Simulation Approach Research Articles

Analysis of the Drag Reduction Performance and Rheological Properties of Drag-Reducing Additives.

In the practical application of hydraulic rotating machinery, it is essential to thoroughly explore drag reduction and rheological characteristics of drag-reducing additives to optimize machinery efficiency and reduce equipment consumption. This paper combines simulation and experimental approaches to investigate the drag-reduction performance and rheological properties of drag-reducing additives. Numerical simulations are initially conducted to investigate the shear-thinning properties of drag-reducing fluid and explore variations in drag-reduction rate. Turbulent phenomena characteristics are described by analyzing turbulent statistical quantities. Subsequently, the rheological behaviors of polyethylene oxide (PEO), cetyltrimethyl ammonium chloride (CTAC), and their mixed solutions under different conditions are scrutinized using a rotational rheometer. The findings indicate that the drag reduction effect amplifies as the rheological index n and characteristic time λ decrease. The numerical simulations show a maximum drag reduction rate of 20.18%. In rheological experiments, a three-stage viscosity variation is observed in single drag-reducing additives: shear thickening, shear thinning, and eventual stabilization. Composite drag-reducing additives significantly reduce the apparent viscosity at low shear rates, thereby strengthening the shear resistance of the system.

VDI/VDE 2634-2 and ISO 10360-13 performance evaluation tests, and systematic errors in structured light systems

Structured light systems (SLSs) are commonly used for precision dimensional measurements. These are active systems in that the projected structured light pattern is used to establish correspondence between the projector and the camera(s) to provide more accurate 3D reconstruction as opposed to passive camera-based systems that rely on the user or features in images to identify corresponding points between the cameras. Errors in the calibration of the model parameters of an SLS can lead to systematic errors in dimensional measurements. In this paper, we explore the topic of performance testing of an SLS, specifically, the sensitivity of the length tests described in the VDI/VDE 2634-2 guideline and the ISO 10360-13 standard to the model parameters (such as intrinsic parameters of the camera and the projector, and camera-projector geometry parameters) of an SLS. The results are based on a hybrid approach of simulations performed using data obtained experimentally from an SLS mounted on a Cartesian coordinate measuring machine (CMM) which is used primarily to position the target at different points within the measurement volume. The results show that the length tests described in the ISO 10360-13 standard provide better sensitivity to the model parameters than the VDI/VDE 2634-2 guideline. However, even the ISO 10360-13 length tests do not detect all parameters with high sensitivity. A model-based approach in identifying length tests (i.e., the position and orientation of reference lengths) such as described in this paper provides better sensitivity to model parameters and therefore is more likely to detect systematic errors in SLSs.

Heavy metal pollution and health risk assessment using deterministic and Monte Carlo simulation approaches in the Himalayan spring and surface water systems of Kulgam District, Kashmir valley, India

Heavy metal pollution and health risk assessment using deterministic and Monte Carlo simulation approaches in the Himalayan spring and surface water systems of Kulgam District, Kashmir valley, India

Insights into the dynamic symphony: a comprehensive computational exploration of pure and fluorinated 6O.6 liquid crystals with the help of DFT analysis

ABSTRACT This article is based on the Density Functional Theory (DFT) simulations approach to examine the structural and vibrational behaviour of pure 6O.6 and fluorinated 6O.6 liquid crystalline compounds. The molecular interactions of the molecules were investigated by their optimised molecular structure. Identification of significant functional groups and their dynamic behaviours are made with the help of vibrational frequencies obtained through DFT calculations. Vibrational Energy Distribution Analysis (VEDA) software was used to perform the Potential Energy Distribution (PED) function of FT-IR and Raman spectra. Furthermore, changes in vibrational peaks provide insight into structural modifications. This investigation mainly focused on analysing the different reactivity, thermodynamic parameters and non-linear optical (NLO) properties of the compounds. This also helps to open a new path for further investigations into optoelectronic applications and molecular dynamics.

Numerical simulation approach for consideration of ageing effects in PCB substrates by modifying viscoelastic materials properties

During operating time of electronic systems, the used materials in such devices are potentially subjected to ageing effects, which might limit the lifetime. Therefore, knowledge about the used materials and the way the materials are affected by ageing effects is of key importance to develop reliable products.In this study, a simulation approach is discussed that is able to consider ageing effects caused by oxidation at elevated temperature of a printed circuit board material, typically used for high frequency applications. The material was characterized for its thermomechanical properties with state-of-the-art techniques for different ageing durations. Ageing was accelerated by storing the samples in an oven at 175 °C for up to 1000 h.Within the simulation workflow, the thermomechanical properties of the different aged states are defined by modifying the pristine viscoelastic properties. Four exponential functions are derived modifying the initial modulus, the characteristic time constants, the shift function and the coefficient of thermal expansion, all in dependency of ageing time.To demonstrate the approach, the soldered interconnection lifetime of a theoretical chip-size-package on a printed circuit board is studied. State-of-the-art lifetime predictions of such interconnections only include thermomechanical ageing effects, for example by creep effects of the solder. By additionally considering the ageing of the printed circuit board, thermal ageing is combined with thermomechanical ageing.Results in the soldered interconnection are compared between either considering additional ageing effects of the printed circuit board or neglecting this behavior. Thus it is shown that thermal ageing plays a significant role in the development of accumulated creep strain which becomes increasingly important with increasing expected lifetime.

Molecular dynamics simulation approach to explore mechanical properties of graphene-polyaniline hybrid sheets

The exploration of graphene-polyaniline (C3N) structures holds promise in multifunctional materials, offering tailored mechanical and electrical properties with applications from energy storage to electronic devices. However, their utilization across varying temperatures and the inevitability of defects pose challenges that require investigation to understand their mechanical behavior. Hence, we examined mechanical properties under the influence of increasing temperature, considering different types of defects including, single vacancies, divacancies, and Stone-Wales. Results demonstrate that fracture strength and strain of heterostructure are highly sensitive to increasing the temperature up to 1100 K. Remarkably samples with 35 % SVC exhibit fracture strength of 87 GPa, which is unprecedentedly higher than most of the typical heterostructures. In SVC-defected samples with increasing concentration, the most noticeable reduction in Young's modulus is observed. The results provide an insight into creating a heterostructure that could have desired mechanical properties though imperfect and defective for nanoelectronic devices.

A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations

As the need for handling complex geometries in computational fluid dynamics (CFD) grows, efficient and accurate mesh generation techniques become paramount. This study presents an adaptive mesh refinement (AMR) technology based on cell-based Cartesian grids, employing a distance-weighted least squares interpolation for finite difference discretization and utilizing immersed boundary methods for wall boundaries. This facilitates effective management of both transient and steady flow problems. Validation through supersonic flow over a forward-facing step, subsonic flow around a high Reynolds number NHLP airfoil, and supersonic flow past a sphere demonstrated AMR’s efficacy in capturing essential flow characteristics while wisely refining and coarsening meshes, thus optimizing resource utilization without compromising accuracy. Importantly, AMR simplified the capture of complex flows, obviating manual mesh densification and significantly improving the efficiency and reliability of CFD simulations.

Diamond machining of additively manufactured Ti6Al4V ELI: Newer mode of material removal challenging the current simulation tools

Single point diamond machining (SPDM) produces smooth machined surfaces that other production methods cannot match. While the mechanics of machining of cast alloys with SPDM is well-explored, the realm of SPDM for additively manufactured parts remains largely uncharted. This work reveals new insights into the surface generation process of an additively manufactured titanium alloy, specifically, a Ti6Al4V Extra Low Interstitials (ELI) alloy workpiece. Our examination of the chip morphology unveiled a distinct mode of chip removal, previously unrecorded in existing literature. During SPDM of additively made Ti6Al4V ELI workpiece, identification of numerous pores and discontinuities in the chips flowing on the tool rake face, indicating periodic intermittent cracking during the material's plastic flow was seen. To examine this phenomenon, a finite element analysis (FEA) model was developed. While the FEA model can well explain the machining mechanics and chip morphology of SPDM of cast Ti6Al4V ELI reported in the literature, it failed to describe the chip morphology that are obtained during machining of additively made workpiece in this work. This disparity underscores the need for innovative simulation approaches tailored for additively manufactured components. The experimental observations in this study highlight a unique form of chip formation in contrast to conventional Ti6Al4V alloy machining processes. At lower feeds, there was a presence of short, discontinuous chip formation with tearing at the outer periphery. Conversely, at higher feeds, a long, continuous ribbon-like chip formation was observed. In addition, some typical additive manufacturing defects appear on the machined surface and chips. Through optimisation of the SPDT parameters, a surface roughness (Ra) value of about 11.8 nm was achieved on additively manufactured Ti6Al4V ELI workpiece. This work provides a fresh perspective on the mechanics of SPDM for additively manufactured components, offering a stepping stone for subsequent studies.

A semi-analytical simulation method for bi-directional functionally graded cantilever beams under arbitrary static loads

This paper presents a novel semi-analytical simulation approach for analysing the behaviour of bi-directional functionally graded cantilever beams subjected to arbitrary static loads, such as concentrated moments, concentrated forces, distributed force and their combinations applied at any location along the beam. The fundamental equations governing the cantilever beam’s response are derived, on the basis of which the proposed semi-analytical method is implemented using MATLAB programming language. The simulation results include field variables as well as stress contours, providing a compressive understanding of the beam’s behaviour. To validate the accuracy and reliability of the proposed method, a convergence study is conducted in comparison with the graded finite element method (GFEM) and analytical solutions. In the end, the developed method is applied to simulate the bending behaviour of bi-directional functionally graded cantilever beams under various loads individually and their combinations. The stress contours and deflection curves obtained from the simulation are compared with the solutions obtained using GFEM, revealing that the developed method possesses excellent capability in accurately simulating the bending behaviour of cantilever beams.

Application of semi-direct fuel injection system to free piston engine generator for better performance: Simulation approach with validation results

This paper explores methods to improve the fuel economy of a two-stroke free piston engine generator (FPEG) with no additional cost. In order to solve the problem of low fuel capture and poor fuel economy of two-stroke FPEG using port fuel injection (PFI), the fuel injection method is optimised in this paper. This fuel injection system is semi-direct injection (SDI). In contrast to PFI, SDI also uses a low-pressure injector, which introduces fuel directly into the ports of the engine. This study explores the feasibility of SDI on a free-piston engine generator through numerical simulation. The results show that SDI can effectively avoid short-circuit losses and improve the fuel capture rate by more than 60 % relative to PFI. Meanwhile, the ISFC is reduced by 42.3 %, and the lowest fuel consumption rate is 259.7 g/(kW·h). The combustion efficiency of SDI is maintained at over 95 % under warm-up conditions. Compared with PFI, the stagnation period is shortened by up to 10.9 % and the centre of gravity of combustion is advanced by up to 16.7 %. SDI effectively reduces HC emissions and has a lower NOx emission level than PFI. At the same time, SDI produces more CO due to stratified combustion.

A novel approach for quantum financial simulation and quantum state preparation

A novel approach for quantum financial simulation and quantum state preparation

Anthropogenic heat from buildings in Los Angeles County: A simulation framework and assessment

Anthropogenic heat (AH), i.e., waste heat from buildings to the ambient environment, increases urban air temperature and contributes to the urban heat island effect, which leads to more air-conditioning energy use and higher associated waste heat during summer, forming a positive feedback loop. This study used a bottom-up simulation approach to develop a dataset of the annual hourly AH profiles for 1.7 million buildings in Los Angeles (LA) County for the year 2018 aggregated at three spatial resolutions: 450 m, 12 km, and the census tract. Building AH exhibits strong seasonal and diurnal patterns, as well as large spatial variations across the urban areas. Building AH peaks in May and reaches a maximum of 878 W/m2 within one of several AH hotspots in the region. Among the three major AH components (surface convection, heat rejection from HVAC systems, and zonal air exchange), the surface convection component is the largest, accounting for 78% of the total building AH across LA County. Higher AH is attributed to large building density, a high percentage of industrial buildings, and older building stock. While AH peaks during the day, the resulting ambient temperature increases are much larger during the night. During the July 2018 heatwave in LA County, building AH (excluding the surface component) leads to a daily maximum ambient temperature increase of up to 0.6 °C and a daily minimum ambient temperature increase of up to 2.9 °C. It is recommended that reducing summer building AH should be considered by policy makers in developing mitigation measures for cities to transition to clean energy while improving heat resilience.

Solar photovoltaic-assisted DC vapour compression with a low-cost ice gel thermal battery for off-grid building cooling

In off-grid areas where extending the grid is costly, traditional AC powered air conditioning units pose challenges for off-grid photovoltaic PV setups due to expensive inverters and battery storage. This leads to an interest in cost-effective solar-driven DC cooling system. However, its efficiency depends on solar energy availability with limited operation during low solar radiation. This study proposes a solar PV-driven DC vapour compression system with variable refrigerant flow (VRF) and low-cost organic phase change material (PCM) ice gel storage. Preliminary experimental work determined the best operational mode namely, Mode A (PV + Thermal Battery), Mode B (PV-electrical battery-3h + Thermal Battery), Mode C (PV-grid + Thermal Battery), and Mode D (Grid + Thermal Battery). Key performance parameters include the Performance Quality Factor of the PV-powered system SFPVVC, and levelized cost of cooling (LCOC). Mode B was found the best, and due to the system's complex dynamic variation with environmental parameters, a novel simulation approach combining artificial neural network (ANN) and TRNSYS was developed and experimentally validated. Thermal comfort is crucial for healthcare facilities as it promotes patient well-being, enhances staff performance, and is vital for infection control, thus a rural healthcare facility in Malaysia was selected for the simulation case-study. Mode B showed at least 50 % higher SFPVVC compared to grid reliance, with superior LCOC performance, ensuring sustainable cooling despite limited electricity supply. The low-cost ice gel thermal battery reduced the system cost to 0.06 USD/kWh compared to 0.56 USD/kWh without it and 1.01 USD/kWh with grid extension. These findings offer insights for implementing similar sustainable cooling systems in rural areas.

Reliability analysis of complex systems using subset simulations with Hamiltonian Neural Networks

We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficient gradient evaluations using Hamiltonian neural networks. This combination is especially advantageous because the neural network architecture conserves the Hamiltonian, which defines the acceptance criteria of the Hamiltonian Monte Carlo sampler. Hence, this strategy achieves high acceptance rates at low computational cost. Our approach estimates small failure probabilities using Subset Simulations. However, in low-probability sample regions, the gradient evaluation is particularly challenging. The remarkable accuracy of the proposed strategy is demonstrated on different reliability problems, and its efficiency is compared to the traditional Hamiltonian Monte Carlo method. We note that this approach can reach its limitations for gradient estimations in low-probability regions of complex and high-dimensional distributions. Thus, we propose techniques to improve gradient prediction in these particular situations and enable accurate estimations of the probability of failure. The highlight of this study is the reliability analysis of a system whose parameter distributions must be inferred with Bayesian inference problems. In such a case, the Hamiltonian Monte Carlo method requires a full model evaluation for each gradient evaluation and, therefore, comes at a very high cost. However, using Hamiltonian neural networks in this framework replaces the expensive model evaluation, resulting in tremendous improvements in computational efficiency.

Research on the Effect of Structural Parameters on Cavitation Performance of Shear Hydrodynamic Cavitation Generator

The method of cavitation is increasingly applied in water environmental protection. Based on the numerical simulation method, a study on the structural parameters of the shear-type hydrodynamic cavitation generators for wastewater treatment is proceeded. The internal flow field is described by employing the mixed multiphase flow model and the Zwart cavitation model. Experiments were conducted by applying the wastewater from a dyeing factory as the medium. The degradation rate of COD in water characterizes the cavitation performance of the hydrodynamic cavitation generator, and the rationality of the numerical simulation approach is validated. The findings indicate that different structural parameters have a great influence on the cavitation performance. The appropriate number of tooth rows creates a flow field with a greater vapor and velocity than the other parameters. The number of teeth in a single row, the outer diameter of the hydrodynamic cavitation generator and the tooth bevel angle also affect the cavitation performance to some extent, and there is an optimal value. The study provides a reference for the applicability of a numerical simulation of the flow field inside the hydrodynamic cavitation generator and the structural optimization of the rotary hydrodynamic cavitation generator.

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant.

Prospects for Antiferromagnetic Spintronic Devices

This article examines recent advances in the field of antiferromagnetic spintronics from the perspective of potential device realization and applications. We discuss advances in the electrical control of antiferromagnetic order by current-induced spin–orbit torques, particularly in antiferromagnetic thin films interfaced with heavy metals. We also review possible scenarios for using voltage-controlled magnetic anisotropy as a more efficient mechanism to control antiferromagnetic order in thin films with perpendicular magnetic anisotropy. Next, we discuss the problem of electrical detection (i.e., readout) of antiferromagnetic order and highlight recent experimental advances in realizing anomalous Hall and tunneling magnetoresistance effects in thin films and tunnel junctions, respectively, which are based on noncollinear antiferromagnets. Understanding the domain structure and dynamics of antiferromagnetic materials is essential for engineering their properties for applications. For this reason, we then provide an overview of imaging techniques as well as micromagnetic simulation approaches for antiferromagnets. Finally, we present a perspective on potential applications of antiferromagnets for magnetic memory devices, terahertz sources, and detectors.

Explainable Anomaly Detection of Synthetic Medical IoT Traffic Using Machine Learning

In the context of the Internet of Things (IoT), particularly within medical facilities, the detection and categorization of Internet traffic remain significant challenges. While conventional methods for IoT traffic analysis can be applied, obtaining suitable medical traffic data is challenging due to the stringent privacy constraints associated with the health domain. To address this, this study proposes a network traffic simulation approach using an open-source tool called IoT Flock, which supports both CoAP and MQTT protocols. The tool is used to create a synthetic dataset, to simulate IoT traffic originating from various smart devices in different hospital rooms. The study shows a complete anomaly detection analysis of IoT-Flock-generated traffic, both normal and malicious, by leveraging and comparing traditional machine learning techniques, deep learning models with multiple hidden layers, and explainable artificial intelligence techniques. The results are very promising. For the binary classification, for example, the obtained accuracy is close to 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} in the case of the CoAP protocol. Good results are also obtained when the multinomial classification is performed, observing that CoAP packets are classified better than MQTT packets, even if the identification of the different MQTT packets reaches very high metrics for the most of the considered algorithms. Moreover, the obtained classification rules are also meaningful in the considered IoT context. The results indicate that IoT-Flock synthetic data can effectively be used to train and test machine and deep learning models for detecting abnormal IoT traffic in medical scenarios. This research attempts also to bridge the gap between IoT security and healthcare, providing useful insights into securing medical IoT networks in general.

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation.

The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI. MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-freeimages using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility. In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC. The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.

A between-wall heat transfer simulation approach based on the lattice Boltzmann method and its application in tube heat transfer analysis

Between-wall heat transfer is a fundamental physical process. As a typical coupling heat and mass transfer process, between-wall heat transfer-based problems can be solved conveniently on the basis of advanced computation methods among which the lattice Boltzmann method (LBM) has been widely used in the analysis of simple heat and mass transfer cases. To investigate the applicability of LBM in more complex solid-fluid coupling heat transfer analysis, a 3D fluid-solid-fluid model was established in this work to carry out between-wall heat transfer simulations using the doubled-population LBM. Three independent simulation regions were coupled in the heat transfer model, and the heat flux was taken as the physical quantity that transferred from the region at high temperature to that at low temperature through solid wall. The simulation results satisfactorily agree with that obtained using the commercial code FLUENT. Furthermore, an equivalent fluid-wall-fluid heat transfer model was established for simulation of heat transfer between internal and external fluids through a circular tube. All the boundaries are complete quadrilaterals on which heat transfer boundary conditions can be conveniently applied. The proposed between-wall model based on LBM can be easily established and applied in cases with internal heat source or mass force.