Dynamics

In this paper, we consider the flow of a nanofluid in an enclosed lid-driven cavity using a single-phase model. Two cases are considered: one in which the top and bottom walls are kept at adiabatic conditions, and a second case in which the left- and right-side walls are kept in adiabatic conditions. The impact of different viscosity models on the mixed convection heat transfer is examined, and numerical methods are used to obtain solutions for the Navier–Stokes equations for various parameter ranges. Using our robust methods, we are able to obtain novel solutions for large Reynolds numbers and very small Richardson numbers. Using water as the base fluid and aluminium oxide nanoparticles, our results suggest that heat transfer enhancement occurs with increasing particle concentration and decreasing Richardson numbers. There are also significant differences depending on the viscosity model used in terms of the impact of reducing corner recirculation regions in the cavity. Full article

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of the total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives on the development of onboard neuromorphic computers that mimic the operation of a biological brain using the nonlinear–dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs are a semi-classical technology, their technical simplicity and low cost compared to quantum computers make them ideally suited for applications in autonomous AI systems. Providing a perspective on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing. Full article

The NASA’s Artemis project intends to bring humans back to the Moon in the next decade. A key element of the project will be the Lunar Gateway, a space station placed in a peculiar, near rectilinear Halo orbit in the vicinity of a collinear libration point in the Earth–Moon system. This study focuses on the high-fidelity description of the relative orbit dynamics of a chaser spacecraft with respect to the Gateway, as well as on the design of a proper orbit control strategy for rendezvous maneuvers. A novel formulation of the Battin–Giorgi approach is introduced, in which the reference orbit is that traveled by the Gateway, i.e., it is a highly non-Keplerian, perturbed orbit. The modified Battin–Giorgi approach allows for the description of a relative orbit motion with no restrictive assumption, while including all the relevant orbit perturbations on both the chaser and the Gateway. Moreover, nonlinear hybrid predictive control is introduced as a feedback guidance strategy. This new technique is shown to outperform the classical, well-established feedback linearization in terms of success rate and accuracy on the final conditions. Moreover, a Monte Carlo analysis confirms that hybrid predictive control is also effective in the presence of the temporary unavailability of propulsion or thrust misalignment. Full article

The dynamic behaviour of a spring-mounted symmetrical NACA0012 wing in a freestream flow of air is studied in the pre-stall region, over 0° to 12° angles of incidence. The primary aim of this work is for use within the automotive sector to reduce drag and fuel emissions. However, this work will also be of interest in the motorsport sector to improve performance, and also have some applications within the aerospace and renewable energy sectors. The general operation of the concept has previously been verified at these low angles in the pre-stall region with that of a theoretical estimation using finite and infinite wings. This paper provides a numerical solution of the same problem and is compared with the previous experimentation. At these low angles, the computations yield a dynamic response settling into a static equilibrium. The stable solutions match the start of a steady regime well, when compared with the experiment. The trends are also comparable with the experiment, but the velocities at which they occur are underestimated in the computation. The computations demonstrate a drag reduction of 59% when compared to a fixed wing, whereas the lift remains stable at a near constant value with increasing wind speed. Thence, downforce is maintained whilst drag is reduced, which will facilitate higher speeds on the straight whilst maintaining vehicle direction stability. Limitations to this proof-of-concept work are highlighted and future development work is suggested to achieve even further increases in performance. Full article

A finite element method is employed to examine the impact of a magnetic field on the development of plaque in an artery with stenotic bifurcation. Consistent with existing literature, blood flow is characterized as a Newtonian fluid that is stable, incompressible, biomagnetic, and laminar. Additionally, it is assumed that the arterial wall is linearly elastic throughout. The hemodynamic flow within a bifurcated artery, influenced by an asymmetric magnetic field, is described using the arbitrary Lagrangian–Eulerian (ALE) method. This technique incorporates the fluid–structure interaction coupling. The nonlinear system of partial differential equations is discretized using a stable P2P1 finite element pair. To solve the resulting nonlinear algebraic equation system, the Newton-Raphson method is employed. Magnetic fields are numerically modeled, and the resulting displacement, velocity magnitude, pressure, and wall shear stresses are analyzed across a range of Reynolds numbers (Re = 500, 1000, 1500, and 2000). The numerical analysis reveals that the presence of a magnetic field significantly impacts both the displacement magnitude and the flow velocity. In fact, introducing a magnetic field leads to reduced flow separation, an expanded recirculation area near the stenosis, as well as an increase in wall shear stress. Full article

Freight wagons in Europe have used Y25 bogies since the 1960s. Although very cost-effective, Y25 suffers from intrinsic limitations due to its architecture and running behaviour. This study introduces an innovative lightweight bogie, named 4L bogie, aimed at removing those limitations as well as improving running dynamics and track friendliness. This task was particularly challenging as the high ratio between laden and tare weight (up to 5:1) forced us to use a non-conventional suspension system and an innovative architecture of frame, reducing the mass by about 15% and the yaw moment of inertia by about 30% with respect to the Y25 bogie. Maintenance issues were addressed by reducing the number of components and easing overhaul, while the new design was validated from both the structural and the running dynamics point of view, assessing its interaction with the track in terms of stability, curving behaviour and the vertical response of the 4L bogie. Stability was improved by about 20% even in empty conditions and high conicity at the wheel/rail contact. Vertical dynamic force on a straight track, evaluated according to the Ride Force Count metric, and wear behaviour on sharp and mild curves were considerably reduced, leading to an improved track friendliness of the bogie. Full article

The development of dynamic plasticity models with accounting of interplay between several plasticity mechanisms is an urgent problem for the theoretical description of the complex dynamic loading of materials. Here, we consider dynamic plastic relaxation by means of the combined action of dislocations and phase transitions using Al-Cu solid solutions as the model materials and uniaxial compression as the model loading. We propose a simple and robust theoretical model combining molecular dynamics (MD) data, theoretical framework and machine learning (ML) methods. MD simulations of uniaxial compression of Al, Cu and Al-Cu solid solutions reveal a relaxation of shear stresses due to a combination of dislocation plasticity and phase transformations with a complete suppression of the dislocation activity for Cu concentrations in the range of 30–80%. In particular, pure Al reveals an almost complete phase transition from the FCC (face-centered cubic) to the BCC (body-centered cubic) structure at a pressure of about 36 GPa, while pure copper does not reveal it at least till 110 GPa. A theoretical model of stress relaxation is developed, taking into account the dislocation activity and phase transformations, and is applied for the description of the MD results of an Al-Cu solid solution. Arrhenius-type equations are employed to describe the rates of phase transformation. The Bayesian method is applied to identify the model parameters with fitting to MD results as the reference data. Two forward-propagation artificial neural networks (ANNs) trained by MD data for uniaxial compression and tension are used to approximate the single-valued functions being parts of constitutive relation, such as the equation of state (EOS), elastic (shear and bulk) moduli and the nucleation strain distance function describing dislocation nucleation. The developed theoretical model with machine learning can be further used for the simulation of a shock-wave structure in metastable Al-Cu solid solutions, and the developed method can be applied to other metallic systems, including high-entropy alloys. Full article

This paper focuses on the numerical study of spherical particle sedimentation, taking into account hydrodynamic interactions. Infinite impulse response (IIR) digital filters, specially tailored to solve the sedimentation dynamics, were used in the present study to numerically solve the coupled ordinary differential equations with the time-dependent coefficients of the problem. Hydrodynamic interactions are modeled using the Rotne–Prager–Yamakawa (RPY) approximation, to which a correction is made to better account for short-range interactions. In order to validate both the proposed numerical resolution method and the RPY correction, this paper begins with the study of two interacting spherical particle sedimentation methods. Comparisons with previously published analytical or numerical results confirm the relevance of the present approach. Full article

Some characteristics of stationary flows of the Sawada–Kotera system lend themselves to generalization, producing a large class of separable Lagrangian systems with two degrees of freedom. All of these systems come in couples that have the same equations of motion, although they are not related by a gauge transform. Some nonpolynomial examples are provided. Full article

Fluidized beds are pivotal in the process industry and chemical engineering, with Computational Fluid Dynamics (CFD) playing a crucial role in their design and optimization. Challenges in CFD modeling stem from the scarcity or inconsistency of experimental data for validation, along with the uncertainties introduced by numerous parameters and assumptions across different CFD codes. This study navigates these complexities by comparing simulation results from the open-source MFIX and OpenFOAM, and the commercial ANSYS FLUENT, against experimental data. Utilizing a Eulerian–Eulerian framework and the kinetic theory of granular flow (KTGF), the investigation focuses on solid-phase properties through the classical drag laws of Gidaspow and Syamlal–O’Brien across varied parameters. Findings indicate that ANSYS Fluent, MFiX, and OpenFOAM can achieve reasonable agreement with experimental benchmarks, each showcasing distinct strengths and weaknesses. The study also emphasizes that both the Syamlal–O’Brien and Gidaspow drag models exhibit reasonable agreement with experimental benchmarks across the examined CFD codes, suggesting a moderated sensitivity to the choice of drag model. Moreover, analyses were also carried out for 2D and 3D simulations, revealing that the dimensional approach impacts the predictive accuracy to a certain extent, with both models adapting well to the complexities of each simulation environment. The study highlights the significant influence of restitution coefficients on bed expansion due to their effect on particle–particle collisions, with a value of 0.9 deemed optimal for balancing simulation accuracy and computational efficiency. Conversely, the specularity coefficient, impacting particle–wall interactions, exhibits a more subtle effect on bed dynamics. This finding emphasizes the critical role of carefully choosing these coefficients to effectively simulate the nuanced behaviors of fluidized beds. Full article

Organic Rankine Cycle (ORC)–based small-scale power plants are becoming a promising instrument in the recent drive to utilize renewable sources and reduce carbon emissions. But the effectiveness of such systems is limited by the low efficiency of gas expanders, which are the main part of an ORC system. Limaçon-based expansion machines with a fast inlet control valve have great prospects as they could potentially offer efficiencies over 50%. However, the lack of a highly reliable and significantly fast control valve is hindering its possible application. In this paper, a push–pull solenoid valve is optimized using a stochastic optimization technique to provide a fast response. The optimization yields about 56–58% improvement in overall valve response. A performance comparison of the initial and optimized valves applied to a limaçon expander thermodynamic model is also presented. Additionally, the sensitivity of the valve towards a changing inlet pressure and expander rotor velocity is analyzed to better understand the effectiveness of the valve and provide clues to overall performance improvement. Full article

High-pressure gas quenching is widely used in the metals industry during the heat treatment processing of steel specimens to improve their material properties. In a gas quenching process, a preheated austenised metal specimen is rapidly cooled with a gas such as nitrogen, helium, etc. The resulting microstructure relies on the temporal and spatial thermal history during the quenching. As a result, the corresponding material properties such as hardness are achieved. Challenges reside with the selection of the proper process parameters. This research focuses on the heat treatment of steel sample batches. The gas quenching process is fundamentally investigated in experiments and numerical simulations. Experiments are carried out to determine the heat transfer coefficient and the cooling curves as well as the local flow fields. Quenched samples are analyzed to derive the material hardness. CFD and FEM models numerically determine the conjugate heat transfer, flow behavior, cooling curve, and material hardness. In a novel approach, the experimental and simulation results are adopted to train artificial neural networks (ANNs), which allow us to predict the required process parameters for a targeted material property. The steels 42CrMo4 (1.7225) and 100Cr6 (1.3505) are investigated, nitrogen is the quenching gas, and geometries such as a disc, disc with a hole and ring are considered for batch series production. Full article

In chaos control, one usually seeks to stabilize the unstable periodic orbits (UPOs) that densely inhabit the attractors of many chaotic dynamical systems. These orbits collectively play a significant role in determining the dynamics and properties of chaotic systems and are said to form the skeleton of the associated attractors. While UPOs are insightful tools for analysis, they are naturally unstable and, as such, are difficult to find and computationally expensive to stabilize. An alternative to using UPOs is to approximate them using cupolets. Cupolets, a name derived from chaotic, unstable, periodic, orbit-lets, are a relatively new class of waveforms that represent highly accurate approximations to the UPOs of chaotic systems, but which are generated via a particular control scheme that applies tiny perturbations along Poincaré sections. Originally discovered in an application of secure chaotic communications, cupolets have since gone on to play pivotal roles in a number of theoretical and practical applications. These developments include using cupolets as wavelets for image compression, targeting in dynamical systems, a chaotic analog to quantum entanglement, an abstract reducibility classification, a basis for audio and video compression, and, most recently, their detection in a chaotic neuron model. This review will detail the historical development of cupolets, how they are generated, and their successful integration into theoretical and computational science and will also identify some unanswered questions and future directions for this work. Full article

Thermo-vortices (bright spots, blobs, swirls) in cosmic fluids (planetary atmospheres, or even black hole accretion disks) are sometimes observed as clustered into quasi-symmetrical quasi-stationary groups but conceptualized in models as autonomous items. We demonstrate—using the (analytical) Sharp Boundaries Evolution Method and a generic model of a thermo-vorticial field in a rotating “thin” fluid layer in a spacetime that may be curved or flat—that these thermo-vortices may be not independent but represent interlinked parts of a single, coherent, multi-petal macro-structure. This alternative conceptualization may influence the designs of numerical models and image-reconstruction methods. Full article

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R²), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures. Full article

A semilinear quadratic equation of the form Aij(x)uij=Bi(x,u)ui+F(x,u) defines a metric Aij; therefore, it is possible to relate the Lie point symmetries of the equation with the symmetries of this metric. The Lie symmetry conditions break into two sets: one set containing the Lie derivative of the metric wrt the Lie symmetry generator, and the other set containing the quantities Bi(x,u),F(x,u). From the first set, it follows that the generators of Lie point symmetries are elements of the conformal algebra of the metric Aij, while the second set serves as constraint equations, which select elements from the conformal algebra of Aij. Therefore, it is possible to determine the Lie point symmetries using a geometric approach based on the computation of the conformal Killing vectors of the metric Aij. In the present article, the nonlinear Poisson equation Δgu−f(u)=0 is studied. The metric defined by this equation is 1 + 2 decomposable along the gradient Killing vector ∂t. It is a conformally flat metric, which admits 10 conformal Killing vectors. We determine the conformal Killing vectors of this metric using a general geometric method, which computes the conformal Killing vectors of a general 1+(n−1) decomposable metric in a systematic way. It is found that the nonlinear Poisson equation Δgu−f(u)=0 admits Lie point symmetries only when f(u)=ku, and in this case, only the Killing vectors are admitted. It is shown that the Noether point symmetries coincide with the Lie point symmetries. This approach/method can be used to study the Lie point symmetries of more complex equations and with more degrees of freedom. Full article

The emergence of electric vehicles has brought new issues such as the problem of rolling element bearings (REBs) operating at high speeds. Losses due to these components in mechanical transmissions are a key issue and must therefore be taken into account right from the design stage of these systems. Among these losses, the one induced by the motion of rolling elements, known as drag loss, becomes predominant in high-speed REBs. Although an experimental approach is still possible, it is difficult to isolate this loss in order to study it properly. A numerical approach based on CFD is therefore a possible way forward, even if other issues arise. The aim of this article is to study the ability of such an approach to correctly estimate the drag coefficient associated with the motion of rolling elements. The influence of the numerical domain extension, the mesh refinement, the simplification of the ring shape, and the presence of the cage on the values of the drag coefficient is presented. While it seems possible to compromise on the calculation domain and mesh size, it appears that the other parameters must be taken into account as much as possible to obtain realistic results. Full article

Characteristic dynamics in lava flows, such as the formation processes of lava levees, toe-like tips, and overlapped structures, were reproduced successfully through numerical simulation using the smoothed particle hydrodynamics (SPH) method. Since these specific phenomena have a great influence on the flow direction of lava flows, it is indispensable to elucidate them for accurate predictions of areas where lava strikes. At the first step of this study, lava was expressed using a molten metal with known physical properties. The computational results showed that levees and toe-like tips formed at the fringe of the molten metal flowing down on a slope, which appeared for actual lava flows as well. The dynamics of an overlapped structure formation were also simulated successfully; therein, molten metal flowed down, solidified, and changed the surface shape of the slope, and the second molten metal flowed over the changed surface shape. It was concluded that the computational model developed in this study using the SPH method is applicable for simulating and clarifying lava flow phenomena. Full article

This paper discusses the auto-correlation functions of chaotic binary sequences obtained by a one-dimensional chaotic map and two binary functions. The two binary functions are alternately used to obtain a binary sequence from a chaotic real-valued sequence. We consider two similar methods and give the theoretical auto-correlation functions of the new binary sequences, which are expressed by the auto-/cross-correlation functions of the two chaotic binary sequences generated by a single binary function. Furthermore, some numerical experiments are performed to confirm the validity of the theoretical auto-correlation functions. Full article