Correct Simulation Research Articles

Considerations in designing climate change assessments for complex, non-linear hydrological systems

Bias correction of climate model simulations is vital to allow climate change impacts to be assessed for water resources systems. However, there has been limited research to date on the implications of system non-linearity on bias correction approaches. Here we bias correct regional climate model simulations of precipitation and evapotranspiration and use the output to force hydrological and water balance models of five small, interconnected lakes located south west of Sydney. We show that substantial, non-linear storage within the lakes amplifies biases that are not evident when the climate forcing or even the hydrological model simulations are evaluated using daily distributions of the climate variables and streamflow.The non-linearity in the stage-storage relationships of the lakes means that each lake responds differently to the same climate forcings. For example, ensemble mean projections for one lake suggest increases in water level across the full distribution of lake levels, whilst other lakes are projected to have decreasing water levels up to the median of the distribution, but increases during wetter conditions. These differences are explained by the varying influence of potential evapotranspiration increases depending on the surface area of the lakes at different depths. Using bottom up climate change assessments, we further explore these non-linear responses of the lakes to different climate forcings. We show that bottom up climate change assessments can provide information on the relative role of potential evapotranspiration changes compared to precipitation changes, providing more guidance to ecosystem managers than just using bias corrected climate model simulations alone. The paper discusses opportunities for future work to improve representation of climate attributes important for storage dominated water resource and natural ecosystems

Novice and Experienced Teachers’ Beliefs and Stated Attitudes About Oral Corrective Feedback

This study aims to investigate novice and experienced teachers’ stated beliefs and attitudes about oral corrective feedback (OCF) and the similarities and differences between them. Certain qualitative and quantitative research techniques were employed in this study to answer the research questions. Regarding qualitative design, some interviews were conducted to find out novice and experienced teachers’ stated beliefs about OCF. The quantitative part of the study is based on data collected through Situations for Error Correction (SEC) Simulation. The novice and experienced teachers were asked to write how they would respond to each situation and why they would respond that way. The aim of this tool was to identify the novice and experienced teachers’ stated attitudes. The results indicated that although there were some similarities between the novice and experienced teachers’ beliefs and attitudes about OCF, their OCF strategies and beliefs varied regarding correcting errors concerning language components.

Exploring tau protein and amyloid-beta propagation: A sensitivity analysis of mathematical models based on biological data

Alzheimer’s disease is the most common dementia worldwide. Its pathological development is well known to be connected with the accumulation of two toxic proteins: tau protein and amyloid-β. Mathematical models and numerical simulations can predict the spreading patterns of misfolded proteins in this context. However, the calibration of the model parameters plays a crucial role in the final solution. In this work, we perform a sensitivity analysis of heterodimer and Fisher–Kolmogorov models to evaluate the impact of the equilibrium values of protein concentration on the solution patterns. We adopt advanced numerical methods such as the IMEX-DG method to accurately describe the propagating fronts in the propagation phenomena in a polygonal mesh of sagittal patient-specific brain geometry derived from magnetic resonance images. We calibrate the model parameters using biological measurements in the brain cortex for the tau protein and the amyloid-β in Alzheimer’s patients and controls. Finally, using the sensitivity analysis results, we discuss the applicability of both models in the correct simulation of the spreading of the two proteins.Statement of significance: Alzheimer’s disease is related to the accumulation of tau protein and amyloid-β. Mathematical models to predict the spreading patterns require accurate parameter calibration. In this work, we perform a sensitivity analysis of heterodimer and Fisher–Kolmogorov models to evaluate the impact of the equilibrium values of protein concentration on the solution patterns obtained with advanced numerical simulations on patient-specific brain geometry derived from magnetic resonance images. By using biological measurements in the brain cortex for the proteins in Alzheimer’s patients and controls, we use sensitivity analysis to discuss the applicability of models in simulating protein spreading.

Characterizing coastal aquifer heterogeneity from a single piezometer head chronicle

We propose a new method for identifying the hydraulic properties of coastal aquifers based on their response to continental and marine influences observed from isolated piezometers displaying both tidal and seasonal fluctuations. From a single piezometer, a first approximation of hydraulic conductivity and porosity can be derived analytically from the mean hydraulic head and its tidal fluctuations. If this approximation also allows a correct simulation of seasonal head variations, the aquifer can be modelled as homogeneous. Otherwise, the seasonal head variations provide constraints on the heterogeneity of hydraulic properties. This new method is applied to a coastal aquifer in Normandy, formed by a relatively flat, kilometer-wide coastal strip of Quaternary sands followed by a foothill of higher Brioverian shales. Hydraulic head variations monitored daily for 8 years in a piezometer located in the Quaternary sand formation enable us to constrain their hydraulic conductivity (5–20 m.d−1) and porosity (7–10 %) within restricted ranges. They also suggest the existence of a significantly more porous formation (15–30 %) upstream of the observation piezometer, which could consist of more porous sands or fractured shales. In the case of limited surface–subsurface interactions, the identification method relies on a step-by-step combination of analytical solutions and simplified 1D numerical models. When more important surface–subsurface interactions are involved, a spatialized 2D model is used, that allows the analysis of seepage areas. Based on this case study, we gradually introduce 1D and 2D approaches, and discuss their applicability. We finally illustrate one major application of this approach by analyzing the interest of aquifer properties investigation on groundwater-induced flooding vulnerability.

Validated Dynamic Stall Simulation of Pitching Low Reynolds Number Airfoils

Deep dynamic stall is one of several complex behaviors that result in extreme variation of the aerodynamic loads on small wind turbine (SWT) blades during unsteady wind conditions. In this study, unsteady Reynolds-averaged Navier–Stokes simulations are performed for two low Reynolds number (Re) airfoils where sinusoidal pitching is applied to replicate the dynamic stall that occurs on rotating SWT blades. The SD 7037 airfoil is simulated at Re=4.1×104 and a pitching reduced frequency of k=0.08, and the S833 airfoil is at Re=1.7×105 and k=0.06. The simulated lift coefficient and dynamic stall timing agree with experimental data, which is attributed to the wall-normal resolution of the mesh and is an advancement from the early prediction of stall seen consistently in previous numerical studies. The accurate prediction of dynamic stall is found to be dependent on the correct simulation of the bursting of the laminar separation bubble (LSB), which initiates the complete separation of the boundary layer and the formation of a leading-edge vortex. The γ−Reθ,t k−ω model combined with the use of a fine mesh at the airfoil leading edge results in an accurate simulation of the bursting LSB and the correct prediction of the deep dynamic stall.

SIMKEF – A decision support system to predict the infestation probability of Drosophila suzukii

Drosophila suzukii is an invasive, polyphagous pest of soft-skinned fruits, causing widespread economic damage in orchards and vineyards worldwide, including Germany. Decision support systems (DSS) are necessary to control D. suzukii prior to the occurrence of considerable yield loss in terms of quantity and quality. In this study, we introduce the newly developed DSS SIMKEF, simulating and predicting the daily infestation probability of D. suzukii during the fruit ripening phase of different host crops, using hourly air temperature data. The model validation of daily infestation probability of D. suzukii resulted in correct simulations of 75% in blackberry, 54% in sweet cherry, 54% in grapevine ’Dornfelder’, 40% in sour cherry, 27% in grapevine ‘Portugieser’, and 25% in raspberry. Therefore, the current prototype of SIMKEF needs to be further improved to achieve higher accuracies of the simulation results in the future.

The impact of mesh size, turbulence parameterization, and land‐surface‐exchange scheme on simulations of the mountain boundary layer in the hectometric range

AbstractThe horizontal grid spacing of numerical weather prediction models keeps decreasing towards the hectometric range. We perform limited‐area simulations with the Icosahedral Nonhydrostatic (ICON) model across horizontal grid spacings (1 km, 500 m, 250 m, 125 m) in the Inn Valley, Austria, and evaluate the model with observations from the Cross‐Valley Flow in the Inn Valley Investigated by Dual‐Doppler LIDAR Measurements (CROSSINN) measurement campaign. This allows us to investigate whether increasing the horizontal resolution automatically improves the representation of the flow structure, surface exchange, and common meteorological variables. Increasing the horizontal resolution results in an improved simulation of the thermally induced circulation. However, the model still faces challenges with scale interactions and the evening transition of the up‐valley flow. Differences between two turbulence schemes (1D turbulence kinetic energy (TKE) and 3D Smagorinsky) emerge due to their different surface transfer formulations, yielding a delayed evening transition in the 3D Smagorinsky scheme. Generally speaking, the correct simulation of the mountain boundary layer depends mostly on the representation of model topography and surface exchange, and the choice of turbulence parameterization is secondary.

Revisiting the dry friction-like magnetic vector hysteresis model

Physically correct simulations of magnetic devices require precise and energy-consistent hysteresis models. Electrical steel sheets of, e.g., power transformers or rotating machines represent one crucial element in such a simulation since the hysteresis is revealed uni- and multi-axial, and the steel sheets may have anisotropy. A dedicated vector hysteresis model based on dry friction-like pinning is revisited and derived in terms of thermodynamic state equations in a theoretical manner. The model is then linked to an incremental energy minimization procedure used in elasto-plasticity theory. A numerically efficient two-dimensional solution scheme of the hysteresis model is extended to the three-dimensional case. Rotational losses are also in the scope of this paper since the model cannot describe this loss type by nature. Therefore, numerical adaptations are discussed to account for vanishing rotational losses in saturation.

An improved canopy interception scheme into biogeochemical model for precise simulation of carbon and water fluxes in subtropical coniferous forest

The process-based Biome-BGC (Biome Biogeochemical Cycles) model is widely used to simulate the fluxes of carbon and water of terrestrial ecosystems. While exhibiting excellent performance in simulating carbon cycle processes, this model provides a relatively simple simulation of the water cycle processes, particularly in canopy interception, which may result in significant errors in evapotranspiration (ET) and soil moisture estimations. This study initially refined the temporal scale of the original Biome-BGC model from a daily scale to an hourly scale, and then incorporated a theoretical interception module into the hourly-scale model for correcting canopy interception algorithm, to enhance the accuracy of hydrological processes simulations. The modified model was tested using the half-hourly observed meteorological and eddy covariance flux data at the Qianyanzhou subtropical coniferous forest site. In comparison with the original daily-scale model, simulations from the hourly-scale model showed better agreement with measurements at Qianyanzhou forest site, with 30.61 % and 40.92 % lower annual average gross primary productivity (GPP) and ET simulations. Although the accuracy of GPP and ET simulations did not show a significant improvement after canopy interception correction, it notably enhanced the accuracy of canopy interception and soil moisture simulations. The canopy interception rates were 55.8 % and 44.1 % for the original daily-scale and hourly-scale model, obviously overstimated compared with canopy correction simulation (31.1 %), leading to a significant underestimation of runoff. The canopy interception and runoff simulations after interception correction were proven relatively more reasonable. Additionally, the coefficient of determination (R2) for measured vs simulated soil water content (SWC) were 0.38, 0.51 and 0.60 for the original daily-scale, hourly-scale, and canopy interception correction simulations respectively, with a notable improvement in accuracy. This study suggested that improvements in temporal scale and canopy interception for process-based biogeochemical model can provide a better understanding of the carbon and water exchanges in response to instantaneous meteorological conditions and support improved water resource management.

Improving crop yield estimation by unified model parameters and state variable with Bayesian inference

Data assimilation techniques integrating crop growth models and remote sensing technologies offer a feasible approach for large-scale crop yield estimation. Previous research has primarily focused on either recalibrate the uncertain model parameters or updating model state variables independently using remotely sensed observations. In this study, we developed a two-step inference algorithm that couples the parameter inference and the state update, to solve the joint posterior distribution of uncertain parameters and state variables given the remote sensing observations. An Observing Simulation System (OSS) experiment was first performed based on the WOFOST crop model to validate the effectiveness of the parameter inference method. The results indicate that, the parameter inference method successfully improved the estimation of different types of model parameters and enhanced yield estimation. Furthermore, leaf area index (LAI) retrieved from Sentinel-2 was assimilated into the WOFOST model to simulate winter wheat yield at the plot scale in the northeastern part of Henan Province. The results demonstrated that the proposed two-step inference algorithm can more effectively correct model simulation biases and improve winter wheat yield estimation accuracy (R²=0.58, MAPE=12.75 %, and RMSE=1112 kg·ha⁻¹), outperforming the standard EnKF algorithm (R²=0.51, MAPE=14.62 %, RMSE=1328 kg·ha⁻¹). Overall, attributed to its unified approach to estimating both model parameters and state variables, the proposed two-step inference algorithm shows promising application prospects for data assimilation-based crop yield estimation.

Evaluating simulation tools for securing sensor data with blockchain: A comprehensive analysis

Securing data generated from diverse sensors poses a critical challenge in contemporary applications, particularly due to the escalating volume of data and its vulnerability to security breaches. Blockchain technology has emerged as a promising solution, yet the implementation of blockchain applications entails significant costs. Assessing the feasibility of such implementations through simulation is imperative but has been hindered by a lack of details survey about the simulation tools leading to wrong choice of tools by the researchers, as every application is unique and requires specific blockchain platform as per use.This paper covers an extensive survey of 32 simulation tools used in blockchain, focusing on their advantages, limitations, platform used, and capability to evaluate performance metrics of blockchain applications. This enables the researchers to choose the correct simulation tool for their work. The work allows the programmer to test their application using the correct simulation tool, saving implementation costs and time. Furthermore, we discussed CBlockSim, a simulation tool designed specifically for blockchain applications, elucidating its functionality through a comparative study of Bitcoin and Ethereum.Our work contributes to advancing the understanding and application of simulation tools in blockchain-based sensor data security, offering insights that can significantly enhance the security posture of sensor networks.

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

ABSTRACT The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very well with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, the delta change method seems to have with no biases for maximum temperature, but increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles: A genetic programming approach

Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment, typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction. Despite recent advancements in machine learning techniques, there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available. This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles, employing a Genetic Programming (GP) approach. Utilizing a soil dataset extracted from existing literature, comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading, the study intentionally limited input parameters to three features to enhance model simplicity: Standard Penetration Test (SPT) corrected blow count (N60), Peak Ground Acceleration (PGA), and pile slenderness ratio (L/D). Model performance was assessed via coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), with R2 values ranging from 0.95 to 0.99 for the training set, and from 0.92 to 0.98 for the testing set, which indicate of high accuracy of prediction. Finally, the study concludes with a sensitivity analysis, evaluating the influence of each input parameter across different pile types.

Beam test results of the orbit correction at the low-energy superconducting accelerator section in the RAON accelerator

The RAON accelerator has been constructed for various science programs with stable ions and rare isotopes since 2011. After the installation of the injector section was completed in 2020, the low-energy superconducting accelerator (SCL3) section was also installed consecutively until 2022, and the beam commissioning using an argon beam is in progress from the injector section to the SCL3 section. For successful beam commissioning, various beam dynamics studies have been carried out, and the research of the orbit correction has been also conducted to determine the location and the number of steering magnets and BPMs for safe and efficient beam operation. In the SCL3 section, the orbit correction simulation was carried out as varying the number of steering magnets for the consideration of the correction of distorted beam orbit and the economic benefits, and then the beam test was carried out based on the simulation results during the beam commissioning. Here we will present the results of simulations and experiments for the orbit correction at the SCL3 section in the RAON accelerator.

Simulation of translational motion correction during cartesian brain MRI

Brain Magnetic Resonance Imaging (MRI) is invaluable for non-invasively capturing detailed anatomical and functional information. However, motion artifacts, particularly during brain imaging, can compromise the precision of scans. This study explores motion correction techniques, focusing on the widely-used PROPELLER method and its application to Golden-angle Cartesian Randomized Time-resolved (GOCART) acquisition. While PROPELLER effectively corrects in-plane translation and rotation, its use with cartesian data demands increased sampling. GOCART, a high-speed cartesian sampling scheme, has shown promise in Dynamic Contrast-Enhanced (DCE) MRI, yet its specific artifacts in brain imaging remain underexplored. Our simulation framework assesses PROPELLER correction for translational motion in GOCART-sampled data, examining two motion directions, varied frequencies, and different temporal resolutions. Serving as a vital pre-clinical testing tool, this platform contributes to the optimization of motion correction algorithms, addressing challenges and refining imaging protocols for enhanced diagnostic reliability in advanced brain MRI.

Experimental study and finite element analysis of heavy-duty escalator truss under full load conditions

The performance of the heavy-duty escalator truss greatly affects the stability and service life of the whole escalator system, and the manufacturing cost of truss structure accounts for more than 1/5. Thus, how to design the truss structure reasonably is a pivotal issue drawing the attention of numerous engineers and researchers. In this work, the experimental research of heavy-duty escalators under full load conditions were performed in terms of the end restraints, the docking port clearances, and the deflection. Based on the experimental results, the three-dimensional simulation model of truss structure was created, and the influences of various factors such as the internal chamfer of truss member, the lower deviation of truss member, the dead weight of escalator, and the pretension force of each bolt at the docking port were analyzed and quantified. Finally, the finite element model which can almost completely characterize the actual structure was obtained with slight difference. The conclusions drawn in this work provide the basis for the efficient design, correct simulation, low cost production and rapid installation of the heavy-duty escalator truss.

On the Validity of Constant pH Simulations.

Constant pH (cpH) simulations are now a standard tool for investigating charge regulation in coarse-grained models of polyelectrolytes and colloidal systems. Originally developed for studying solutions with implicit ions, extending this method to systems with explicit ions or solvents presents several challenges. Ensuring proper charge neutrality within the simulation cell requires performing titration moves in sync with the insertion or deletion of ions, a crucial aspect often overlooked in the literature. Contrary to the prevailing views, cpH simulations are inherently grand-canonical, meaning that the controlled pH is that of the reservoir. The presence of the Donnan potential between the implicit reservoir and the simulation cell introduces significant differences between titration curves calculated for open and closed systems; the pH of an isolated (closed) system is different from the pH of the reservoir for the same protonation state of the polyelectrolyte. To underscore this point, in this paper, we will compare the titration curves calculated using the usual cpH algorithm with those from the exact canonical simulation algorithm. In the latter case, titration moves adhere to the correct detailed balance condition, and pH is calculated using the recently introduced surface Widom insertion algorithm. Our findings reveal a very significant difference between the titration isotherms obtained using the standard cpH algorithm and the canonical titration algorithm, emphasizing the importance of using the correct simulation approach when studying charge regulation of polyelectrolytes, proteins, and colloidal particles.

Process-based classification of Mediterranean cyclones using potential vorticity

Abstract. Mediterranean cyclones (MCs) govern extreme weather events across the Euro-African Basin, affecting the lives of hundreds of millions. Despite many studies addressing MCs in the last few decades, their correct simulation and prediction remain a significant challenge to the present day, which may be attributed to the large variability among MCs. Past classifications of MCs are primarily based on geographical and/or seasonal separations; however, here we focus on cyclone genesis and deepening mechanisms. A variety of processes combine to govern MC genesis and evolution, including adiabatic and diabatic processes, topographic influences, land–sea contrasts, and local temperature anomalies. As each process bears a distinct signature on the potential vorticity (PV) field, a PV approach is used to distinguish among different “types” of MCs. Here, a combined cyclone-tracking algorithm is used to detect 3190 Mediterranean cyclone tracks in ECMWF ERA5 from 1979–2020. Cyclone-centered, upper-level isentropic PV structures in the peak time of each cyclone track are classified using a self-organizing map (SOM). The SOM analysis reveals nine classes of Mediterranean cyclones, with distinct Rossby-wave-breaking patterns, discernible in corresponding PV structures. Although classified by upper-level PV structures, each class shows different contributions of lower-tropospheric PV and flow structures down to the surface. Unique cyclone life cycle characteristics, associated hazards (precipitation, winds, and temperature anomalies), and long-term trends, as well as synoptic, thermal, dynamical, seasonal, and geographical features of each cyclone class, indicate dominant processes in their evolution. Among others, the classification reveals the importance of topographically induced Rossby wave breaking to the generation of the most extreme Mediterranean cyclones. These results enhance our understanding of MC predictability by linking the large-scale Rossby wave formations and life cycles to coherent classes of under-predicted cyclone aspects.

Multibody Models Generated from Natural Language

AbstractComputational models are conventionally created with input data, script files, programming interfaces, or graphical user interfaces. This paper explores the potential of expanding model generation, with a focus on multibody system dynamics. In particular, we investigate the ability of Large Language Model (LLM), to generate models from natural language. Our experimental findings indicate that LLM, some of them having been trained on our multibody code Exudyn, surpass the mere replication of existing code examples. The results demonstrate that LLM have a basic understanding of kinematics and dynamics, and that they can transfer this knowledge into a programming interface. Although our tests reveal that complex cases regularly result in programming or modeling errors, we found that LLM can successfully generate correct multibody simulation models from natural-language descriptions for simpler cases, often on the first attempt (zero-shot).After a basic introduction into the functionality of LLM, our Python code, and the test setups, we provide a summarized evaluation for a series of examples with increasing complexity. We start with a single mass oscillator, both in SciPy as well as in Exudyn, and include varied inputs and statistical analysis to highlight the robustness of our approach. Thereafter, systems with mass points, constraints, and rigid bodies are evaluated. In particular, we show that in-context learning can levitate basic knowledge of a multibody code into a zero-shot correct output.

Simulation and performance analysis of quantum error correction with a rotated surface code under a realistic noise model

The demonstration of quantum error correction (QEC) is one of the most important milestones in the realization of fully-fledged quantum computers. Toward this, QEC experiments using the surface codes have recently been actively conducted. However, it has not yet been realized to protect logical quantum information beyond the physical coherence time. In this work, we performed a full simulation of QEC for the rotated surface codes with a code distance 5, which employs 49 qubits and is within reach of the current state-of-the-art quantum computers. In particular, we evaluate the logical error probability in a realistic noise model that incorporates not only stochastic Pauli errors but also coherent errors due to a systematic control error or unintended interactions. While a straightforward simulation of 49 qubits is not tractable within a reasonable computational time, we reduced the number of qubits required to 26 qubits by delaying the syndrome measurement in simulation. This and a fast quantum computer simulator, , implemented on GPU allows us to simulate full QEC with an arbitrary local noise within reasonable simulation time. Based on the numerical results, we also construct and verify an effective model to incorporate the effect of the coherent error into a stochastic noise model. This allows us to understand what the effect coherent error has on the logical error probability on a large scale without full simulation based on the detailed full simulation of a small scale. The present simulation framework and effective model, which can handle arbitrary local noise, will play a vital role in clarifying the physical parameters that future experimental QEC should target. Published by the American Physical Society 2024