Simulation Parameters Research Articles

Modeling Method of Corn Kernel Based on Discrete Element Method and Its Experimental Study

Due to the complex shape of corn kernels and the large difference in size, in order to establish a more realistic model of corn kernels, this paper tests and analyzes two representative corn varieties planted in northern China, and each variety is divided into high and low moisture content as the research object. According to its natural shape, the corn kernels are subdivided into four categories: wide horse-tooth shape, narrow-horse tooth shape, pyramid shape and spherical shape. The geometric parameters are tested and analyzed, and the correlation between the characteristic dimensions is obtained. Through the Rocky software, the polyhedral modeling method was used to establish the corn kernel models of different shapes, and the contact parameters and mechanical parameters were measured and calibrated, which was used as the basis for the modeling of corn kernel groups. Finally, through the simulation analysis of the corn kernel accumulation process and the comparison of the test results, the influence of different contact parameters on the simulation test results is explored, and the influence trend of the simulation results is analyzed. The feasibility and accuracy of the corn kernel population model established by the polyhedron method used in this paper are verified, which provides a reference for studying the simulation parameters of corn kernel population modeling.

Design and experiment of facility elevated planting strawberry continuous picking manipulator

Strawberry picking manipulator is one of the key factors affecting the effectiveness of robot picking operations, elevated strawberries cultivated off the ground with hanging fruits provide convenient conditions for automatic strawberry picking. In order to adapt to the small space of elevated strawberry picking operation and to solve the problem of discontinuous picking process, a P-P-R-P type continuous strawberry picking manipulator was proposed. Based on the elevated cultivation mode and the clamping-shear picking method, the structural parameters and control system of the continuous picking manipulator were determined. The kinematics model of the continuous picking manipulator was established, the Jacobian condition number and performance of each joint in the picking operation were solved, and its flexibility and operability were analyzed. According to the growth characteristics of elevated strawberries, the picking simulation routes and operation cycles were set. Using MATLAB software, the simulation tests were carried out to analyze the working space of the continuous picking manipulator, the maximum speed and acceleration changes of each joint. In order to improve the success rate of strawberry picking, the operating parameters such as rotational speed, end picking angle and clamping position of the continuous picking manipulator were optimized using Box-Behnken test. Based on the simulation results and parameter optimization results, a continuous picking manipulator and motion control system were constructed and validation tests were conducted. The results showed that the average picking success rate of the continuous picking manipulator was 74.21 %, and the average picking efficiency was 12.87 s per piece. In conclusion, the P-P-R-P type continuous picking manipulator proposed in this study meets the automated operation requirements of strawberry picking and can be adapted to the elevated operating environment.

Computational Investigation of the High-Temperature Chemistry of Small Hydroxy Ethers: Methoxymethanol, Ethoxymethanol, and 2-Methoxyethanol.

In the search for alternative energy carriers that can replace conventional fossil fuels, sustainably produced oxygenated hydrocarbons represent a promising class of potential candidates. An illustrative member of this class of alternative biofuels are oxymethylene ethers (OMEs). This study makes a contribution to this objective by investigating hydroxy ethers, specifically methoxymethanol, ethoxymethanol, and 2-methoxyethanol. These bifunctional oxygenated molecules are relevant intermediates formed during the combustion of OMEs or via an equilibrium reaction in methanol and formaldehyde mixtures. The high-temperature chemistry of hydroxy ethers is examined, with a particular focus on the unimolecular reactions involving H atom migration and bond fission. Quantum chemical calculations are utilized to gain insights into these processes. The results include bond dissociation energies, one-dimensional representations of the potential energy surfaces along the reaction coordinate for the simulated reactions, thermodynamic properties, and reaction rate parameters, which were derived via established ab initio methods. A detailed account of the unimolecular decomposition reactions of methoxymethanol, ethoxymethanol, and 2-methoxyethanol is provided, including the predominant reaction rates and pressure dependencies. The BDEs are benchmarked against data from different literature sources. For ME, a direct comparison of BDEs and reaction constants is performed with the results of previous studies. The derived reaction parameters for all three hydroxy ethers are compared to the results obtained via theoretical methods for dimethoxymethane and diethoxymethane, which feature similar chemical structures. The high-temperature chemistry of methoxymethanol and ethoxymethanol shares similarities and is dominated by rapid H atom migrations, forming alcohol + formaldehyde. This reaction is geometrically constrained in 2-methoxyethanol and therefore bond fission reactions dominate. A brief systematic comparison of the bifunctional oxygenated structure of hydroxy ether with other fuel species, such as n-alkanes, ethers, and alcohols, was conducted. The findings of this study are supported by the results of published chemical kinetic models of 2-methoxyethanol, formaldehyde + methanol, and OME-2. This includes simulations of chemical kinetic mechanisms, which were updated with the newly derived reaction rates, and compared with associated experimental data from literature. These experiments are concentration measurements obtained from a jet-stirred reactor and laminar burning velocities measured in an atmospheric burner. The results are in reasonable agreement with the reported experiments and thus may be considered an update to the models. The updated reaction rate constants showed no observable impact on the ignition delay times of OME-2. However, they did result in a significant increase in the concentration of methoxymethanol as an intermediate. In conclusion, this study provides crucial insights into the high-temperature combustion properties of hydroxy ethers. The data presented is intended to enhance comprehension of the decomposition behavior of these bifunctional oxygenated species and to support the development of detailed chemical kinetic models for combustion applications.

FRACTAL-BASED MODELING OF RAIL ROUGHNESS

This paper uses fractal modeling techniques to effectively depict the roughness characteristics of the rails examined in this study by means of both the structure function and the Weierstrass–Mandelbrot function to capture the intricate nature of the rail roughness. Experimental analysis of the roughness of the railway rails from Faurei Railway Testing Center (RTC Faurei) in Romania proves that the roughness height exhibits distinct mathematical fractal characteristics. The study evaluated and compared 41 classical statistical parameters derived from roughness measurements with simulated fractal parameters. The classical roughness parameters, including the Autocorrelation function, Amplitude Density Function, as well as Bearing Area Curves, and rail acoustic roughness, were determined from a profile obtained using the Weierstrass function and compared to the measured ones, revealing a noticeable congruence between the generated charts. The research findings indicate a strong convergence between experimental measurements and simulated profiles, with most parameters falling within a 10% relative error range. This aspect highlights the approach of fractal potent in assessing rail roughness behavior. Consequently, the simulated parameters could potentially analyze rail roughness quality to maintain and track grinding and mitigate the rolling noise.

Numerical prediction of impact damage in thick fabric composite laminates

A simulation methodology for assessing the damage in thick fabric Carbon Fibre Reinforced Polymer (CFRP) composite laminates under low- and high-velocity impacts is presented. It encompasses steps for calibration, verification, and validation of the elastic and fracture material properties as well as determination of model parameters for the numerical simulations. Damage is modelled using a discrete fracture approach with cohesive interface elements that capture individual cracks occurring in and between plies. For computational efficiency, the method is implemented in a two-dimensional (2D) axi-symmetric model. Results from double-cantilever beam, end-notched flexure, and quasi-static indentation experiments align well with numerical simulations and serve to calibrate and verify the implementation of the discrete fracture approach. The methodology is extended to dynamic impact analysis to predict damage mechanisms, force–displacement histories, and is validated using test results. This methodology combines meaningful insight in the failure mechanisms with a manageable computational effort, achieving a factor 50 improvement compared to a benchmark. A parametric analysis summarised in failure maps relates damage mechanisms to impact energy, mass, and laminate thickness. The proposed methodology strikes a balance between computational efficiency and accuracy, making it a valuable tool for optimum design and certification of thick CFRP composite laminates under impact.

CFD investigations of a shape-memory polymer foam-based endovascular embolization device for the treatment of intracranial aneurysms.

The hemodynamic and convective heat transfer effects of a patient-specific endovascular therapeutic agent based on shape-memory polymer foam (SMPf) are evaluated using computational fluid dynamics studies for six patient-specific aneurysm geometries. The SMPf device is modeled as a continuous porous medium with full expansion for the flow studies and with various degrees of expansion for the heat transfer studies. The flow simulation parameters were qualitatively validated based on the existing literature. Further, a mesh independence study was conducted to verify an optimal cell size and reduce the computational costs. For convective heat transfer, a worst-case scenario is evaluated where the minimum volumetric flow rate is applied alongside the zero-flux boundary conditions. In the flow simulations, we found a reduction of the average intra-aneurysmal flow of > 85% and a reduction of the maximum intra-aneurysmal flow of > 45% for all presented geometries. These findings were compared with the literature on numerical simulations of hemodynamic and heat transfer of SMPf devices. The results obtained from this study provide a novel and practical framework for optimizing the design of patient-specific SMPf devices, integrating advanced computational models of hemodynamics and heat transfer. This framework could guide the future development of personalized endovascular embolization solutions for intracranial aneurysms with improved therapeutic outcome.

(Invited) Finite Element Modeling of Atmospheric Corrosion: Boundary Conditions, Model Fidelities, and Computational Advances with Machine Learning

Corrosion is a detrimental materials degradation mechanism to many in-service systems, especially in marine environments. Due to the large array of influential variables, atmospheric corrosion can be expensive (time, materials, replications, etc.) to evaluate experimentally. To reduce these costs, Finite Element Method (FEM) simulations can be used to assess the rate and extent of corrosion. While a useful tool, complexities in FEM can substantially influence computational time and accuracy of model outputs. These complexities include experimental variation in boundary conditions to various levels of physics fidelities. Herein, the importance of boundary conditions is explored: identifying critical parameters for accurate simulations in atmospheric scenarios and weighing the computational cost and accuracy of high and low fidelity models.Further, accelerations of FEM predictions have recently been achieved to decrease computational time to make predictions, identify additional experiments where error reduction is possible, and make high fidelity predictions for a wide parameter space at a fraction of the cost. The efficacy of this protocol will be showcased for galvanic corrosion minimizing the number of simulations necessary to obtain accurate predictions of the current and damage outputs. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions. Acknowledgements SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. This document is SAND 2024-03970C.

Simulation of peak properties in thermoluminescence dosimeters with the potential stimulation of all electron traps

In thermoluminescence (TL) models, the competition between energy levels for electron trapping is a direct consequence of conduction band mediation in electron transport. From a theoretical perspective, these competitive effects are considered responsible for many TL phenomena. Furthermore, while they are easily detectable in simulation studies, there are no clear and reliable criteria for their experimental verification. In the present work, it is suggested that in certain materials with narrow band gap energies, existing electron traps can be accessed by thermal stimulation up to 500 °C. It is possible to predict properties through simulation that can then be verified experimentally. The simulation is applied to a complex TL glow curve consisting of four electron trapping levels and one luminescence center. With the appropriate selection of simulation parameter values, the competition is limited solely to electron transport, rather than to significant differences in parameter values. The simulation indicated that the last peak serves as the primary competitor. Furthermore, the shape of the TL glow curve depends on the strength of the competition. When the competitor is strong, the lower temperature peaks behave as first-order peaks, and the shape of the glow curve remains stable as a function of dose. The last peak, which acts as the final competitor, exhibits behavior similar to that of the peak derived from the one trap, one recombination (OTOR) model. This OTOR-like behavior provides an excellent experimental test for the simulation results and, consequently, for the model used. The validity of the simulation was assessed by comparing the input values of kinetic parameters with the output values obtained from computerized glow curve deconvolution (CGCD) analysis for each TL glow curve.

Transport Mechanism of Hydroxide Ions Focused on the Vehicle Mechanism Near the Cation Sites in Anion Exchange Membranes

Recently, interest in hydrogen usage and production has surged to achieve a sustainable society, with fuel cells and electrolyzers coming into focus. Fuel cells exhaust only water, offering an eco-friendly alternative. Electrolyzers, utilizing renewable sources like solar and wind, generate hydrogen from water, reducing environmental impact. We explore anion exchange membranes (AEM) used in the fuel cells and electrolyzers. While proton exchange membranes (PEM) are currently more common, AEMs offer advantages such as cost reduction through non-precious metal catalysts, operations with less corrosion, and a variety of fuel options, potentially expanding applications. However, a major performance degradation cause in AEMs is their inferior ion transport properties. Enhancing AEM ion conductivity, particularly by understanding the hydroxide ion transport mechanism, remains crucial. Thus, we aim to clarify the hydroxide ion transport mechanism in AEMs.In AEMs under low hydration conditions, the vehicular mechanism generally prevails over the Grotthuss mechanism. Recent studies on AEM materials with rigid side chains have shown that the proximity and rigidity of cationic sites cause overlapping of cationic solvation shells, suggesting an increased contribution of the vehicular mechanism (1). Specifically, hydroxide ions are transported via the vehicular mechanism along with surrounding water molecules, implying that the overlapping of cationic solvation shells facilitates smooth movement of hydroxide ions around the cationic sites within AEMs. These insights into the importance of vehicular mechanism prompt our study to use classical molecular dynamics simulations to clarify its impact on hydroxide ion transport in AEMs.We focus on the QPAF-4 polymer (2) distinguished by its high hydroxide ion conductivity and stability under alkaline conditions, in simulations conducted on LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). The simulation cell is composed of 35 QPAF-4 polymers, featuring 210 cationic groups (NH3 +), and 210 hydroxide ions to maintain charge balance. The number of water molecule depends on hydration level (λ), the ratio of water molecule to cationic group. Simulation parameters include a 1.0 fs timestep, three-dimensional periodic boundary, sampling from 1.0×103 to 1.0×107 steps, 10 ns simulation time, temps of 300 K and 353 K, and λ = 3, 4, 6, 9, 12.This study reveals the relationship between hydration level (λ) and equilibrium density in QPAF-4 polymers. As λ increases, the equilibrium density increases until λ = 5, beyond which it decreases. This behavior aligns with phenomena observed in previous research using AEM materials with quaternary ammonium cationic groups (1). In earlier studies, under low λ conditions, polymer voids filled with water, leading to increase density, whereas at higher λ, polymer water absorption and expansion cause a decrease in density. These findings suggest that a similar phenomenon may occur in QPAF-4 polymers.RDF analysis at 300 K evaluated distances between nitrogen atoms of cationic groups of QPAF-4 polymer and hydroxide ion oxygen atoms (gN-Oh(r)), depicted in Fig.1(a), and water molecule oxygen atoms (gN–Ow(r)), illustrated in Fig.1(b), under varying λ conditions. First peaks of gN–Oh(r) and gN–Ow(r) existed at approximately 3.4 Å, with first minima at approximately 4.7 Å and 5.3 Å, respectively, defining the first solvation shell. The greater first minimum value for gN–Ow(r) suggests hydroxide ion transport within the first solvation shell of cation group. The ratio of the number of hydroxide ions and water molecules in the first solvation shell of a cationic group to the total number of these molecules in the system are 96.4 % and 87.6 %, respectively. The analysis of distances between cationic group nitrogen atoms (gN–N(r)), illustrated in Fig.1(c), under each λ condition indicates a structural distance of 6.0 Å at λ = 6. Given that the radius of the first solvation shells is approximately 5.3 Å, this implies overlapping first solvation shells around cationic groups, suggesting continuous solvation shell formation. As λ increases, cationic group distances expand, but distances to water and hydroxide ions stay constant, underscoring the need to study the impact of reduced overlapping regions for ion transport.Future research will explore ion transport in AEMs under different λ and temperatures to find optimal conditions for hydroxide ion transport. This could aid in developing new AEM materials. The study emphasizes the essential hydroxide ion transport mechanism for electrolyte membrane performance.References(1)Chen, Chen, et al. "Hydroxide Solvation and Transport in Anion Exchange Membranes," Journal of the American Chemical Society, 2016, pp. 991-1000.(2)Ono, Hideaki, et al. "Robust anion conductive polymers containing perfluoroalkylene and pendant ammonium groups for high performance fuel cells." Journal of Materials Chemistry A, 2017, pp. 24804–24812. Figure 1

An automated toolbox for microcalcification cluster modeling for mammographic imaging.

Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications. Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks. The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre-set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three-dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two-dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast-calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images. The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings. This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures.

Aerodynamics evaluation and flight test of a vertical take-off and landing fixed-wing UAV with joined-wing configuration in transition flight state

The transition states between the cruising flight and the taking-off/landing process of a vertical take-off and landing (VTOL) fixed-wing UAV of joined-wing configuration are subject to significant unsteady aerodynamic interference. In this paper, the aerodynamic characteristics of this VTOL UAV during transition flights are evaluated by CFD with and without crosswind interference, in order to reveal the underlying mechanisms of the transition process. Based on these CFD-derived parameters, a flight test with a specifically-designed joined-wing VTOL UAV is proposed. The obtained results demonstrate that the forward flight speed is a crucial parameter during the takeoff transition phase. In the time interval of 5–15 s, significant disturbances are observed in the forces and moments due to the rotor deceleration and forward propeller acceleration, which result in slipstream and downwash flow effects. When crosswind disturbance is added, significant roll and yaw moments arise due to the vast vertical stabilizer area, which requires coordinated attitude adjustments between the rotor and the fixed-wing rudder surface. The descent transition phase is set a duration of 10 s. Four seconds later, as the rotor downwash flow intensifies, the lift force of the fixed wing is transferred to the rotor. When the combined lift becomes insufficient, the flight altitude decreases. When introducing crosswind disturbance, the entire aircraft undergoes significant additional pitch, yaw, and roll moments, with a maximum wave momentum greater than 200 %. Flight tests are then conducted using simulated parameters. The obtained results show that the take-off and landing transition responses without crosswinds are consistent with the predicted outcomes, which demonstrates the high effectiveness of the CFD simulations in predicting these transitions.

Effect of plantar fascia stiffness on plantar windlass mechanism and arch: Finite element method and dual fluoroscopic imaging system verification

This study explored the relationship between the foot arch stiffness and windlass mechanism, focusing on the contribution of the posterior transverse arch. Understanding the changing characteristics of foot stiffness is critical for providing a scientific basis for treating foot-related diseases. Based on a healthy male's computed tomography, kinematic, and dynamics data, a foot musculoskeletal finite element model with a dorsiflexion angle of 30°of metatarsophalangeal joint was established. Analyze the changes in stress distribution of the plantar fascia, metatarsophalangeal joint angle, arch height, and length during barefoot walking as the stiffness of the plantar fascia varies from 25 % to 200 %. For validation, the simulated arch parameters were compared with the dual fluorescence imaging system measurements. The width of transverse arch, height, and length of longitudinal arch measured by the dual fluorescence imaging system were 45.14 ± 1.63 mm, 29.29 ± 1.57 mm, and 155.16 ± 2.69 mm, respectively. The results of the simulation were 46.51 mm, 29.96 mm, and 156.71 mm, respectively. With the increase of plantar fascia stiffness, the effect of the windlass mechanism increased, the flexion angle of the metatarsophalangeal joint decreased, the distal stress of plantar fascia decreased gradually, while the proximal and middle stress increased, the transverse arch angle increased, but when the plantar fascia stiffness exceeds 150 %, the transverse arch angle decreases. The increase of plantar fascia stiffness will increase the effect of the windlass mechanism but decrease the flexion angle of the metatarsophalangeal joint. The stiffness of the plantar fascia influences the behavior of the plantar fascia. The plantar fascia stiffness affects the distal tension of the plantar fascia by affecting the flexion of the metatarsophalangeal joint in the plantar windlass mechanism. It affects the stiffness of the transverse arch of the foot together with the ground reaction force acting on the distal metatarsal.

A stochastic population model for the impact of cancer cell dormancy on therapy success

Therapy evasion – and subsequent disease progression – is a major challenge in current oncology. An important role in this context seems to be played by various forms of cancer cell dormancy. For example, therapy-induced dormancy, over short timescales, can create serious obstacles to aggressive treatment approaches such as chemotherapy, and long-term dormancy may lead to relapses and metastases even many years after an initially successful treatment.In this paper, we focus on individual cancer cells switching into and out of a dormant state both spontaneously as well as in response to treatment. We introduce an idealized mathematical model, based on stochastic agent-based interactions, for the dynamics of cancer cell populations involving individual short-term dormancy, and allow for a range of (multi-drug) therapy protocols. Our analysis – based on simulations of the many-particle limit – shows that in our model, depending on the specific underlying dormancy mechanism, even a small initial population (of explicitly quantifiable size) of dormant cells can lead to therapy failure under classical single-drug treatments that would successfully eradicate the tumour in the absence of dormancy. We further investigate and quantify the effectiveness of several multi-drug regimes (manipulating dormant cancer cells in specific ways, including increasing or decreasing resuscitation rates or targeting dormant cells directly). Relying on quantitative results for concrete simulation parameters, we provide some general basic rules for the design of (multi-)drug treatment protocols depending on the types and processes of dormancy mechanisms present in the population.

Multi-objective optimization prediction model for building parameters of photovoltaic windows based on NSGA II-BP

This article simulates the indoor useful daylight illuminance (UDI), energy consumption, and power generation of photovoltaic (PV) window buildings using EnergyPlus simulation software. Extensive data on these simulation parameters are obtained using parametric simulation software and combined with actual meteorological data. The factors significantly influencing PV window building performance are determined based on ANOVA. A model is developed to predict energy consumption, power generation, and UDI of PV window buildings using a back propagation neural network. For better lighting quality, lower energy consumption, and greater power generation, NSGA-II is introduced to optimize the windows' performance with multi-objective parameters. Moreover, the resulting energy saving rate, annual average power generation growth rate, and UDI growth rate are compared with the initial values to evaluate the effectiveness of the optimal solution. The results demonstrate that the energy saving rate of the building is 18.23 %, and the growth rates of the useful daylight illuminance and power generation reach 41.6 % and 5.12 %, respectively, compared to the initial values.

Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol.

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but the agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables to address the sampling problem. We apply this protocol to 37 host-guest systems. The simplicity of setting up the simulations and producing well-converged binding free energy estimates without the need to optimize simulation parameters provides a reliable solution to the sampling problem. This, in turn, allows for a systematic force field comparison and ranking according to the correlation between simulations and experiments, which can inform the selection of an appropriate model. The protocol can be readily adapted to test more force field combinations and study more complex protein-ligand systems, where the choice of an appropriate physical model is often based on heuristic considerations rather than systematic optimization.

Simulating Real-Time Molecular Electron Dynamics Efficiently Using the Time-Dependent Density Matrix Renormalization Group.

Compared to ground-state electronic structure optimizations, accurate simulations of molecular real-time electron dynamics are usually much more difficult to perform. To simulate electron dynamics, the time-dependent density matrix renormalization group (TDDMRG) has been shown to offer an attractive compromise between accuracy and cost. However, many simulation parameters significantly affect the quality and efficiency of a TDDMRG simulation. So far, it is unclear whether common wisdom from ground-state DMRG carries over to the TDDMRG, and a guideline on how to choose these parameters is missing. Here, in order to establish such a guideline, we investigate the convergence behavior of the main TDDMRG simulation parameters, such as time integrator, the choice of orbitals, and the choice of matrix-product-state representation for complex-valued nonsinglet states. In addition, we propose a method to select orbitals that are tailored to optimize the dynamics. Lastly, we showcase the TDDMRG by applying it to charge migration ionization dynamics in furfural, where we reveal a rapid conversion from an ionized state with a σ character to one with a π character within less than a femtosecond.

NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations

Abstract. We introduce NitroNet, a deep learning model for the prediction of tropospheric NO2 profiles from satellite column measurements. NitroNet is a neural network trained on synthetic NO2 profiles from the regional chemistry and transport model WRF-Chem, which was operated on a European domain for the month of May 2019. This WRF-Chem simulation was constrained by in situ and satellite measurements, which were used to optimize important simulation parameters (e.g. the boundary layer scheme). The NitroNet model receives NO2 vertical column densities (VCDs) from the TROPOspheric Monitoring Instrument (TROPOMI) and ancillary variables (meteorology, emissions, etc.) as input, from which it reproduces NO2 concentration profiles. Training of the neural network is conducted on a filtered dataset, meaning that NO2 profiles showing strong disagreement (>20 %) with colocated TROPOMI column measurements are discarded. We present a first evaluation of NitroNet over a variety of geographical and temporal domains (Europe, the US West Coast, India, and China) and different seasons. For this purpose, we validate the NO2 profiles predicted by NitroNet against satellite, in situ, and MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) measurements. The training data were previously validated against the same datasets. During summertime, NitroNet shows small biases and strong correlations with all three datasets: a bias of +6.7 % and R=0.95 for TROPOMI NO2 VCDs, a bias of −10.5 % and R=0.75 for AirBase surface concentrations, and a bias of −34.3 % to +99.6 % with R=0.83–0.99 for MAX-DOAS measurements. In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. During wintertime considerable low biases arise because the summertime/late-spring training data are not fully representative of all atmospheric wintertime characteristics (e.g. longer NO2 lifetimes). Nonetheless, the wintertime performance of NitroNet is surprisingly good and comparable to that of classic regional chemistry and transport models. NitroNet can demonstrably be used outside the geographic and temporal domain of the training data with only slight performance reductions. What makes NitroNet unique when compared to similar existing deep learning models is the inclusion of synthetic model data, which offers important benefits: due to the lack of NO2 profile measurements, models trained on empirical datasets are limited to the prediction of surface concentrations learned from in situ measurements. NitroNet, however, can predict full tropospheric NO2 profiles. Furthermore, in situ measurements of NO2 are known to suffer from biases, often larger than +20 %, due to cross-sensitivities to photooxidants, which other models trained on empirical data inevitably reproduce.

Investigation into the Suitability of AA 6061 and Ti6Al4V as Substitutes for SS 316L Use in the Paraplegic Swivel Mechanism

SS 316L, a low-carbon 316 Stainless Steel, has been used to manufacture swivel mechanisms for paraplegic patients, but its weight is relatively high compared to a few materials in its range of properties. Aluminum alloy 6061 and Titanium alloy (Ti6Al4V) offer lightweight and incredible strength-to-weight ratio, hence their use for medical, aerospace, and automotive applications. This study, therefore, seeks a replacement for SS 316L. A 3D model of a swivel mechanism was developed to compare the performance of the swivel mechanism made with SS 316L, AA 6061, and Ti6Al4V. The kinematic analysis of the mechanism based on a range of weights: 1kN, 1.1 kN, 1.2 kN, 1.3 kN, 1.4 kN, and 1.5 kN was carried out to generate the inputs for the simulation. The 3D model was made with SolidWorks, and the results of the kinematic analysis were used to define the simulation parameters for the mechanism. Two scenarios generated depicted the full collapse of the mechanism and the full extension. The results showed that AA 6061 and Ti6Al4V outperformed SS 316L with higher yield strength and factor of safety. Therefore, swivel plates made with AA 6061 and Ti6Al4V have higher yield strength than those made with SS 316L, adding to the advantage that they have a higher strength-to-weight ratio. From this analysis and known knowledge of the cost of these materials, the optimal replacement considering cost with yield strength is AA 6061. However, Ti6Al4V is a better alternative for the strength-to-weight ratio for SS 316L.

Simulation of single-layer internal short circuit in anode-free batteries

The lithium metal battery technologies that can fulfil the high energy density goal have grave safety concerns and lead to fire/smoke, leading to battery failure. Out of all the causes of fire, internal short circuits (ISC) are the most common. The ISC safety test is considered a crucial checkpoint for battery design, but the present tests, like nail penetration and ball indentation, lack certainty and reproducibility in declaring battery safety. In light of these experimental limitations, we present an experimentally validated ISC simulation method that can elucidate fundamental mechanisms underlying ISC. The experimental/simulation method isolates the shorted single-layer from the unshorted layers, which helps in scrutinizing ISC and thermal runaway (TR) phenomenon. The present ISC model is flexible and computationally inexpensive compared to other 3D electrochemical thermal coupled (ECT) ISC simulations for a whole battery pack. We show the experimental validation of terminal voltage, short-circuit current, shorting resistance, internal temperature and other derived parameters of an ISC simulation of anode-free cell. Finally, the simulation model was used to do a parametric study for an anode-free battery (AFB) and the effect of cell design, and shorting parameters on ISC was scrutinized.

A fuzzy based chicken swarm optimization algorithm for efficient fault node detection in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are built with miniature sensor nodes (SN), which are deployed into the geographical location being sensed to monitor environmental conditions, which transfer the sensed physical information to the base station for further processing. The sensor nodes frequently experience node failure as a result of their hostile deployment and resource limitations. In WSN, node failure can cause a number of issues, namely Wireless Sensor Networks topology changes, broken communications links, disconnected portions of the network, and data transmission errors. An important concern of WSN is the detecting, diagnosing and recovering of sensor node failures. In the course of this effort, an effective strategy for sensor node failure detection algorithm using the Poisson Hidden Markov Model (PHMM) and the Fuzzy-based Chicken Swarm Optimization (F-CSO) is proposed for efficient detection of sensor node faults in the WSN. The proposed work offers optimal false alarm, false positive, energy consumption, detection accuracy, network lifetime, and least delay rates. Moreover, the F-CSO provides improved localization to locate the defective sensor nodes that are present in the WSN. The proposed work is implemented in the NS2 simulator with realistic simulation parameters, and the simulation results demonstrate that the proposed work is more effective in terms of 89.5% fault detection accuracy, 19.53% throughput, 8.43% energy consumption with minimum delay and less false positive rate when it is compared with other existing state-of-art systems.