Force Components Research Articles

Efficient all-electron hybrid density functionals for atomistic simulations beyond 10 000 atoms.

Hybrid density functional approximations (DFAs) offer compelling accuracy for abinitio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drastically optimized resolution-of-identity-based real-space implementation of the exact exchange evaluation for both non-periodic and periodic boundary conditions in the all-electron code FHI-aims, targeting high-performance central processing unit (CPU) compute clusters. The introduction of several new refined message passing interface (MPI) parallelization layers and shared memory arrays according to the MPI-3 standard were the key components of the optimization. We demonstrate significant improvements of memory and performance efficiency, scalability, and workload distribution, extending the reach of hybrid DFAs to simulation sizes beyond ten thousand atoms. In addition, we also compare the runtime performance of the PBE, HSE06, and PBE0 functionals. As a necessary byproduct of this work, other code parts in FHI-aims have been optimized as well, e.g., the computation of the Hartree potential and the evaluation of the force and stress components. We benchmark the performance and scaling of the hybrid DFA-based simulations for a broad range of chemical systems, including hybrid organic-inorganic perovskites, organic crystals, and ice crystals with up to 30 576 atoms (101 920 electrons described by 244 608 basis functions).

Multiscale dimensions of the foreign working citizens participation to the Italian labour market: intra-regional heterogeneities across the North–South divide

This article delves into the distinctive intra- and interregional geographical heterogeneity of Italy, emphasizing demographic and socio-economic variations and the role of foreign employment, considering the labour market as a fundamental driver for migration and local inclusion. The article identifies a gap in understanding the employed foreign population as a multiscale process in Lombardy and Campania, representative regions as case studies from the North and South divide using a MGWR approach. The results reveal contrasting effects of the Italian labour force’s unemployment rate (URI). In Lombardy, a positive effect suggests working competition between labour force components while, in Campania, the relation is less clear. The analysis underscores significant local heterogeneity, emphasizing the importance and urgency of employing local scale analysis for accurate statistics. The study emphasizes the multiscale nature of the analysed process, demonstrating variable effects across different regional contexts. While the study is limited to two regions and cross-sectional data, it marks the first attempt in Italy to address the foreign presence as a multiscale process, highlighting the need for localized and multiscale approaches in understanding spatial processes related to demography and population issues.

Single-Molecule Detection of the Encounter and Productive Electron Transfer Complexes of a Photosynthetic Reaction Center.

Small, diffusible redox proteins play an essential role in electron transfer (ET) in respiration and photosynthesis, sustaining life on Earth by shuttling electrons between membrane-bound complexes via finely tuned and reversible interactions. Ensemble kinetic studies show transient ET complexes form in two distinct stages: an "encounter" complex largely mediated by electrostatic interactions, which subsequently, through subtle reorganization of the binding interface, forms a "productive" ET complex stabilized by additional hydrophobic interactions around the redox-active cofactors. Here, using single-molecule force spectroscopy (SMFS) we dissected the transient ET complexes formed between the photosynthetic reaction center-light harvesting complex 1 (RC-LH1) of Rhodobacter sphaeroides and its native electron donor cytochrome c2 (cyt c2). Importantly, SMFS resolves the distribution of interaction forces into low (∼150 pN) and high (∼330 pN) components, with the former more susceptible to salt concentration and to alteration of key charged residues on the RC. Thus, the low force component is suggested to reflect the contribution of electrostatic interactions in forming the initial encounter complex, whereas the high force component reflects the additional stabilization provided by hydrophobic interactions to the productive ET complex. Employing molecular dynamics simulations, we resolve five intermediate states that comprise the encounter, productive ET and leaving complexes, predicting a weak interaction between cyt c2 and the LH1 ring near the RC-L subunit that could lie along the exit path for oxidized cyt c2. The multimodal nature of the interactions of ET complexes captured here may have wider implications for ET in all domains of life.

Chaos synchronization of two coupled map lattice systems using safe reinforcement learning

When synchronizing two continuous typical chaotic systems, the state variables of the response system are usually bounded and satisfy the Lipschitz condition. This permits a universal synchronization method. In the synchronization of two high-dimensional discrete chaotic systems such as the coupled map lattice systems, the state variables of the response system often diverge, which makes it difficult to seek a universal synchronization method. To overcome this difficulty, bounded and hard constraints, which correspond to the concept of safety level III in reinforcement learning terms, on the state variables must be imposed, when the discrete response system is perturbed. We propose a universal method for synchronizing two discrete systems based on safe reinforcement learning (RL). In this method, the RL agent’s policy is used to reach the goal of synchronization and a safety layer added directly on top of the policy is used to satisfy safety level III. The safety layer consists of a one-step predictor for the perturbed response system and an action correction formulation. The one-step predictor, based on a next generation reservoir computing, is used to identify whether the next state of the perturbed system is within the chaotic domain, and if not, the action correction formula is activated to modify the corresponding perturbing force component to zero. In this way, the state of the perturbed system will remain in the bounded chaotic domain. We demonstrate that the proposed method succeeds in synchronization without divergence through a numerical example with two coupled map lattice systems, and the average synchronization time is 12.66 iteration steps over 1000 different initial conditions. The method works even when parameter mismatches and disturbances exist in the system. We compare the performance in both cases with and without the safety layer and find that successful synchronization is only possible in the former case, emphasizing the significance of the safety layer. The performance of the algorithm with optimal hyper-parameters obtained by Bayesian optimization is robust and stable.

A Potential Surface Warming Regime for Volcanic Super‐Eruptions Through Stratospheric Water Vapor Increases

AbstractLarge volcanic eruptions are known to influence the climate through a variety of mechanisms including aerosol‐forced cooling and warming via emitted CO2. The January 2022 Hunga shallow underwater eruption caused an increase in stratospheric water vapor, and demonstrated how the associated positive radiative forcing can be an important component of an eruption's climate forcing. We present interactive stratospheric aerosol model simulations of super‐volcanic eruptions with a range of SO2 emissions that can produce climate warming through feedback effects produced by a large igneous province (or “flood basalt”) mid‐latitude super‐eruption using Goddard Earth Observing System Chemistry Climate Model climate model simulations. The model experiments suggest total SO2 emissions ≳4,000 Tg/4 Gt generate a multi‐year period of sustained aerosol absorptive local‐heating of the upper troposphere and lower stratosphere and hence produce net climate warming after strong initial cooling. The eruptions produce stratospheric water vapor increases of factors of 8–600. The initiation of these feedbacks within the simulations suggest they could occur for individual stratovolcano eruptions of the scale of the Toba or Tambora eruptions. We note the sensitivity of our results to volcanic sulfate aerosol microphysics and model chemistry.

Effect of external electric field on copper/silica contact electrification and adhesion: insight from first-principles and molecular mechanics investigations

The electrostatic force induced by charge transfer during contact electrification is one of the main components of adhesion force at the solid interface. Some studies found that the magnitude of charge transfer and the consequently electrostatic force can be tuned by the external electric field. However, the detailed mechanism is still lacking in understanding. In this study, the effect of external electric field on copper/silica contact electrification and adhesion is studied via first-principles and molecular mechanics calculations and the mechanism is revealed by electrostatic potential and adhesion energy analysis. It is proved that the external electric field can affect the contact potential difference, which is the driving force of contact electrification, thus influencing the magnitude of charge transfer and electrostatic force. When the electric field direction is the same as the electron transfer direction, the contact electrification can be suppressed, leading to the decrease in the ratio of electrostatic force to van der Waals force. In particularly, the contact electrification and electrostatic force can be completely eliminated when applying a specific electric field intensity. This can provide an inspiration for quantitatively studying the source of adhesion force at solid interface.

Dynamic stall modeling of wind turbine blade sections based on a data-knowledge fusion method

Dynamic stall often leads to unsteady load and performance degradation in horizontal axis wind turbines. Therefore, accurate modeling of dynamic stall is crucial. However, due to the large variations of the blade aerodynamic profiles and the complexity of dynamic stall flow, numerical simulation and wind tunnel experiment are costly. On the other hand, widely used semi-empirical models have limited accuracy. Hence, this paper proposes a data-knowledge fusion method that incorporates physical knowledge into a neural network to improve its accuracy and generalization ability. Firstly, the force components of the Leishman–Beddoes model are incorporated into the network. An efficient dynamic stall model for the S809 airfoil is thus established with a small amount of high-precision experimental data. It achieves extrapolation predictions of reduced frequency and angle of attack with only 1/5 of the samples in the database to train. Moreover, to make full use of the accumulated existing airfoil data to assist in modeling other airfoils, the obtained S809 model is fused in the network to predict the aerodynamics of S810 and S814. The average relative error of the prediction cases is nearly 10%. Comprehensively, this paper provides a new paradigm for assessing the dynamic stall of the wind turbine blade.

A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration

The vector forces at the human-mattress interface are not only crucial for understanding the distribution of vertical and shear forces exerted on the human body during sleep but also serves as a significant input for biomechanical models of sleeping positions, whose accuracy determines the credibility of predicting musculoskeletal system loads. In this study, we introduce a novel method for calculating the interface vector forces. By recording indentations after supine and lateral positions using a vacuum mattress and 3D scanner, we utilize image registration techniques to align body pressure distribution with the mattress deformation scanning images, thereby calculating the vector force values for each unit area (36.25 mm × 36.25 mm). This method was validated through five participants attendance from two perspectives, revealing that (1) the mean summation of the vertical force components is 98.67% ± 7.21% body weight, exhibiting good consistency, and mean ratio of horizontal component force to body weight is 2.18% ± 1.77%. (2) the predicted muscle activity using the vector forces as input to the sleep position model aligns with the measured muscle activity (%MVC), with correlation coefficient over 0.7. The proposed method contributes to the vector force distribution understanding and the analysis of musculoskeletal loads during sleep, providing valuable insights for mattress design and evaluation.

Torque and power of knee extensor muscles at individualized isokinetic angular velocities.

Existing isokinetic contractions are characterized using standardized angular velocities, which can induce differing adaptations. Here, we characterized the variation in the isokinetic parameters of knee extensors according to individualized angular velocity (IAV). We performed a cross-sectional study of 19 young, healthy men. We measured the maximum angular velocity (MAV) of concentric knee extension using the isotonic mode of an isokinetic dynamometer. Isometric and isokinetic (at angular velocities corresponding to 100%, 70%, 40%, and 10% of each individual's MAV) knee extensor contractions were performed, and the peak torque and mean power were recorded. Peak torque significantly decreased with increasing IAV (129.42 ± 25.04, 84.37 ± 20.97, and 56.42 ± 16.18 Nm at 40%, 70%, and 100%, respectively), except for isometric contraction (233.36 ± 47.85) and at 10% of MAV (208 ± 48.55). At the mean power, 10% of MAV (74.52 ± 20.84 W) was significantly lower than the faster IAV (176.32 ± 49.64, 161.53 ± 56.55, and 145.95 ± 50.64 W at 40%, 70%, and 100%, respectively), and 100% was significantly lower than 40%. The optimized IAV for isokinetic contraction to improve power output while maintaining torque is 10% to 40% of MAV. IAV may reflect both the velocity and force components of power because individuals do not have the same angular velocity.

Social and psychological features of the mental component of the modern hybrid war against Ukraine

The concept of “hybrid war” and its structural elements, to which we added a mental component, have been considered. It is shown that in the course of such a war, “hybrid” means are used as a specific tool for ensuring the success of military intervention with the simultaneous involvement of the population in the conflict, which is carried out in the covered military territory, and misleads the international community with the aim of its desired influence on the said conflict. A set of tools with which the aggressor country exerts simultaneous influence on the social sphere of the country's life is analyzed. The main components of hybrid forces are informational, ideological, psychological, economic, political, diplomatic, military, technological, energy. It is proposed to add one more component to their composition - mental warfare. It is shown that in the course of the mental war against Ukraine, the enemy set itself the goal of growth to destroy the independent state, the Ukrainian nation and the Ukrainian civilization as a whole and to appropriate its historical heritage. For our research, this is a rather alarming conclusion, so it raises the question of how to confront it. The social and psychological features of waging a psychological war against Ukraine by the russian federation are outlined and analyzed. It is shown that the mental war against our country with the help of hybrid information technologies, which easily influence the consciousness of the population, manipulates it. It is the information activity as a component of the hybrid war that involves a massive psychological impact on the emotional state of a person, the delivery of distorted information to the country’s population. The author’s definition of the concept of “mental war” is formulated as a complex psychological influence by the aggressor country on the consciousness of the population of the country that was attacked, using changes in its national outlook, historical memory, values, national-historical identity in the interests of the aggressor country. Taking into account the peculiarities of the hybrid war and its components, recommendations have been formulated for countering the hybrid aggression of the enemy, which is trying to destroy and change the mentality of Ukrainians for its own use: increase the number of publications and video materials in the information space about the opinions of foreign politicians and experts condemning russian aggression; to create an Institute for the study of war, the work of which will involve both domestic specialists in the field of hybrid warfare and mental warfare, as well as leading scientists of Western countries; to develop the National Doctrine of mental protection of the population in the conditions of a full-scale war against Ukraine.

Influence of tangential hydrodynamic force and inclination angle calculation on the dynamic responses analysis of mooring line based on lumped mass method

Research on mooring line response offers a scientific foundation and technical backing for ocean engineering management. The lumped mass method is commonly used to calculate the dynamic responses of mooring line, pioneered by Walton and Polachek, although it ignores the tangential hydrodynamic force. Nakajima addressed this limitation, but the inclination angle calculation for each element is approximated by the average of the adjacent two nodes. To accurately assess the influences of these factors, this work introduces the tangential hydrodynamic force component into the equation of motion for mooring line dynamic analysis based on the lumped mass method. Meanwhile, we improve the discretization process of lumped mass nodes, analyzing half of the connected spring at both ends of each lumped mass node respectively. The detailed formulation and motion equation for static and dynamic mooring line analysis are presented. The time-domain solution procedure based on the finite difference method is adopted. Finally, different external excitations are applied to calculate the dynamic responses of mooring line. The results show that the top tension increases by about 2%–5% considering the contribution of tangential hydrodynamic force, and the inclination angle calculation significantly influences the dynamic configuration of the mooring line when it is intense change.

Sustainable high-speed milling enhancement of GnP-reinforced titanium nanocomposites under dry environment

No studies have been done on using graphene nanostructure reinforcement material to improve titanium alloy's dry high-speed machining performance, which is crucial for green manufacturing. This study seeks to understand the impact of graphene on improving the high-speed machining of Ti6Al4V (Ti64) nanocomposites. High-density Ti64-based nanocomposite samples with varying amounts of GnPs (0 wt%, 0.6 wt%, 1.2 wt%, and 2.4 wt%) were fabricated using ball milling and HFIHS sustainable technologies. Subsequently, a detailed investigation was conducted to examine the impact of the GnP contents on high-speed milling performance, namely, cutting force components, surface quality, and machined surface and subsurface microhardness. The variable milling parameters include the cutting speed and feed rate. The results showed that the inclusion of GnPs significantly affected the dry high-speed milling of the nanocomposites. The nanocomposites containing 1.2 wt% and 2.4 wt% of GnP-Ti64 exhibited lower cutting forces than the base-Ti64 without GnP (0 wt% Gnp). At a 100 m/min cutting speed, the cutting and feed forces decreased by 35% and 44%, respectively, compared to the base-Ti64 specimens. This is attributed to lower machining friction due to the presence of graphene and TiC (formed due to the reaction of Ti and GnP particles during sintering) at grain boundaries, facilitating material removal and reducing cutting force. Although the nanocomposites had a higher microhardness, they all showed improved surface quality in comparison to the base-Ti64 specimens. For example, at a feed rate of 270 mm/min the nanocomposites contain 0.6 wt%, 1.2 wt%, and 2.4 wt.% GnPs showed roughness reductions of 6%, 27%, and 38%, respectively, compared to the base-Ti64. Regarding surface and sub-surface hardness after machining, the 2.4 wt.% GnP samples showed superior performance in terms of minimal post-milling hardness variations compared to the base hardness of the material. All GnP-Ti64 milled specimens showed considerably improved surface morphology compared to the base-Ti64 machined specimens. Overall, the nanocomposites containing 1.2 wt% and 2.4 wt% GnPs are strong contenders for reducing cutting forces, enhancing surface roughness, and lowering the fluctuations in microhardness, leading to green manufacturing.

Forecasting the cutting force in end milling

The object of this study is the process of end milling, taking into account the discontinuity of the process, simultaneous cutting with several flutes arranged in a spiral, tool runout, and feedback in the elastic machining system, in particular, for the depth of cutting. The subject of the study is the cutting force and identification of its empirical model. During identification, the cutting force coefficient is automatically determined when matching the theoretical and experimental oscillograms of the cutting force component. The reported results related to forecasting the cutting force at end milling are based on a mechanistic approach and involve the process modeling method for forecasting. The simulation uses an algorithm for representing the interaction of the cutter flutes workpiece engagement, based on the scan of the cutter according to the rotation angle coordinate. The algorithm makes it possible to identify empirical coefficients and exponents of the cutting force model based on experimental oscillograms of cutting force components. The built model is implemented in an application program and owing to the representation of the machining system in the form of a closed structural diagram, it allows predicting the elastic displacement, which will determine the actual cutting depth. The developed program under an interactive mode using digital files of experimental cutting force components makes it possible to perform model identification and predict cutting force components with an error of 4.6 %. The adequacy of the algorithms was confirmed by measuring the profile of the machined surface in the places where the cutting mode changed with the feed stopped. The developed simulation algorithm makes it possible to take into account the simultaneous cutting by several flutes arranged in a spiral, the runout of the tool, and the feedback in the elastic machining system, in particular, the depth of cutting

Leidenfrost spheres, projectiles, and model boats: Assessing the drag reduction by superhydrophobic surfaces

Superhydrophobic surfaces are expected to reduce drag on bluff bodies moving in water due to the introduction of a thin air layer around the solid and the resulting partial slip boundary condition. The use of Leidenfrost vapor layers, sustained on the surface of a heated metal body is a reliable method to estimate the maximum drag reduction possible due to such air layers. In the past such an approach was used to estimate the drag reduction on a free-falling heated sphere, in which case the form drag is the lead component of the drag force. Here, we extend this approach to evaluate the effect of the thin gas layers on the hydrodynamic drag of free-falling streamlined projectiles and towed model boats, where the form drag is minimal, and the skin friction drag is the lead component of the drag force. By comparing the drag for streamlined bodies with and without sustained air-layers, we see only incremental drag reductions, for the sub-critical Reynolds number tested herein. The same is true for towed model boats. These results hold both for superhydrophobic surface treatments and Leidenfrost vapor layers. Thus, we concluded that for the investigated range of sub-critical Reynolds numbers, the skin friction drag is less sensitive to the effect of the thin gas layers compared to the form drag. These novel findings have important implications for the practical potential of energy savings using gas layers sustained on superhydrophobic surfaces.

Predictive modelling of residual stress in turning of hard materials using radial basis function network enhanced with principal component analysis

This study proposes a hybrid machine learning (ML) model that combines a radial basis function network (RBFN) with principal component analysis (PCA) to predict residual stress (RS) in the machining process. Higher temperatures and plastic deformation can generate RS conditions in the machining of hard materials, significantly influencing the quality of machined parts, particularly their surface integrity. It is crucial to evaluate and guarantee machining conditions that ensure reliable surface integrity that yields compressive RS conditions. Incorrect parameter settings can lead to poor surface quality, resulting in tensile RS conditions that impact both the quality and lifetime of manufactured products. The methodology involved experimental machining trials for data acquisition, with conditions selected for hard material machining tests conducted using a computer numeric control (CNC) lathe under dry cutting conditions. The cutting force components were measured, and RS was evaluated using X-ray diffraction. A total of 60 trials were conducted. Data preprocessing, including normalization and removal of outliers, was executed before constructing the ML algorithm, leaving 59 valid tests for model training and testing. Separate datasets for training (70 % of the data) and testing (30 %) were randomly selected from the valid experimental tests. PCA enhanced the RBFN model’s generalization by reducing data dimensionality and providing the hidden units parameter. The eigenvectors obtained through PCA served as an efficient initial reference for RBF centroids, highlighting principal directions of variation. The PCA-RBFN model, using a multiquadric radial function, exhibited robust performance in capturing underlying patterns during training and demonstrated balanced results in the tests. In the PCA-RBFN model training stage, it exhibited a coefficient of determination (R2) of 76.66 %, a mean average error (MAE) of 72.15 MPa, and a root mean square error (RMSE) of 84.94 MPa. In the testing stage, the model showed increased metrics, indicating its ability to predict RS values accurately, with an R2 of 80.54 %, MAE of 51.25 MPa, and RMSE of 70.36 MPa. The evaluation between the training and testing stages highlighted the model’s coherence in performance, demonstrating its ability to generalize well to new data. A second comparative analysis was conducted with a conventional RBFN approach using stochastic method for centroid determination. The hybrid PCA-RBFN method exhibited lower computational effort, requiring only three iterations compared to 221,923 iterations in the conventional approach. Despite the conventional approach yielding better metrics in the training stage, it exhibited signs of overfitting, a common concern in machine learning models. The hybrid PCA-RBFN consistently outperformed it regarding generalization and fit during testing. The proposed methodology holds practical implications for the manufacturing industry, allowing the integration of a pre-trained predictive model into machine controllers or smart sensors. The hybrid PCA-RBFN’s computationally lightweight approach also supports incorporating real-time adaptive algorithms into production systems, enabling continuous real-time adjustments to the model factors and weights, thereby improving automated control.

Multi-load effect on the deformation analysis of composite nano reinforced origami sandwich panel

Graphene origami in a copper matrix is used as a composition of core in a sandwich panel between two piezoelectric/piezomagnetic layers. More accurate modeling of the composite sandwich structure is performed using a higher-order model including thickness stretching term. Principle of virtual work is used in order to derive governing equations in terms of resultant components of force and moment as well as electromagnetic loads. The resultant components are derived in higher-order framework with accounting electric and magnetic potentials using the effective material properties of graphene origami in Halpin-Tsai and rule of mixture framework. The deformation/strain/stress components are analytically obtained in terms of thermal, mechanical, electrical, and magnetic loads as well as folding degree and content of graphene origami. Verification is performed for justification of the numerical results. A foldability dependent parametric analysis is presented to show controllability of stress, strain and deformations along the thickness direction.

Identification of dynamic coefficient matrix for drilling process simulations from measured tool geometry, axial force and torque

This paper aims to quantitatively analyze the relationship between forces acting on the tool tip and tool movement during drilling operations. The study encompasses axial and lateral vibrations superimposed on the nominal tool movement, arising from rigid body motion (rotational and axial velocities). Specifically, only forces attributed to the cutting process are considered, excluding considerations of indentation forces around the chisel edge. The research adopts a generalized approach, spanning from tool measurements to establishing the force model. The investigation involves measuring cutting forces and correlating them with the varying rake and inclination angles of the drill’s cutting edges. An analytical model is proposed to describe the distribution of all local force components along drill edges, considering the evolution of forces and geometry. The dynamic coefficient matrix is evaluated by using the identified cutting coefficient and tool geometry. Validation of the proposed methodology is demonstrated through drilling experiments on Ti6Al4V alloy, utilizing three solid carbide drills with distinct geometries. The proposed procedure allows complete identification of the dynamic characteristics from the measurements taken at the entrance stage of hole drilling operation. Moreover, the influence of tool geometry on cutting coefficients and dynamic coefficient matrices are discussed.

Calculating torque, back-EMF, inductance, and unbalanced magnetic force for a hybrid electrical vehicle by in-wheel drive application

To use a Hybrid Excitation Synchronous Machine (HESM) in a hybrid electrical vehicle (HEV), its performance indicators such as back-EMF, inductance and unbalanced magnetic force should be computed preferably by an analytical method. First, the back-EMF is calculated by considering alternate-teeth and all-teeth non-overlapping and overlapping windings. The effects of three types of magnetization patterns including the radial, parallel and Halbach magnetizations on the back-EMF waveform have also been investigated. Then, the self-inductance of the stator and rotor windings, the mutual inductance between the stator and rotor windings, and the mutual inductance between the stator phases are computed. Next, the components of the unbalanced magnetic force (UMF) in the direction of the x and y axes and its amplitude are computed. Moreover, the effects of the magnetization patterns on those magnetic pulls are investigated. To minimize the UMFs, symmetry must be implemented in the excitation sources; therefore, first the stator winding then the permanent magnet and rotor winding are modified in such a way that the UMFs are reduced. Increasing the temperature leads to a weakening of the magnet’s residual flux density, which strongly affects the performance characteristics of the electric machine such as Back-EMF and UMF. Finally, the ratio of the permanent magnet flux to the rotor flux is determined in such a way that the average torque is maximized. In this section, the effects of three magnetization patterns will be investigated.

Wave–structure interaction by a two–way coupling between a fully nonlinear potential flow model and a Navier–Stokes solver

A two-way domain decomposition coupling procedure between a fully nonlinear potential flow model and a Navier–Stokes solver capturing the free surface with a Volume of Fluid method is used to study wave–structure interaction applied to offshore wind turbines. Away from the structure, the large-scale inviscid wave field is modeled by the potential code. Wave generation and absorption in this 3D hybrid model take place in the outer potential domain. The codes exchange data in the region around their common boundaries. Through the two-way coupling, waves propagate in and out of the viscous subdomain, making the hybrid algorithm suitable to study wave diffraction on marine structures, while keeping the viscous subdomain small. Each code uses its own mesh and time step. Subdomains are overlapping, therefore continuity conditions on velocity and free surface have to be verified on two distinct coupling surfaces at any time. Parallel implementation with communications between the models relying on the Message Passing Interface library allows calculations on large spatial and temporal scales. The coupling algorithm is first tested for regular nonlinear waves and then applied to simulate wave loads exerted on a vertical monopile in 3D. Attention is paid to the high-order components of the horizontal force.

A general framework for the design of efficient passive pitch systems

Mitigating the impact of variable inflow conditions is critical for a wide range of engineering systems such as drones or wind and tidal turbines. Passive control systems are of increasing interest for their inherent reliability, but a mathematical framework to aid the design of such systems is currently lacking. To this end, in this paper a two-dimensional rigid foil that passively pitches in response to changes in the flow velocity is considered. Both an analytical quasi-steady model and a dynamic low-order model are developed to investigate the pivot point position that maximizes unsteady load mitigation. The paper focuses on streamwise gusts, but the proposed methodology would apply equally to any change in the inflow velocity (speed and/or direction). The quasi-steady model shows that the force component in any arbitrary direction can be kept constant if the pivot lies on a particular line, and that the line coordinates depend on the gust and the foil characteristics. The dynamic model reveals that the optimum distance of the pivot location from the foil increases with decreasing inertia. For a foil at small angles of incidence, the optimum pivot point is along the extended chord line. This knowledge provides a methodology to design optimum passively pitching systems for a plethora of applications, including flying and swimming robotic vehicles, and provides new insights into the underlying physics of gust mitigation.