilation improves the rainfall spatial distribution, and the reasonable water vapor transportation caused by the improved dynamic conditions corrects the precipitation intensity. This work highlights the importance of dynamic information in TC forecasting and emphasizes the need for efficient and high‐precision 3D wind measurements to improve NWP.