Simulation Error Research Articles

Mechanical Modeling and Finite Element Analysis of Typical Compliant Mechanisms Based on Flexible Leaf Springs

In recent years, compliant mechanisms have been widely applied across various fields due to their unique functionalities and superior attributes, demonstrating tremendous development potential and playing critical roles in several key areas. However, the distinct motion characteristics of compliant mechanisms necessitate the consideration of numerous factors in practical applications, making foundational theoretical formulas particularly important. The theoretical derivation of compliant mechanisms forms the essential basis for advancing their development and applications. This study focuses on several typical compliant mechanisms, deriving theoretical formulas by establishing mechanical models and conducting finite element simulations. The simulation errors, all within 1.5%, validate the accuracy of these formulas, thereby providing a theoretical foundation for the further application and development of compliant mechanisms.

A comparative study of experimental and simulated ultrasound beam propagation through cranial bones

Objective.Transcranial ultrasound is used in a variety of treatments, including neuromodulation, opening the blood-brain barrier, and high intensity focused ultrasound therapies. To ensure safety and efficacy of these treatments, numerical simulations of the ultrasound field within the brain are used for treatment planning and evaluation. This study investigates the accuracy of numerical modelling of the propagation of focused ultrasound through cranial bones.Approach.Holograms of acoustic fields after propagation through four human skull specimens were measured for frequencies ranging from 270 kHz to 1 MHz, using both quasi-continuous and pulsed modes. The open-source k-Wave toolbox was employed for simulations, using an equivalent-source hologram and a uniform bowl source with parameters that best matched the measured free-field pressure distribution.Main results.The average absolute error in k-Wave simulations with sound speed and density derived from CT scans compared to measurements was 15% for the spatial-peak acoustic pressure amplitude, 2.7 mm for the position of the focus, and 35% for the focal volume. Optimised uniform bowl sources achieved calculation accuracy comparable to that of the hologram sources.Significance.This method is demonstrated as a suitable tool for prediction of focal position, size and overall distribution of transcranial ultrasound fields. The accuracy of the shape and position of the focal region demonstrate the suitability of the sound speed and density mapping used here. However, large errors in pressure amplitude and transmission loss in some individual cases show that alternative methods for mapping individual skull attenuation are needed and the possibility of considerable errors in pressure amplitude should be taken into account when planning focused ultrasound studies or interventions in the human brain, and appropriate safety margins should be used.

Forecasting Renewable Energy Consumption Using a Novel Fractional Grey Reverse Accumulation Model

The accumulation operation is the most fundamental method for processing data in grey models, playing a decisive role in the accuracy of model predictions. However, the traditional forward accumulation method does not adhere to the principle of prioritizing new information. Therefore, we propose a novel fractional reverse accumulation, which increases the accumulation coefficient for new data to fully utilize the new information carried by the latest data. This led to the development of a novel grey model, termed the FGRM(1,1). This model was validated using renewable energy consumption data from France, Spain, the UK, and Europe, and the results demonstrated that the FGRM(1,1) outperformed other models in terms of simulation error, prediction error, and comprehensive error. The predictions indicated significant growth in renewable energy consumption for France and Spain, moderate growth for the UK, and robust growth for Europe overall. These findings highlight the effectiveness of the proposed model in utilizing new information and provide insights into energy transition and emission reduction potential in Europe.

Catalytic epoxidation of waste palm kernel oil using in situ performic acid formation

AbstractThe use of renewable resources in the epoxidation process can reduce the dependence on non‐renewable petroleum resources and contribute to a more environmentally friendly chemical industry. This study aims to investigate the epoxidation of waste palm kernel oil as a renewable feedstock. The synthesis of epoxidized waste palm kernel oil was conducted by reacting waste palm kernel oil, formic acid, and hydrogen peroxide in a one‐pot system. Currently, there is no reported literature on the simultaneous application of a catalyst for the epoxidation of waste palm kernel oil derived from industrial waste. The optimum process parameters were determined, including hydrogen peroxide to waste palm kernel oil molar ratio (1.5:1), formic acid to waste palm kernel oil molar ratio (0.5:1), and stirring speed (300 rpm). The optimum relative conversion to oxirane of epoxidized waste palm kernel oil was 88%. A mathematical model was developed using numerical integration based on the fourth‐order Runge–Kutta method as follows: = 0.894 mol·L−1·min−1, = 7.420 mol·L−1·min−1, and = 0.086 mol·L−1·min−1. Based on the findings of the kinetic study, the kinetic model was validated due to its minimal simulation error.

Research on modeling of simulation errors for aircraft bay door hydraulic system based on data-driven

Research on modeling of simulation errors for aircraft bay door hydraulic system based on data-driven

Estimation of the Impact of Climate Warming on Spring Wheat (Triticum aestivum L.) Phenology From Observations and Modelling in the Arid Region of Northwest China

ABSTRACTClimate warming has induced shifts in the phenological period and thus affected cultivar selection and effective crop management. Particularly, the great climate warming in the dry environment could have more effects on the phenology of spring wheat with the distinct cycle of biological events during growth. In this study, the daily observations of spring wheat phenology and meteorology from 1991 to 2018 were used to analyse changes in phenology concerning accumulated temperature in the Hexi Corridor region of Northwest China. Five crop growth models (WheatGrow, WOFOST, CropSyst, CERES‐Wheat and APSIM‐Wheat) were selected to evaluate the reliability of the phenological stage simulations in the study region. Results show that in the past 28 years, the annual accumulated temperature in the whole growth period from sowing to maturity increased by 3.08°C–8.35°C/a at three sites of the region. Climate warming shortened the phenological period at rates of 3.56–4.49 days/10a, mostly attributed to the shortened duration from anthesis to maturity. Statistical analysis demonstrated that the shortened phenological period cannot be simply expressed by the linear correlation between the length of phenological phases and accumulated temperature in the respective growth stages. The five wheat growth models after parameter validation can generally capture the phenological dates, and WOFOST performed best at the three sites. However, when the calibrated model was used for simulations of long‐term variations of phenological dates, the accumulated errors in simulations could result in large deviations of the predicted physiological change to the observations.

Numerical simulation for low permeability reservoirs based on POD-TPWL method

ABSTRACT Numerical simulation is a key technology to improve the development efficiency of low-permeability and tight oil reservoirs. In this paper, in order to improve the speed of reservoir numerical simulation, a complex two phase flow model was presented considering the threshold pressure gradient and stress sensitivity, and the application process of POD-TPWL method is derived and summarized. Furthermore, the computational accuracy and efficiency based on the POD-TPWL method was investigated. The simulation time and error of production data of POD-TPWL method and other methods were statistically analyzed and compared. The results reveal that the POD-TPWL method not only ensures accuracy and stability but also significantly improves computational speed. The calculation time of the full order model is 487 s, the calculation time of the POD method is 284 s, and the calculation time of the POD-TPWL method is 122 s. The calculation speed of the POD-TPWL method is almost 4 times faster than the full order model, the POD-TPWL method is more efficient in the numerical simulation of low permeability nonlinear seepage.

Numerical treatment for Darcy–Forchheimer flow of propylene glycol with carbon nanotubes under the impacts of MHD and activation energy

This study is the application of a recurrent neural networks with Bayesian regularization optimizer (RNNs-BRO) to analyze the effect of various physical parameters on fluid velocity, temperature, and mass concentration profiles in the Darcy–Forchheimer flow of propylene glycol mixed with carbon nanotubes model across a stretched cylinder. This model has significant applications in thermal systems such as in heat exchangers, chemical processing, and medical cooling devices. The data-set of the proposed model has been generated with variation of various parameters such as, curvature parameter, inertia coefficient, Hartmann number, porosity parameter, Eckert number, Prandtl number, radiation parameter, activation energy variable, Schmidt number and reaction rate parameter for different scenarios. The refinement of each data-set is processed through RNNs-BRO for attestation of the proposed scheme. The outcomes are provided through graphical interpretation. The increment of curvature parameter results in the acceleration of the velocity profile, while an opposite behavior is noticed for higher values of inertia coefficient, Hartmann number, porosity parameter for single wall carbon nanotubes (SWCNTs) as well as multi wall carbon nanotubes (MWCNTs). The temperature of fluid increases for both SWCNTs and MWCNTs as the curvature parameter, radiation parameter, Eckert number, and Hartmann number are increased. However, an opposite trend is noticed for Prandtl number. The concentration profile is enhanced for higher values of activation energy variable and curvature parameter for both SWCNTs and MWCNTs, whereas opposite trend is observed for reaction rate parameter, and Schmidt number. The effectiveness of scheme is endorsed through various statistical measures like regression index, error histograms, correlation analysis and convergence analysis showing a minimum level of mean square error (E-12 to E-04) for the comprehensive simulation of the proposed model.

Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area

In this study, the JiaoGang Basin in the Yangtze River Delta plains of the river network area was the research object. A basin water level simulation model was constructed based on the physical mechanism model and Mike software, and the parameters were calibrated and validated. Based on the dataset produced by the physical model, three types of ML models, Support Vector Machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), were constructed, trained, validated, and compared with the physical model. The results showed that the physical mechanism model met the water level simulation accuracy requirements at most stations. In the training and validation periods, the RF water level simulation and GBDT water level simulation models had root mean square errors (RMSEs) of all stations less than 0.25 and the Nash–Sutcliffe coefficient (NSE) of all stations was greater than 0.7. The physical mechanism model and ML water level simulation models can simulate the water level in the JiaoGang Basin better. The RF and GBDT models considerably outperform the physical mechanism model in terms of the peak simulation errors and peak present time errors, and the fluctuations of the ML water level simulation models (RMSE and NSE) are minor compared to those of the physical mechanism model.

Sensitivity Analysis in Photodynamics: How Does the Electronic Structure Control cis-Stilbene Photodynamics?

The techniques of computational photodynamics are increasingly employed to unravel reaction mechanisms and interpret experiments. However, misinterpretations in nonadiabatic dynamics caused by inaccurate underlying potentials are often difficult to foresee. This work focuses on revealing the systematic errors in the nonadiabatic simulations due to the underlying potentials and suggests a thrifty approach to evaluate the sensitivity of the simulations to the potential. This issue is exemplified in the photochemistry of cis-stilbene, where similar experimental outcomes have been differently interpreted based on the electronic structure methods supporting nonadiabatic dynamics. We examine the predictions of cis-stilbene photochemistry using trajectory surface hopping methods coupled with various electronic structure methods (OM3-MRCISD, SA2-CASSCF, XMS-SA2-CASPT2, and XMS-SA3-CASPT2) and assess their ability to interpret experimental observations. While the excited-state lifetimes and calculated photoelectron spectra show consistency with experiments, the reaction quantum yields vary significantly: either completely suppressing cyclization or isomerization. Intriguingly, analyzing stationary points on the potential energy surface does not hint at any major discrepancy, making the electronic structure methods seemingly reliable when treated separately. We show that performing an ensemble of simulations with different potentials provides an estimate of the electronic structure sensitivity. However, this ensemble approach is costly. Thus, we propose running nonadiabatic simulations with an external bias at a resource-efficient underlying potential (semiempirical or machine-learned) for the sensitivity analysis. We demonstrate this approach using a semiempirical OM3-MRCISD method with a harmonic bias toward cyclization.

Development of a Machine Learning Tool to Predict Deep Inspiration Breath Hold Requirement for Locoregional Right-Sided Breast Radiation Therapy Patients.

&#xD;This study presents machine learning (ML) models that predict if deep inspiration breath hold (DIBH) is needed based on lung dose in right-sided breast cancer patients during the initial computed tomography (CT) appointment. &#xD;Materials and methods. &#xD;Anatomic distances were extracted from a single-institution dataset of free breathing (FB) CT scans from locoregional right-sided breast cancer patients. Models were developed using combinations of anatomic distances and ML classification algorithms (gradient boosting, k-nearest neighbors, logistic regression, random forest, and support vector machine) and optimized over 100 iterations using stratified 5-fold cross-validation. Models were grouped by the number of anatomic distances used during development; those with the highest validation accuracy were selected as final models. Final models were compared based on their predictive ability, measurement collection efficiency, and robustness to simulated user error during measurement collection. &#xD;Results. &#xD;This retrospective study included 238 patients treated between 2016 and 2021. Model development ended once eight anatomic distances were included, and the validation accuracy plateaued. The best performing model used logistic regression with four anatomic distances achieving 80.5% average testing accuracy, with minimal false negatives and positives (< 27%). The anatomic distances required for prediction were collected within 3 minutes and were robust to simulated user error during measurement collection, changing accuracy by < 5%. &#xD;Conclusion. &#xD;Our logistic regression model using four anatomic distances provided the best balance between efficiency, robustness, and ability to predict if DIBH was needed for locoregional right-sided breast cancer patients.&#xD.

Evaluation of the Duration of Albedo Measurement Campaigns

Bifacial PV modules are increasingly used in commercial solar PV projects. With the use of the solar energy available at the module’s rear surface, the correct estimation of the albedo during the pre-construction phase of commercial projects becomes more important. The intention of the study is to determine the impact of the duration of a short-term albedo measurement campaign to reduce the uncertainty with respect to satellite albedo, thus providing a ‘beneficial campaign’. Albedo measurements from seven sites in the USA have been used to estimate the impact of 1-day and 7-day measurements (short-term measurements) on the simulated annual electrical energy production. Simulations based on monthly albedo data obtained from a recognised satellite provider have been compared with simulations where the albedo has been corrected based on short-term measurements. The study found that a 1-day campaign is often not beneficial, causing an increase in uncertainty with respect to satellite albedo. Measurement campaigns in winter do not generally reduce the error of the annual electrical energy production simulation, however, a 7-day campaign undertaken in summer appears to often reduce the uncertainty. It should be noted that the climate conditions, ground vegetation and measurement setup of the analysed seven sites in the USA may not be representative for other sites in the rest of the world.

The impact of setup errors on dose distribution in cervical cancer radiotherapy and the margin from CTV to PTV

PurposeThis study calculates the needed margin from clinical target volume (CTV) to planning target volume (PTV) in IMRT for cervical cancer. It also assesses the impact of setup errors on target and organ at risk (OAR) dose distribution.MethodsWe retrospectively analyzed 50 cervical cancer patients who underwent IMRT, with 210 CBCT scans. We calculated the CTV-to-PTV margin and simulated setup errors in the TPS to reassess dose distribution impacts on targets and OAR.ResultsSetup errors in X(anterior–posterior,AP), Y(cranial–caudal,CC), and Z(left–right,LR) directions were (1.4 ± 1.0) mm, (2.3 ± 1.5) mm, and (1.9 ± 1.2) mm, respectively, leading to CTV-to-PTV margins of 4.4 mm, 6.4 mm, and 5.8 mm. X-axis errors did not significantly affect target dosimetry (P > 0.05), but Y and Z errors did (P < 0.05). X-axis errors impacted the small intestine and rectum (P < 0.05), Y-axis errors mainly affected the colon (P < 0.05), and Z-axis errors affected the colon, small intestine, and rectum (P < 0.05).ConclusionOur study underscores the need to account for setup errors in radiotherapy for cervical cancer. Customizing the CTV-to-PTV margin based on institutional error data is key to maintaining target dose coverage and optimizing treatment outcomes.

CFD simulation and Grid study of Flying Wing Standard Model

Abstract In order to study the influence of mesh size variation on the numerical simulation of typical flying wing model, based on the verification and validation of NNW-FlowStar software, the numerical simulation of flying wing model with multiple sets of unstructured grids with succession variation in Ma=0.9 is carried out, and the lift coefficient, drag coefficient and pitching moment coefficient under corresponding conditions are obtained. Results show that as the height of the first layer decreases, lift coefficient is gradually approaching converged. When the height of the first layer reaches 0.04, the simulation error is already within 1%.

Development of an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle.

To enhance meteorological detection methods, an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle (UAV) was proposed. Computational fluid dynamics (CFD) was employed to model the surrounding airflow distribution during UAV hovering, thereby determining the optimal positions for sensor installation. A novel radiation shield was designed for the temperature sensor, offering both excellent radiation shielding and superior ventilation. To further improve temperature measurement accuracy, an error correction model based on CFD and neural network algorithms was designed. CFD was used to quantify the temperature measurement errors of the sensor under different environmental conditions. Subsequently, random forest and multilayer perceptron algorithms were employed to train and learn from the simulated temperature errors, resulting in the development of the error correction model. To validate the accuracy of the detection system, comparative experiments were conducted using the measurement values from the 076B temperature observation instrument as a reference. The experimental results indicate that the mean absolute error, root mean square error, and correlation coefficient between the experimental temperature errors and the algorithm-predicted errors are 0.055, 0.066, and 0.971 °C, respectively. The average error of the corrected temperature data is 0.05 °C, which shows substantial agreement with the reference temperature data. During UAV hovering, the average discrepancies between the temperature, humidity, and air pressure data of the detection system and the ground-based reference data are 0.6 °C, 1.6% RH, and 0.77 hPa, respectively.

Sustainable illumination: experimental and simulation analysis of illumination for workers wellbeing in the workplace

Incorporating an illumination optimization plan into the worker safety management process in industrials is essential since the quality of workplace illumination has a significant impact on workers' well-being. The purpose of this study was to optimize illumination conditions in industrial workspaces using light-emitting diode (LED) technology with a color temperature of 6500 K and a CRI of 85%. To accomplish this, the DIALux illumination simulation tool was utilized. It was validated using measured illumination data from a lux meter (ST-1300 model 1308). Four factors—the number of luminaires, the effective height of the luminaires, the illumination technology, and the light loss factor—were applied to configure sixteen simulation scenarios. Subsequently, eleven quadratic polynomial equations were proposed for estimating the changes in the average illumination in eleven workplaces using the multiple regression analysis technique on the simulatively prepared dataset. The final step was assessing the desirability degree of the suitable illumination scenario. It was discovered that the DIALux software's illumination simulation error percentage fell within an acceptable range. The illumination technology factor had the greatest impact on the average illumination of workplaces. The most impactful interaction terms of the factors were illumination technology and the number of luminaires. For every workplace, the optimized illumination's desirability degree was expected to be more than 0.9. The proposed approach yields promising results and can be a valuable tool for improving the illumination conditions of workers in industries.

Splitting model and rapid simulation for large-scale natural gas pipeline networks

Numerical discrete methods must typically solve high-dimensional nonlinear partial differential equations when simulating large-scale natural gas pipeline networks. This leads to a sharp increase in computational complexity, resulting in reduced simulation speed. In response to these issues, this study focused on the development of a splitting model and rapid simulation techniques for large-scale pipeline networks. A novel simulation method named linearized lumped parameter model (LLPM) was proposed; by using Lanczos integrate and Taylor expansion methods, the LLPM considers the inertial and gravity terms. Combining with the linearized finite difference method, a simulation technique was constructed, in which the pipeline network is split into an independent node model and pipeline models. Additionally, the boundary conditions of each pipeline are decoupled from the network. This method not only accelerates the simulation efficiency by breaking down the high-dimensional network model into low-dimensional node and pipeline models, but also provides detailed parameter profiles along the pipeline, thus overcoming the limitations of traditional lumped parameter methods. Finally, to validate the proposed method, an actual offshore pipeline network with a total length of 930.36 km was simulated. The results reveal that, compared with the measurement data, the simulation error of the proposed method is 0.92%, and the calculation speed is 132.16 times faster than that of the discrete method. The findings of this study provide a valuable reference for the fast and accurate simulation of large-scale natural gas pipeline networks.

A robust seismic wavefield modeling method based on minimizing spatial simulation error using L2-norm cost function

To reduce the spatial simulation error generated by the finite difference method, previous researchers compute the optimal finite-difference weights always by minimizing the error of spatial dispersion relation. However, we prove that the spatial simulation error of the finite difference method is associated with the dot product of the spatial dispersion relation of the finite-difference weights and the spectrum of the seismic wavefield. Based on the dot product relation, we construct a norm cost function to minimize spatial simulation error. For solving this optimization problem, the seismic wavefield information in wavenumber region is necessary. Nevertheless, the seismic wavefield is generally obtained by costly forward modeling techniques. To reduce the computational cost, we substitute the spectrum of the seismic wavelet for the spectrum of the seismic wavefield, as the seismic wavelet plays a key role in determining the seismic wavefield. In solving the optimization problem, we design an exhaustive search method to obtain the solution of the norm optimization problem. After solving the optimization problem, we are able to achieve the finite-difference weights that minimize spatial simulation error. In theoretical error analyses, the finite-difference weights from the proposed method can output more accurate simulation results compared to those from previous optimization algorithms. Furthermore, we validate our method through numerical tests with synthetic models, which encompass homogenous/inhomogeneous media as well as isotropic and anisotropic media.

A regression-based parametric model for radiative flux density distribution considering shadowing and blocking effects

In solar power tower system, the Radiative Flux Density Distribution (RFDD) on the receiver surface reflected by a heliostat is influenced by various factors, referred to as scene parameters. The previous analytical models simplify the complex optical modeling process, thus neglecting the comprehensive impacts of the scene parameters, resulting in simulation errors. In this paper, a regression-based parametric model, namely Neural Elliptical Gaussian (NEG), is proposed to address this issue. The NEG model comprehensively considers the impacts of various scene parameters on the RFDD, including heliostat size, slant distance of heliostat, incident angle of sunlight, sunlight distribution parameter, slope error, etc. The relationship between the scene parameters and the RFDD is established using a neural network. Additionally, the overlooked shadowing and blocking effects in the conventional analytical models and data-driven methods are addressed by introducing the flux spot centroid offset in the NEG model. Since the NEG model is established based on statistical regression using a sampled and more accurate flux spot dataset, it shows more accurate flux spot prediction ability. Experimental results show that, for most scenarios, the root mean squared error is less than 0.35%, and the total energy error and peak value error are less than 5%.

Development of a novel power generation model for bifacial photovoltaic modules based on dynamic bifaciality

The bifaciality is significantly affected by the irradiance intensity and non-uniformity of rear irradiance (NUF). Therefore, it would result in large errors with a static bifaciality when simulating the dynamic power generation of bifacial photovoltaic (bPV) modules. In the study, a novel dynamic bifaciality was proposed, considering the above two factors simultaneously. Firstly, the fundamental bifaciality under different irradiance intensities was obtained via current–voltage (I-V) characteristic tests. Secondly, a three-dimensional (3D) view factor model was established to study the effects of the installation parameters, solar irradiance, and solar position on NUF. A regression model was built up to predict NUF with a goodness of fit (R2) of 0.914. Thirdly, an electrical model for bPV modules was established considering the non-uniform distribution of rear irradiance. A bifaciality correction factor (CF) was proposed to quantify the influence of NUF on bifaciality. Moreover, an empirical formula between CF and NUF was fitted with a R2 of 0.98. Finally, the dynamic bifaciality was obtained by correcting the fundamental bifaciality with CF, based on which a novel power generation model of bPV modules was developed. Compared to the outdoor experiments, it was found that when NUF was 14.6%, the daily mean relative errors (MAEs) of the novel model and the traditional method were 1.14% and 6.07%, respectively. When NUF was 23.2%, the daily MAEs were 1.40% and 8.10%, respectively. This indicates that under different conditions, the novel model could reduce the relative error of bPV power simulation by about 80% compared to the traditional method.