3D Simulations Research Articles

On turning maneuverability in self-propelled burst-and-coast swimming

Fish have evolved remarkable underwater turning maneuverability, primarily under active control. This allows them to execute turns within confined spaces, such as during C-start rapid turning. In our study, conducted through computational fluid dynamics simulations of a self-propelled swimmer, we revealed that burst-and-coast swimming patterns can generate various turning behaviors purely through passive fluid–body interactions. The burst-and-coast swimming is characterized by the alternating tail movements between continuous undulating burst phases (bp) and non-undulating or gliding coast phases (cp). Through extensive systematic three-dimensional (3D) simulations, we found that both the burst-and-coast duty cycle—the ratio of burst duration to the total cycle duration—and the swimmer's undulation frequency inhibit turning maneuverability, which is quantified by the curvature of swimming trajectories. We also found there is an optimal Reynolds number that maximizes turning maneuverability. Further analysis suggests that the turning maneuverability is probably due to the persistent presence of the Wagner effect during burst phases and the Magnus effect during coast phases, which differs from the mechanism of actively generating lateral forces by asymmetric continuous flapping. These insights not only advance our understanding of fish locomotion control mechanisms but also provide guidelines for designing underwater robots with improved navigational capabilities.

Mind the Kinematics Simulation of Planet–Disk Interactions: Time Evolution and Numerical Resolution

Planet–disk interactions can produce kinematic signatures in protoplanetary disks. While recent observations have detected non-Keplerian gas motions in disks, their origins are still being debated. To explore this, we conduct 3D hydrodynamic simulations using the code FARGO3D to study nonaxisymmetric kinematic perturbations at two scale heights induced by Jovian planets in protoplanetary disks, followed by examinations of detectable signals in synthetic CO emission line observations at millimeter wavelengths. We advocate for using residual velocity or channel maps, generated by subtracting an azimuthally averaged background of the disk, to identify planet-induced kinematic perturbations. We investigate the effects of two basic simulation parameters, simulation duration and numerical resolution, on the simulation results. Our findings suggest that a short simulation (e.g., 100 orbits) is insufficient to establish a steady velocity pattern given our chosen viscosity (α = 10−3) and displays plenty of fluctuations on an orbital timescale. Such transient features could be detected in observations. By contrast, a long simulation (e.g., 1000 orbits) is required to reach steady state in kinematic structures. At 1000 orbits, the strongest detectable velocity structures are found in the spiral wakes close to the planet. Through numerical convergence tests, we find hydrodynamics results converge in spiral regions at a resolution of 14 cells per disk scale height or higher. Meanwhile, synthetic observations produced from hydrodynamic simulations at different resolutions are indistinguishable with 0.″1 angular resolution and 10 hr of integration time on Atacama Large Millimeter/submillimeter Array.

A high-order generalised differential quadrature element method for simulating 2D and 3D incompressible flows on unstructured meshes

In this paper, a high-order generalised differential quadrature element method (GDQE) is proposed to simulate two-dimensional (2D) and three-dimensional (3D) incompressible flows on unstructured meshes. In this method, the computational domain is decomposed into unstructured elements. In each element, the high-order generalised differential quadrature (GDQ) discretisation is applied. Specifically, the GDQ method is utilised to approximate the partial derivatives of flow variables and fluxes with high-order accuracy inside each element. At the shared interfaces between different GDQ elements, the common flux is computed to account for the information exchange, which is achieved by the lattice Boltzmann flux solver (LBFS) in the present work. Since the solution in each GDQ element solely relies on information from itself and its direct neighbouring element, the developed method is authentically compact, and it is naturally suitable for parallel computing. Furthermore, by selecting the order of elemental GDQ discretisation, arbitrary accuracy orders can be achieved with ease. Representative incompressible flow problems, including 2D laminar flows as well as 3D turbulent simulations, are considered to evaluate the accuracy, efficiency, and robustness of the present method. Successful numerical simulations, especially for scale-resolving 3D turbulent flow problems, confirm that the present method is efficient and high-order accurate.

Energy-consistent discretization of viscous dissipation with application to natural convection flow

A new energy-consistent discretization of the viscous dissipation function in incompressible flows is proposed. It is implied by choosing a discretization of the diffusive terms and a discretization of the local kinetic energy equation and by requiring that continuous identities like the product rule are mimicked discretely. The proposed viscous dissipation function has a quadratic, strictly dissipative form, for both simplified (constant viscosity) stress tensors and general stress tensors. The proposed expression is not only useful in evaluating energy budgets in turbulent flows, but also in natural convection flows, where it appears in the internal energy equation and is responsible for viscous heating. The viscous dissipation function is such that a consistent total energy balance is obtained: the ‘implied’ presence as sink in the kinetic energy equation is exactly balanced by explicitly adding it as source term in the internal energy equation.Numerical experiments of Rayleigh–Bénard convection (RBC) and Rayleigh–Taylor instabilities confirm that with the proposed dissipation function, the energy exchange between kinetic and internal energy is exactly preserved. The experiments show furthermore that viscous dissipation does not affect the critical Rayleigh number at which instabilities form, but it does significantly impact the development of instabilities once they occur. Consequently, the value of the Nusselt number on the cold plate becomes larger than on the hot plate, with the difference increasing with increasing Gebhart number. Finally, 3D simulations of turbulent RBC show that energy balances are exactly satisfied even for very coarse grids. Therefore, the proposed discretization also forms an excellent starting point for testing sub-grid scale models and is a useful tool to assess energy budgets in any turbulence simulation, with or without the presence of natural convection.

Lower‐Hybrid Wave‐Induced Plasma Mixing Related to Kelvin‐Helmholtz Vortices During Southward IMF

AbstractWe examine characteristics of the boundaries of 11 Kelvin‐Helmholtz vortex crossings observed by MMS on 23 September 2017 under southward IMF conditions. At both the leading and trailing edges, boundary regions of mixed plasma are observed together with lower‐hybrid wave activity. We found that thicker boundary regions feature a higher number of sub‐ion scale current sheets, of which only one shows clear reconnection signatures. Moreover, the lower‐hybrid waves along the vortex spine region are identified as an effective mechanism for plasma transport with an estimated diffusion coefficient of s. Comparisons with 3D simulations performed under the same conditions as the MMS event suggest that the extension of the boundary regions as well as the number of current sheets are related to different evolutionary stages of the vortices. Such observations can be explained by changes in the upstream magnetic field conditions.

Beryllium–tungsten graded density inner shells in double shell capsules for improved hydrodynamic stability

The outer surface of the high-Z inner shell in the double shell configuration of inertial confinement fusion experiments experiences Rayleigh–Taylor instability growth during the implosion process due to inverted density and pressure gradients between a highly compressed foam interstitial layer and the accelerating dense inner shell. Graded density layers have long been known to reduce instability growth rates. In this study, we employ high-fidelity radiation hydrodynamic simulations to demonstrate this improved stability when grading beryllium into tungsten. We first characterize the response to L-band preheat of these layers using a newly calibrated radiation drive. While graded layer capsules suffer reduced performance (here, measured as DD neutron yield from a CD foam fuel) in 1D simulations due to reduced kinetic energy coupling and reduced fuel compression, they suffer less of a performance drop when 2D instabilities are accounted for. With the improved stability of graded layers, we explore the performance of capsules with larger fuel radii and thinner shells as a preliminary study to find new designs in which graded layers produce the highest yields.

Junction conditions for one-dimensional network hemodynamic model for total cavopulmonary connection using physically informed deep learning technique

Abstract This paper presents a novel methodology utilizing physics-informed neural network (PINN) as a junction condition for a 1D network model of blood flow in total cavopulmonary connection generated by the Fontan procedure. The technique integrates a 3D mesh generation process based on the parameterization of the junction geometry, along with a sophisticated physically regularized neural network architecture. Synthetic datasets are produced using 3D steady Stokes simulations within fixed boundaries. We use a physically informed feedforward neural network that utilizes a physically regularized loss function, which incorporates the principle of mass conservation. Our PINN achieves a tolerance of 6% on the test set. We develop a 1D-PINN multiscale model based on a previously developed method for multiscale 1D–3D simulations. Comparison with 1D–3D Stokes based model and 3D Navier–Stokes based model verifies the 1D-PINN model. In the first and second comparison, the maximum deviations of the averaged pressures and flows do not exceed 1.48% and 12.26%, respectively.

Extrusion Parameters Optimization and Mechanical Properties of Bio-Polyamide 11-Based Biocomposites Reinforced with Short Basalt Fibers.

The increasing demand for sustainable materials in high-value applications, particularly in the automotive industry, has prompted the development of biocomposites based on renewable or recyclable matrices and natural fibers as reinforcements. In this context, this paper aimed to produce composites with improved mechanical and thermal properties (tensile, flexural, and heat deflection temperature) through an optimized process pathway using a biobased polyamide reinforced with short basalt fibers. This study emphasizes the critical impact of fiber length, matrix adhesion, and the variation in matrix properties with increasing fiber content. These factors influence the properties of short-fiber composites produced via primary processing using extrusion and shaped through injection molding. The aim of this work was to optimize extrusion conditions using a 1D simulation software to minimize excessive fiber fragmentation during the extrusion process. The predictive model's capacity to forecast fiber degradation and the extent of additional fiber breakage during extrusion was evaluated. Furthermore, the impact of injection molding on these conditions was investigated. Moreover, a comprehensive thermomechanical characterization of the composites, comprising 10%, 20%, and 30% fiber content, was carried out, focusing on the correlation with morphology and processing using SEM and micro-CT analyses. In particular, how the extrusion process parameters adopted can influence fiber breakage and how injection molding can influence the fiber orientation were investigated, highlighting their influence in determining the final mechanical properties of short fiber composites. By optimizing the process parameters, an increment with respect to bio-PA11 in the tensile strength of 38%, stiffness of 140%, and HDT of 77% compared to the matrix were obtained.

Enhancing 3D planetary atmosphere simulations with a surrogate radiative transfer model

Abstract This work introduces an approach to enhancing the computational efficiency of 3D atmospheric simulations by integrating a machine-learned surrogate model into the OASIS global circulation model (GCM). Traditional GCMs, which are based on repeatedly numerically integrating physical equations governing atmospheric processes across a series of time-steps, are time-intensive, leading to compromises in spatial and temporal resolution of simulations. This research improves upon this limitation, enabling higher resolution simulations within practical timeframes. Speeding up 3D simulations holds significant implications in multiple domains. Firstly, it facilitates the integration of 3D models into exoplanet inference pipelines, allowing for robust characterisation of exoplanets from a previously unseen wealth of data anticipated from JWST and post-JWST instruments. Secondly, acceleration of 3D models will enable higher resolution atmospheric simulations of Earth and Solar System planets, enabling more detailed insights into their atmospheric physics and chemistry. Our method replaces the radiative transfer module in OASIS with a recurrent neural network-based model trained on simulation inputs and outputs. Radiative transfer is typically one of the slowest components of a GCM, thus providing the largest scope for overall model speed-up. The surrogate model was trained and tested on the specific test case of the Venusian atmosphere, to benchmark the utility of this approach in the case of non-terrestrial atmospheres. This approach yields promising results, with the surrogate-integrated GCM demonstrating above 99.0% accuracy and 147 factor GPU speed-up of the entire simulation compared to using the matched original GCM under Venus-like conditions.

Examining the Role of Surface Temperature on Boundary Layer Stability and Transition to Turbulence

This study investigates the influence of varying surface temperature on the critical Reynolds number to understand the onset of turbulence in the boundary layer of a flat plate. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent have been utilized for the purpose of this study. The study systematically varies surface temperature to measure its impact on flow stability. The research demonstrates that an increase in surface temperature results in a lower critical Reynolds number (leading to an earlier transition from laminar to turbulent flow) by modeling air as the working fluid and applying the k-ω SST turbulence model. The results align with theoretical predictions which highlight the destabilizing effect of temperature-induced viscosity changes. This finding has practical implications for aerospace engineering, HVAC systems, and environmental modeling where controlling turbulence can optimize performance and energy efficiency. The study acknowledges limitations due to the use of a 2D model and suggests future work using 3D simulations and other flow conditions to expand the applicability of the findings.

LOSS BREAKDOWN IN AXIAL TURBINES: A NEW METHOD FOR VORTEX LOSS AND WAKE DETECTION FROM 3D RANS SIMULATIONS

Abstract To enhance turbine efficiency, it is essential to mitigate the losses generated by irreversible phenomena in turbine flows, including boundary layers, shock waves, vortices, and trailing edge wakes. Fast and accurate detection of losses is crucial from the earliest design stages, where reduced-order models based on oversimplified correlations are employed. This requires a deep understanding of the physics behind loss-generating mechanisms, attainable by examining the 3D flow. While existing criteria can identify various phenomena, accurately quantifying vortex-generated losses remains challenging, as these losses frequently extend beyond the vortical structure. This paper provides a straightforward and effective approach to localize and assess vortex-related losses. The method is grounded in Zlatinov's decomposition of the entropy generation rate into a streamwise and secondary flow component. A criterion based on vortex kinematics evaluates the vortex's strength, enabling the determination of its spatial influence and contribution to overall losses. To validate the method, a post-processing code is developed to perform loss breakdown, using existing identification criteria and new techniques introduced within this work, particularly for wake detection. 3D RANS simulations on various configurations, from simple curved ducts to nozzle guide vanes, allow to gradually test and validate the computational tool. Results confirm that the highest entropy generation rates occur outside the vortical structure and show good ability to identify both the vortex shape and its area of influence in terms of losses. A significant improvement in predicting vortex losses is observed, especially for turbine blades with leakage vortices.

3D Model Simulation in Preoperative Planning of Complex and Revision Hip Replacement

Complex and revision hip arthroplasty has remained one of the most discussed orthopedic topics in recent years. With the advancement of printing technologies 3D preoperative planning and live model simulation provides us with “hands on” procedures without any risk for the patient. Modern computer technologies present the possibility for preoperative planning of Total Hip Arthroplasty (THA) in digital environment. 3D printing of anatomical models in real size allows accurate assessment of the pathological deviations of the anatomy, precise preoperative planning of the reconstruction and simulation of the operative intervention ex-vivo. We present a series of 3D simulations for planning hip replacement, illustrating the technique, discussing its advantages and disadvantages, in complex primary and revision hip arthroplasty. In the complex and revision surgery of the hip joint, a correct assessment of bone defects is mandatory. Reconstructing the biomechanical parameters – centre of rotation, femoral offset and leg length discrepancy, is also of great significance. The 3D reconstruction with biomodel simulations offers exceptional accuracy in this respect. The method can be an excellent tool for education of surgeons in training and residents.

Three-Dimensional (3D) Flood Simulation Aids Informed Decision Making: A Case of a Two-Story Underground Parking Lot in Beijing

Flooding in underground spaces, such as subway stations, underground malls, and garages, has increased due to intensified rainfall, urbanization, and population growth. Traditional 2D simulations often overlook crucial vertical flow variations, especially in steep transitions like stairs and ramps. The current study aims to investigate the flood dynamics in large underground geometries by taking a parking lot in Beijing, China, as a study case. The model overcomes the limitations of previous simulations by adapting a full 3D mesh-based simulation with reasonable computational cost. Unlike earlier studies, this model employs a high temporal resolution transient inflow at the inlet to the underground space. Simulation scenarios consider different return periods (5, 20, and 100 years) and inlet water depths, providing an analysis of their impact on flood status in the underground structure. The model generates high spatial–temporal results, enabling precise detection of flood-prone locations, evacuation times, and suggested mitigation techniques. The results recommend evacuating from hazard areas before the 10th minute during extreme flood events. Additionally, the study estimates a 40% increase in flood hazards for scenarios with direct connections between levels. Overall, the study highlights the importance of 3D simulations for accurate risk assessment.

Probing Three-dimensional Magnetic Fields. III. Synchrotron Emission and Machine Learning

Synchrotron observation serves as a tool for studying magnetic fields in the interstellar medium and intracluster medium, yet its ability to unveil three-dimensional (3D) magnetic fields, meaning probing the field’s plane-of-the-sky (POS) orientation, inclination angle relative to the line of sight, and magnetization from one observational data, remains largely underexplored. Inspired by the latest insights into anisotropic magnetohydrodynamic (MHD) turbulence, we found that synchrotron emission’s intensity structures inherently reflect this anisotropy, providing crucial information to aid in 3D magnetic field studies: (i) the structure’s elongation gives the magnetic field’s POS orientation and (ii) the structure’s anisotropy degree and topology reveal the inclination angle and magnetization. Capitalizing on this foundation, we integrate a machine learning approach—convolutional neural network (CNN)—to extract this latent information, thereby facilitating the exploration of 3D magnetic fields. The model is trained on synthetic synchrotron emission maps, derived from 3D MHD turbulence simulations encompassing a range of sub-Alfvénic to super-Alfvénic conditions. We show that the CNN is physically interpretable and the CNN is capable of obtaining the POS orientation, inclination angle, and magnetization. Additionally, we test the CNN against the noise effect and the missing low-spatial frequency. We show that this CNN-based approach maintains a high degree of robustness even when only high-spatial frequencies are maintained. This renders the method particularly suitable for application to interferometric data lacking single-dish measurements. We applied this trained CNN to the synchrotron observations of a diffuse region. The CNN-predicted POS magnetic field orientation shows a statistical agreement with that derived from synchrotron polarization.

The Influence of the Intake Geometry on the Performance of a Four-Stroke SI Engine for Aeronautical Applications

In this work, the influence of plenum and port geometry on the performance of the intake process in a four-stroke spark ignition engine for ultralight aircraft applications is analyzed. Three intake systems are considered: the so-called “standard plenum”, with a relatively small plenum volume, the “V1 plenum”, with a larger plenum volume, and the “standard plenum” equipped with a large curvature manifold called the “G2 port”. Both measurements and 3D CFD simulations, by using Ansys® Academic Fluent, Release 20.2, are performed to characterize and analyze the steady-flow field in the intake system for selected valve lifts. The experimental data and the numerical results are in excellent agreement with each other. The results show that at the maximum valve lift, i.e., 12 mm, the V1 plenum allows an increase in the air mass flow rate of 9.1% and 9.4% compared to the standard plenum and the standard plenum with the “G2 port”, respectively. In addition, the volumetric efficiency has been estimated under unsteady-flow conditions for all geometries at relatively high engine rpms. The difference between numerical results and measurements is less than 1% for the standard plenum, thus proving the accuracy of the model, which is then used to study the other configurations. The V1 plenum shows a fairly constant volumetric efficiency as the engine speed increases, although such an efficiency is lower than that of the other two geometries considered in this work. Specifically, the use of the “G2 port” leads to an increase of 1.5% in terms of volumetric efficiency with respect to the configuration with the original manifold. Furthermore, for the “G2 port” configuration, higher turbulent kinetic energy and higher swirl and tumble ratios are observed. This is expected to result in an improvement of air–fuel mixing and flame propagation.

Accreting Neutron Stars in 3D General-relativistic Magnetohydrodynamic Simulations: Jets, Magnetic Polarity, and the Interchange Slingshot

Abstract Accreting neutron stars differ from black holes by the presence of the star’s own magnetic field, whose interaction with the accretion flow is a central component in understanding these systems’ disk structure, outflows, jets, and spin evolution. It also introduces an additional degree of freedom, as the stellar dipole can have any orientation relative to the inner disk’s magnetic field. We present a suite of 3D general-relativistic magnetohydrodynamic simulations in which we investigate the two extreme polarities, with the dipole field being either parallel or antiparallel to the initial disk field, in both the accreting and propeller states. When the magnetosphere truncates the disk near or beyond the corotation radius, most of the system’s properties, including the relativistic jet power, are independent of the star–disk relative polarity. However, when the disk extends well inside the corotation radius, in the parallel orientation the jet power is suppressed and the inner disk is less dense and more strongly magnetized. We suggest a physical mechanism that may account for this behavior—the interchange slingshot—and discuss its astrophysical implications. When the star is in the rapidly accreting regime, which in most cases will be associated with strong spin-up, we expect large observational differences between the two magnetic orientations. This may be reflected in increased variability as the accretion flow drags in successive magnetic structures of varying polarity.

Experimental characterization and 3D simulations of turbulent flames assisted by nanosecond plasma discharges

This paper presents quantitative experimental data generated for the validation of plasma-assisted combustion (PAC) simulations. These data are then used to validate the phenomenological model of Castela et al. They are also useful to test other PAC models. In the experiment presented here, Nanosecond Repetitively Pulsed (NRP) discharges are applied to a lean-premixed turbulent methane-air flame initially near the lean blow-off limit. The discharges significantly enhance the combustion and stabilize the flame after a few pulses. Electrical and optical diagnostics are employed to extensively quantify the transient and steady state of the plasma-stabilization process. The flame shape is characterized by OH* chemiluminescence imaging. In the discharge region, OH density profiles are obtained by 1D laser-induced fluorescence, and the gas temperature is measured by optical emission spectroscopy measurements. The local gas temperature increases by 1250 K, and the OH number density rises sevenfold when NRP discharges are applied. These results evidence the cumulative thermal and chemical effects of NRP discharges, which are especially challenging to replicate numerically. A Large Eddy Simulation (LES) of the experiment is performed. Combustion chemistry is modeled by an analytically reduced mechanism, while the plasma discharge is described by the low-CPU cost phenomenological model of Castela et al., which aims to capture the main thermal and chemical effects induced by the discharges. The model of Castela et al. is validated in the burnt gases by the remarkable agreement between the simulations and the experiments regarding the flame shape, the local gas temperature, and the OH number density. More generally, this work demonstrates the relevance of simplified plasma models in LES solvers to simulate complex plasma-assisted burners.

Optimal design of deep stormwater drainage tunnels to address urban flooding in Korea

Deep stormwater drainage tunnels play a crucial role in mitigating severe urban flooding in Korea, particularly as climate change leads to more extreme weather events. These tunnels typically consist of an entrance reservoir, a circular reinforced concrete culvert with a flat slope, a downstream reservoir, and an outlet to a nearby river. In this system, the flow within the circular culvert is driven solely by the momentum of free-falling stormwater runoff. However, concerns have emerged within the Korean water resources engineering community about the drainage capacity of these tunnels, even with a substantial fall height of approximately 30 m. Intensified localized rainfall, the abrupt transition from flood waves to pressurized flow within the circular culvert, and trapped air pockets can also pose significant challenges to the tunnel’s drainage efficiency. Conventional design methods often fail to optimize the culvert’s diameter and spatial configuration, leading to suboptimal performance. In response, this study utilizes 3D numerical simulations with the interFoam solver from the OpenFOAM toolbox to address these issues. The results indicate that smaller culvert diameters—provided they do not exceed the maximum permissible flow velocity—enhance drainage capacity by reducing the impact of shock waves and stabilizing flow. These findings challenge the prevailing design practice in Korea, which holds that larger culverts inherently offer superior drainage capacity. Moreover, the simulations suggest that as culvert diameters increase, the size of trapped air pockets grows, further reducing efficiency. Although air chambers can help mitigate the retarding effects of trapped air pockets, their effectiveness diminishes if positioned near the origin of shock waves, where they risk being filled with stormwater. Based on these insights, several key recommendations are proposed: First, the design of deep stormwater tunnels should prioritize minimizing the extent of trapped air pockets, even when air chambers are used. Second, the current focus on detention capacity in design practices should be reevaluated, as excessive detention capacity may exacerbate air pocket formation. Finally, modifying the inlet channel to induce spiral flow within the entrance reservoir could reduce impulsive forces, lower maintenance costs related to armoring rocks at the reservoir bottom, and stabilize flow more quickly, thereby enhancing overall drainage capacity.

Virtual Development of a Single-Cylinder Hydrogen Opposed Piston Engine

A significant challenge in utilizing hydrogen in conventional internal combustion engines is achieving a balance between NOx emissions and brake power output. A lean premixed charge (Lambda ≈ 2.5) allows for efficient and stable combustion with minimal NOx emissions. However, this comes at the cost of reduced power density due to the higher air requirements of the thermodynamic process. While supercharging can mitigate this drawback, it introduces increased complexity, cost, and size. An intriguing alternative is the 2-stroke cycle, particularly in an opposed piston (OP) configuration. This study presents the virtual development of a single-cylinder 2-stroke OP engine with a total displacement of 0.95 L, designed to deliver 25 kW at 3000 rpm. Thanks to its compact size, high thermal efficiency, robustness, modularity, and low manufacturing cost, this engine is intended for use either as an industrial power unit or in combination with electric motors in hybrid vehicles. The overarching goal of this project is to demonstrate that internal combustion engines can offer a practical and cost-effective alternative to hydrogen fuel cells without significant penalties in terms of efficiency and pollutant emissions. The design of this novel engine started from scratch, and both 1D and 3D CFD simulations were employed, with particular focus on optimizing the cylinder’s geometry and developing an efficient low-pressure injection system. The numerical methodology was based on state-of-the-art commercial codes, in line with established engineering practices. The numerical results indicated that the optimized engine configuration slightly surpasses the target performance, achieving 29 kW at 3000 rpm, while maintaining near-zero NOx emissions (<20 ppm) and high brake thermal efficiency (~40%) over a wide power range. Additionally, the cost of this engine is projected to be lower than an equivalent 4-stroke engine, due to fewer components (e.g., no cylinder head, poppet valves, or camshafts) and a lighter construction.

Numerical Simulation on the Impact of Back Gate Voltage in Thin Body and Thin Buried Oxide of Silicon on Insulator (SOI) MOSFETs

Silicon-on-Insulator (SOI) technology provides a solution for controlling Short-Channel Effects (SCEs) and enhancing the performance of Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs). However, scaling down SOI MOSFETs to a nanometer scale does not necessarily yield further scaling benefits. Introducing multiple gates, such as a double gate configuration, can effectively mitigate SCEs. Nonetheless, fabricating a flawless double gate structure is an exceedingly challenging endeavor that remains unrealized. The adoption of a back gate bias, with an asymmetrical thickness arrangement between the front and back gates, mimicking the behavior of a double gate, offers an alternative approach. This approach has the potential to modify the electrical characteristics of the device, thus potentially leading to improved control over SCEs. In this study, we employed 2D simulations using Atlas to investigate the influence of back gate biases, namely, -2.0 V, 0 V, and 2.0 V on a 10 nm silicon thickness at the top and a 20 nm buried oxide thickness for n-channel MOSFETs. We focused on key parameters, including threshold voltage (VTh), Drain Induced Barrier Lowering (DIBL), and Subthreshold Swing (SS). The results demonstrate that a negative back gate bias is the most favorable configuration, as it yields superior performance. This translates into more effectively controlled SCEs across all the parameters of interest.