Empirical Formulation Research Articles

Updating neoclassical economics with contemporary conceptions of homo economicus: a bibliometric analysis

AbstractThe postulation of rationality pervading neoclassical economics, having found to be lacking in veracity, necessitates an update of Homo economicus in neoclassical economics. An empirical formulation of economics predicated on an evolutionary framework of cognitive development is widely akcnowledged to expand the application and utility of economic models and this study identifies the key research areas associated with the contemporisation of neoclassical economics and reviews the extant body of knowledge on the status of the endeavour. A co-occurring keyword procedure is used to perform a bibliometric analysis to evaluate the extent to which the body of knowledge has progressed with updating the conception of Homo economicus in neoclassical economics. Data obtained from the Scopus and Web of Science databases are subjected to treatment by the procedure which ultimately reflects the relevance and quantity of research output related to the objective of updating neoclassical economics. The results show that, apart from the area of cognitive biases, the research contribution from the identified focus areas are insufficient with 14 of the 18 fields contributing only 12% of the research ouput related to the updating of neoclassical economics. The key insight of this study is that while the failures of neoclassical economics have been well documented in the literature, the effort to improve economic predictions to better approach reality by updating the characterisation of Homo economicus with evidence based findings relating to human behaviour is still immature. It is recommended that the computational and analytical tools endowed by the digital revolution be employed to close the research gap enabling the creation of debiasing strategies and acucrate economic predictions.

Data-driven models for predicting compressive strength of 3D-printed fiber-reinforced concrete using interpretable machine learning algorithms

3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning techniques, including Gene Expression Programming (GEP), Multi-Expression Programming (MEP), and Decision Tree (DT), to forecast the compressive strength of 3D printed fiber-reinforced concrete (3DP-FRC). The dataset comprises 299 data points with sixteen variables gathered from experimental research studies. For training the model, 70 % of the dataset was used, while the remaining 30 % was reserved for model testing. Several statistical metrics were utilized to evaluate the accuracy and applicability of the models. In addition, SHapley Additive exPlanations (SHAP), partial dependence plots, and individual conditional expectations approach were employed for the interpretability of the models. The proposed GEP, MEP, and DT models indicated enhanced efficacy, exhibiting correlation coefficient (R) scores of 0.996, 0.987, and 0.990, with mean absolute errors (MAE) of 1.029, 4.832, and 2.513, respectively. Overall, the established GEP model demonstrated exceptional performance compared to MEP and DT, showcasing high prediction precision in assessing the strength of 3DP-FRC. Moreover, a simple empirical formulation has been devised using GEP to predict the compressive strength, offering a simplified and efficient approach for predicting 3DP-FRC strength. The SHAP approach identified water, silica fume, fiber diameter, curing age, and loading directions as leading controlling parameters in predicting strength of 3DP-FRC. In summary, the proposed models can potentially minimize both the computational workload and the need for experimental trials in formulating the mixed design of 3D-printed concrete.

MODIFICATION OF THE ATTENUATION EQUATION FOR PEAK GROUND ACCELERATION (PGA) IN THE NORTH SUMATERA REGION

Prediction of the empirical formula for ground acceleration is an important thing to analyze for seismic estimation in an area. This needs to be done to reduce the negative impact of earthquakes as a more appropriate mitigation effort in planning and designing earthquake-resistant buildings. Therefore, the empirical formulation from Zhaou, et al is used, which is well adapted to the seismotectonic conditions of the North Sumatra region, which has high seismic vulnerability, considering that this region is located in an active subduction zone. This advantage allows the model to more accurately predict the maximum ground acceleration (PGA) produced by earthquakes in the region. The purpose of this research is to obtain an empirical formulation of the maximum ground acceleration value based on seismic parameters such as distance, depth, and magnitude. It will also show the relationship between these parameters and the value of maximum ground acceleration or peak ground acceleration (PGA) in the North Sumatra region. This study modifies the empirical formulation of Zhao, et al data sourced from the Meteorology Climatology and Geophysics Agency (BMKG) of North Sumatra in 2017-2023 with a magnitude of 3 - 6 Mw. This research uses non-linear regression with the least squares method. The results of the analysis of the empirical formula produce the constant value sought a = 1.4703 b = -0.0025, c = 20.7441, d = 0.0196, e = 0.0015, S_SS = -2.0843, and S_SL = -0.0529. An empirical formula was obtained for the North Sumatra region based on the research results. This equation can be used on a scale of 3.0 - 6.0 Mw and a distance to the earthquake source between 0 - 300 km. The relationship between each parameter of this empirical formula is that the PGA value will increase with the magnitude, and the PGA value will decrease as the epicentre distance increases.

Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations

The coastal zone is a dynamic region that can change rapidly and significantly with respect to the morphology of the beach and incoming wave conditions. Runup forecasts may be improved by adapting a dynamic approach that allows for different runup models to be implemented in response to changes in beach state. Accurately forecasting wave runup is critical to characterize exposure to coastal hazards and provide an early warning against potential erosion and inundation. Here, we developed a decision tree model to produce a weighted ensemble of existing runup models to predict 1.25 years of runup at Duck, North Carolina, USA. We then applied the calibrated decision tree model to reproduce observed runup during the DUNEX experiment in Pea Island, North Carolina, USA. We found that the decision tree approach yielded a prediction that was comparable or greater in accuracy (i.e. higher r2, lower RMSE) than the individual runup models. We also interrogated the decision tree predictions to determine how the individual models perform relative to each other and why certain models perform better than others under the same observed wave and beach conditions. We found that the decision tree approach drew on the processes represented in the individual models in the ensemble to produce a forecast that is accurate and explainable without relying on prior knowledge of the study site(s) or requiring manual adjustments beyond the initial model training.

Numerical estimation of wave breaking forces on seawalls

ABSTRACT This paper presents a computationally efficient numerical scheme to estimate wave forces from plunging breakers on vertical rigid seawalls. The approximated velocity equation, an asymptotical solution of the Green-Naghdi equation in the Camassa-Holm scaling, is utilized to obtain the evolution of the free surface and the depth-averaged horizontal velocity over time, leading to the formation of a plunging breaker. The uniform dynamic pressure along the depth is obtained by utilizing the unsteady Bernoulli equation. The wave forces on seawalls are then determined using Froude-Krylov theory, with adjustments for diffraction effects. The results, while being comparable to some existing formulations, notably differ with some others. This highlights the limitations imposed by the inherent assumptions in the existing empirical formulations and underlines the importance of the proposed methodology that is founded on an accurate modelling of the wave breaking process to estimate wave breaking forces on seawalls.

Modeling of equivalent strain in 2D cross-sections of open die forged components using neural networks

Open die forging is one of the oldest manufacturing methods known to remove defects in the ingot resulting from the casting process. The improved properties of the final component are highly dependent on the strain distribution. Although sinusoidal equations and empirical formulations have been already used to estimate the strain, they have been applied only to the core of the workpiece. In this work, a novel approach is presented to model the equivalent strain distribution in 2D cross-sections, in the direction of the press, of open die forged components using neural networks. The proposed method efficiently combines a parametric sinusoidal function with a neural network to learn the complex relationships between the process parameters and the resulting local strain. The neural network is trained on a dataset of finite element (FE) simulations of rectangular geometries that cover a wide range of aspect ratios, bite ratios, and height reductions. The presented methodology with near real-time prediction capabilities shows good agreement with FE results. Moreover, the parametric function captures the characteristic pattern of the strain distribution and reveals certain physical relationships affecting the deformation of the material. These patterns are later examined by analyzing the parameters identified in the parametric sinusoidal function.

Global Estimates of Particulate Organic Carbon Concentration From the Surface Ocean to the Base of the Mesopelagic

AbstractThe gravitational settling of organic particles from the surface to the deep ocean is an important export pathway and one of the largest components of the ocean carbon pump. The strength and efficiency of the gravitational pump are often measured using metrics reliant on reference depths and empirical formulations that parameterize the relationship between depth and the flux or concentration of particulate organic carbon (POC). Here, BGC‐Argo profiles were used to identify the isolume where POC concentration, [POC], starts to decline, revealing attenuation trends below this isolume that are remarkably consistent across the global ocean. We developed a simple empirical approach that uses observations from the first optical depth to predict [POC] from the surface ocean to the base of the mesopelagic (1,000 m), allowing assessments of spatial and temporal variability in gravitational pump efficiencies. We find that rates of [POC] attenuation are high in areas of high biomass and low in areas of low biomass, supporting the view that bloom events sometimes result in a relatively weak deep biological pump that is characterized by low transfer efficiency to the base of the mesopelagic. Our isolume‐based attenuation model was applied to satellite data to yield the first remote sensing‐based estimate of integrated global POC stock of 3.02 Pg C over the top 1,000 m, with an uncertainty of 0.69 Pg C. Of this total stock, approximately 1.02 Pg was located above the reference isolume where [POC] begins to attenuate.

An empirical tool for predicting the presence or absence of coseismic displacements at GNSS stations

Unmodeled displacements in GNSS times series, induced by instrumental artifacts or geophysical events, create significant biases in station trajectory parameters that can propagate into the reference frame itself. While non-tectonic ‘jumps’, such as equipment changes, affect only a specific GNSS station, seismically-induced displacements can affect large numbers of sites, severely threatening the frame’s stability. Manually reviewing individual GNSS time series for such effects is highly impractical because there can be thousands of GNSS stations in a frame, and the total number of earthquakes Mw ≥ 6.0 since GPS became fully operational is + 5100. To avoid this time-consuming task, automated methods rely on empirical power-law functions to determine which earthquake-station pairs require coseismic displacement parameters. Still, ‘conservative’ power-law functions tend to add coseismic offsets to stations that do not need them, which can also threaten the stability of the frame. In this work, we present an empirical formulation that was obtained using 809 global seismic events to fit power-law parameters that do not overestimate the region of influence of earthquakes. Our method is based on a two-level selection process: level 1 is isotropic and only considers the epicentral distance between the stations and the earthquake, and level 2 uses the geophysical parameters of the earthquake to predict a ‘tighter’ displacement pattern to select which stations require coseismic trajectory parameters. We applied our level 2 method to a database of ~ 4700 event-station pairs and showed that it removed ~ 55% of the total pairs, all of which had been falsely selected by level 1.

The operationally ready full 3D magnetohydrodynamic model from the Sun to Earth: COCONUT+Icarus

Context. Solar wind modelling has become a crucial area of study due to the increased dependence of modern society on technology, navigation, and power systems. Accurate space weather forecasts can predict upcoming threats to Earth’s geospace and allow for harmful socioeconomic impacts to be mitigated. Coronal and heliospheric models must be as realistic as possible to achieve successful predictions. In this study, we examine a novel full magnetohydrodynamic (MHD) chain from the Sun to Earth. Aims. The goal of this study is to demonstrate the capabilities of the full MHD modelling chain from the Sun to Earth by finalising the implementation of the full MHD coronal model into the COolfluid COroNa UnsTructured (COCONUT) model and coupling it to the MHD heliospheric model Icarus. The resulting coronal model has significant advantages compared to the pre-existing polytropic alternative, as it includes more physics and allows for a more realistic modelling of bi-modal wind, which is crucial for heliospheric studies. In particular, we examine different empirical formulations for the heating terms in the MHD equations to determine an optimal one that would be able to mimic a realistic solar wind configuration most accurately. Methods. New heating source terms were implemented into the MHD equations of the pre-existing polytropic COCONUT model. A realistic specific heat ratio was applied. In this study, only thermal conduction, radiative losses, and approximated coronal heating function were considered in the energy equation. Multiple approximated heating profiles were examined to see the effect on the solar wind. The output of the coronal model was used to onset the 3D MHD heliospheric model Icarus. A minimum solar activity case was chosen as the first test case for the full MHD model. The numerically simulated data in the corona and the heliosphere were compared to observational products. First, we compared the density data to the available tomography data near the Sun and then the modelled solar wind time series in Icarus was compared to OMNI 1-min data at 1 AU. Results. A range of approximated heating profiles were used in the full MHD coronal model to obtain a realistic solar wind configuration. The bi-modal solar wind was obtained for the corona when introducing heating that is dependent upon the magnetic field. The modelled density profiles are in agreement with the tomography data. The modelled wind in the heliosphere is in reasonable agreement with observations. Overall, the density is overestimated, whereas the speed at 1 AU is more similar to OMNI 1-min data. The general profile of the magnetic field components is modelled well, but its magnitude is underestimated. Conclusions. We present a first attempt to obtain the full MHD chain from the Sun to Earth with COCONUT and Icarus. The coronal model has been upgraded to a full MHD model for a realistic bi-modal solar wind configuration. The approximated heating functions have modelled the wind reasonably well, but simple approximations are not enough to obtain a realistic density-speed balance or realistic features in the low corona and farther, near the outer boundary. The full MHD model was computed in 1.06 h on 180 cores of the Genius cluster of the Vlaams Supercomputing Center, which is only 1.8 times longer than the polytropic simulation. The extended model gives the opportunity to experiment with different heating formulations and improves the approximated function to model the real solar wind more accurately.

Wave overtopping discharges at rubble mound structures in shallow water

Wave overtopping of coastal structures has been studied using physical model experiments with rubble mound breakwaters in shallow water. The mean overtopping discharge is determined for three different foreshore slopes and various hydrodynamic conditions. The hydrodynamic results confirm that energy is transferred to low-frequency waves in very shallow water and that the short waves are in phase with the lower-frequency waves in very shallow water. As a result, the extreme waves (e.g. 2% exceedance wave height) become relatively large in very shallow water due to the energy of the low-frequency waves affecting thereby the wave overtopping. To estimate the amount of energy at the low-frequency waves, an expression is derived which reasonably accurately predicts the low-frequency wave energy (RMSE of 0.06). Considering the non-dimensional overtopping discharge, the existing formulations for the non-dimensional mean wave overtopping discharge perform poorly to reasonably in shallow water with RMSLE ranging from 1.04 to 2.92. A parameter sensitivity study shows that the short-wave steepness, relative crest height and the low-frequency wave height are the most important parameters when predicting the mean overtopping discharge in shallow water. When including the short-wave steepness and relative crest height in an empirical formulation the RMSLE for the current dataset reduces to 0.69. A further increase in accuracy is found when the low-frequency wave height and 2% exceedance wave height are included (RMSLE 0.64).

MONTPEL: A multi-component Penman-Monteith energy balance model

Mechanistic modelling is gradually replacing empiricism in crop models, focusing on leaf-level physiological processes. This shift necessitates simulating crop surface temperature at infra-canopy sub-daily scales but many crop models still rely on empirical formulations for canopy temperature estimation, typically on a daily basis. We developed MONTPEL, a multi-component Penman-Monteith model that allows simulating the crop energy balance with flexible canopy representations (“BigLeaf” vs. “Layered”, “Lumped” vs. “Sunlit-Shaded”) and accounts for atmospheric stability conditions. We analyzed the model behavior, sensitivity and accuracy, using measurements from four wheat (Triticum aestivum L.) experiments conducted under varying pedoclimatic and water stress conditions. Measurements included hourly energy balance terms (total net radiation, soil heat flux, sensible and latent energy fluxes), hourly temperature of the canopy surface or of leaves at different depths inside the canopy, and sunlit and shaded leaf temperatures around solar noon at different dates. MONTPEL reproduced the measured energy balance terms with a root mean square error (RMSE) between 21 and 87 Wm-2 and a coefficient of determination (R²) exceeding 0.65. The model's accuracy in simulating canopy temperature, with RMSE ≤ 2.2 °C and R² ≥ 0.92, remained consistent regardless of measurement scale. Adjusting the aerodynamic resistance for atmospheric stability minimized simulated canopy temperature errors, notably in semi-arid conditions. Crop latent energy flux and temperature were most sensitive to the maximal stomatal conductance (gs,max) parameter. However, using a single gs,max value across the simulated experiments yielded satisfactory results, suggesting a weak sensitivity to the temporal and site-to-site variability of gs,max. Distinguishing sunlit from shaded canopy fractions systematically resulted in lower latent energy fluxes compared to “Lumped” canopy representation results. Analysis identified limitations in the multi-component approach, particularly an unrealistic uniform temperature shift across leaf layers when soil surface temperature changes.

Lattice Boltzmann modeling of natural circulation loop with emphasis on non-Boussinesq mechanism

The natural circulation loop is crucial for the safe and stable operation of nuclear reactors and other applications. Traditional numerical algorithms, based on the Boussinesq approximation, have limitations when dealing with large temperature differences and density disparity, and they do not fully address fluid compressibility. This paper adopts the decoupled and stabilized lattice Boltzmann method (DSLBM) with a non-Boussinesq algorithm to study the natural circulation loop. The DSLBM provides a detailed flow description under large temperature and density differences, incorporating the pseudopotential multiphase model, temperature equation, and state equation, without relying on assumptions. The study examines the loop's performance under various temperature differences, central height differences, and heating source lengths, focusing on mass flow rate, driving head, and heating power. It reveals the energy performance, flow characteristics, and heat transfer properties of the loop, highlighting the physical mechanisms involved. Comparison with the empirical formulation of the incompressible equation from the theoretical aspect shows that when the temperature difference coefficient is lower than 0.15, the two methods are not much different from each other. When the temperature difference coefficient reaches 0.2, 0.3, and 0.4, the difference between the two methods is 9.47%, 19.11%, and 42.64%, respectively. Consequently, the Boussinesq approximation can be compensated by DSLBM, which proves the value of the application of the algorithm in exploiting the compressibility of fluids. The dimensionless fitting correlation with greater universality is obtained, which helps to predict the properties of the natural circulation loop with varying temperature differences, friction coefficients, and geometric structures. The research in this paper will lay the foundation for optimizing the system design of the natural circulation loop and improving energy utilization efficiency.

A performance-based ballistic design framework for RC panels and a probabilistic model for crater quantification

Ballistic design is crucial for widely utilised concrete structures in today’s unpredictable world. In the past, extensive experimental and numerical studies have investigated concrete panels, resulting in design guidelines and empirical formulations for local damage parameters. However, with the advent of performance-based design, notably in seismic design, the ballistic design of concrete structures lacks a comprehensive design philosophy. Additionally, deterministic empirical formulations for local damage parameters, particularly in the case of reinforced concrete (RC) panels’ crater formation, necessitate a probabilistic treatment. Consequently, this paper addresses the need for a well-defined ballistic design philosophy for concrete structures and introduces a probabilistic approach for crater quantification. Firstly, a novel multi-level and multi-parameter performance-based design framework for RC panels is proposed, centred on damage states, namely depth of penetration and crater area. This framework establishes an innovative design philosophy featuring four damage levels for each state. Secondly, using dimensionless explanatory functions, a Bayesian inference model is developed to estimate the crater diameter in RC panels under hard projectile impact, accounting for the associated uncertainties. LS-Dyna is used for numerical modelling of RC panels and rigid projectiles. The numerical models are validated with the experimental data and are utilised to develop the probabilistic model. This study reveals the profound influence of projectile and concrete properties in addition to the incidence angle of the projectile on crater formation. The developed probabilistic model is validated with experimental results demonstrating its reliability and accuracy. Consequently, a reliable design formula for estimating crater diameter for RC panels under hard projectile impact is proposed in addition to the novel ballistic design framework.

Effects of combustion progress variable and Karlovitz number on the scalar dissipation rate of turbulent premixed hydrogen-enriched methane–air flames: An experimental study

The scalar dissipation rate plays a key role in the modeling of turbulent premixed flames. Despite the above, our knowledge concerning the effects of several parameters, such as the combustion progress variable, Karlovitz number, and fuel composition on the scalar dissipation rate requires to be developed. This is carried out experimentally here. Two mixtures, methane–air (Lewis number of unity) and hydrogen-enriched methane–air (Lewis number of 0.8), are examined. Three passive (perforated plates) and one active turbulence generators are used, leading to a wide range of turbulence intensities with the corresponding Karlovitz number changing from 0.1 to 34.5. The hot-wire anemometry and the Rayleigh scattering techniques are used separately to study the background non-reacting turbulent flow and the reacting scalar dissipation rate, respectively. Three models that are available in the literature for estimating the Favre-averaged scalar dissipation rate are experimentally assessed. Using experimental results obtained in this study, it is shown that moving from fresh reactants towards combustion products increases the above modeling parameters by 2–3 times, depending on the tested Lewis number. For each tested Lewis number, empirical formulations that include the effects of both the Karlovitz number and combustion progress variable on the modeling parameters are proposed. Care must be taken in extending the proposed models of the present study to test conditions that are not examined here.

Seismic Performance Assessment of Corroded Reinforced Concrete Columns Based on Codal Provision and Empirical Formulations

Seismic Performance Assessment of Corroded Reinforced Concrete Columns Based on Codal Provision and Empirical Formulations

Modeling the impact of terrain surface deformation on drag force using discrete element method and empirical formulation

Understanding motion-induced terrain deformation is essential for improving predictive models in geotechnical engineering, infrastructure development, and robotics. The existing analysis assumes a uniform and unchanged environment and lacks a description of the terrain deformation induced by motion, which is particularly significant when the grains are loosely packed. To address this gap, we performed numerical investigations to mathematically model the changes in the surrounding environment and their corresponding effects on resistance. Specifically, we examined the response of granular materials and the energy variation of the particles during motion. Increased energy in the direction of motion was categorized as influences above or below the medium surface. Aligned with the influencing factors, two variables were considered to quantify the impact on the drag force. Building upon the energy analysis and the framework of Resistive Force Theory, we proposed a drag force model for buried motion in loose granular terrain. Compared with the experimental data, the average predictive error decreased from 17.8% to less than 2.7%. Our study provides a feasible approach for incorporating the effects of terrain deformation into a drag force model, thereby enhancing the accuracy and contributing to the development of granular locomotion.

A compact open boundary treatment for a pure thermal plume by direct numerical simulation

This study introduces a novel open boundary treatment method for simulating thermal plumes in natural convection, utilizing a combination of the Perfectly Matched Layer (PML) and local one-dimensional inviscid (LODI) methods within a compressible flow solver for Direct Numerical Simulation (DNS). This innovative approach significantly reduces the size of the computational domain yet effectively captures the complex transition from laminar to turbulent flow. Validation of the proposed methodology includes comparisons with existing empirical formulations, experimental data, and well-resolved DNS results, incorporating both time-averaged and spectral analyses. Additionally, the buoyancy-induced laminar-turbulent transition is investigated to enhance understanding of the dynamics and behaviors of thermal plumes. The findings offer insights into optimizing computational efficiency without compromising the accuracy needed to analyze intricate fluid dynamics in thermal engineering applications.

A full-field solution to predict vibrations in the presence of a free surface

Blasting is a common activity in mining that can cause significant damage in the remaining rock mass. The most commonly used method for predicting vibrations is the Swedish or Holmberg-Persson approach. It is a simple and fast method that uses empirical formulation to estimate the vibration levels based on the explosive charge weight, distance to the source, and other parameters. However, it has limited accuracy and does not consider the effect of free surfaces or other boundary conditions. On the other hand, recently developed methods as the full-field solution (FFS) can provide more accurate predictions of vibrations due to excitations provoked by cylindrical columns of explosive, but the inclusion of free surfaces in analytical methods is a challenging problem. This paper presents a new approach that includes free surfaces in the FFS to predict vibrations analytically in the near-field. The main obstacle to including free surfaces in the FFS is the belief that the solution is non-decoupleable in P- and SV-waves separately. The Heelan's approach was previously used to decouple them, but it only provides valid radiation patterns in the far-field and does not account for intrinsic attenuation. In this paper, we address this problem, allowing for the inclusion of free surfaces in the FFS.

Development of Carbon Composite Blades within the Context of the Experimental Validation of a CFD-Based Design Tool for Contra-Rotating, Electric Fan Engines

Electric propulsion systems have emerged as a disruptive technological approach towards achieving sustainable and climate-neutral aviation. To expand the operational envelope of such propulsion units in terms of altitude and velocity, an enclosing duct and counter-rotating rotors to enhance efficiency can be utilized. In this study, an iterative CFD-based design tool developed for these novel propulsion systems is utilized to design a reference engine, having a classic rotor–stator configuration. Being the key component of this propulsor, a manufacturing process for composite blades is presented. This effort aims to make state-of-the-art technology accessible to smaller research projects, promoting the widespread adoption of electric propulsion technology in the aviation sector. By experimental investigations of the blade elongation both in tensile tests and engine operation, measuring the tip clearance with a high-speed camera, this process could be validated to facilitate the transferability of research. Finally, the performance of the manufactured engine is measured by a five-hole miniature probe, not only in design point but also in off-design operation. The results indicate that a substantial reduction in discrepancies between initial specifications, subsequent CFD simulations, and experimental investigations compared to conventional design tools relying on empirical formulations can be achieved due to the CFD-based approach. This allows the CFD-based tool to be validated for designing scalable contra-rotating fan engines.

A Runoff‐On‐Grid Approach to Embed Hydrological Processes in Shallow Water Models

AbstractCatchment‐scale hydrological models encountered dichotomies with the numerical hydrodynamic models when describing surface routing process. We propose a new modeling framework, the so‐called “Runoff‐On‐Grid” approach, for embedding distributed process‐based hydrological modeling into shallow water models, as an alternative to the traditional Fully Hydrodynamic Approach (also known as Rain‐On‐Grid). Antecedent Soil Moisture, subsurface dynamics, and other topsoil hydrological processes are implicitly integrated in the governing hydrodynamic equations via the proposed methodology. The resulting hydrological‐hydrodynamic coupling, based on the DREAM distributed hydrological model and the Iber+ shallow water model, enhances the capabilities of both reference models. Through introducing non‐negligible runoff generation sources, the Runoff‐On‐Grid approach extends the surface hydrodynamic modeling to medium‐sized vegetated and/or (semi)humid catchments, bypassing the limitations of the widespread hydrological losses' empirical formulations. Employed in an event‐based analysis within a High‐Performance Computing framework, the DREAM‐Iber model provides an efficient and reliable reconstruction of the November 2020 flood that occurred in Crotone (Italy), envisaging consequences of similar future scenarios. We show that the proposed modeling technique, nested within emerging environmental technologies and robust on‐site data, details the flood hazard inducing processes merging physical hydrology with advanced hydrodynamics.