Thessa-Carina Bauer, Elke Bradt, Sabine Hild
et al.
The flow behavior of fluids can be characterized by rheology and is especially used in the field of polymeric materials. This study focused on characterizing cerebrospinal fluid (CSF) of patients who developed hydrocephalus after subarachnoid hemorrhage (SAH) with rheology. Samples were drawn from an external ventricular drainage (EVD) at four pre-defined time points after the initial hemorrhage. The CSF samples were analyzed using a rotational rheometer with a double gap geometry. In addition to the characterization of viscoelastic parameters, the cumulative storage factor was calculated to determine the interactions in the fluid. In order to investigate the temperature dependence of the CSF properties, the oscillatory measurements were implemented at certain temperatures that simulated specific conditions, such as 5 °C, at which temperature the CSF samples were stored; 35 °C for hypothermic conditions; 37 °C for physiologic conditions; and 40 °C for elevated body temperature. The overall goal was to evaluate whether rheology-based parameters may help in the prediction of shunt dependence for post-hemorrhagic hydrocephalus patients. For this aim, rheological parameters were correlated to certain laboratory parameters, such as erythrocyte and leukocyte count, glucose, lactate, and total protein concentration.
Thermodynamics, Descriptive and experimental mechanics
Eirini Zanni, Ioannis Stavridis, Elias Zacharogiannis
et al.
<b>Background/Objectives</b>: This study aimed to examine the post-activation performance enhancement effects of bilateral horizontal drop jumps (BHDJs) on 30 m sprint and countermovement jump (CMJ) performance, as well as in sprint mechanical and kinematics characteristics. <b>Methods</b>: Fourteen young sprinters (nine boys and five girls) completed both an experimental condition (EC) and a control condition (CC). The EC consisted of five BHDJs performed at each participant’s individually determined optimal drop height, whereas in the CC, no exercise has been performed. <b>Results</b>: The findings revealed no significant (<i>p</i> > 0.05) interactions for CMJ and time to 30 m. Significant increases in 5 m split times were observed across all segments in the CC, as well as in the initial 5 m segment in the EC. Regarding sprint mechanics, a significant interaction was found in the effectiveness of horizontal force application (−2.42% in CC vs. −0.33% in EC). Step frequency demonstrated significant interaction in the 5–10 m segment (−1.79% in CC vs. 1.20% in EC) and decreased significantly in the 15–20 m segment in the CC (−2.03% in CC vs. −1.85% in EC). <b>Conclusions</b>: In conclusion, performance parameters reduced under the CC, whereas the BHDJ intervention stabilized these parameters or exhibited smaller performance variations than in the CC.
Mechanics of engineering. Applied mechanics, Descriptive and experimental mechanics
Flow resistance is a critical determinant of the efficiency and economics of a slurry pipeline. This study aims to reduce pipeline resistance by integrating a swirler to enhance particle suspension. The variation laws of slurry conveying resistance with and without a swirler under various conveying conditions were investigated. Whether a swirler is present or not, the conveying resistance increases with an increase in the conveying velocity (1~6 m/s), volume concentration (10~50%), particle diameter (0.1~5 mm), particle density (1100~1500 kg/m<sup>3</sup>), and pipe inclination (0~90°). It decreases with an increase in the pipe diameter (50~200 mm). A moderate swirling intensity reduces the resistance in high-velocity large-particle slurry transport. Considering the swirling flow characteristics in the conveying pipeline, resistance loss calculation models accounting for these characteristics were established for different flow states of the slurry in the pipeline. Taking into account the swirling characteristics in the conveying pipeline, a calculation model for resistance loss of the slurry in different flow states within the pipeline was established. The average error between this model and the experimental results was 9.04%.
Thermodynamics, Descriptive and experimental mechanics
Ryosuke Karashima, Shintaro Kishimoto, Takuya Ibara
et al.
<b>Background:</b> The relationship between varus thrust (VT) during gait and static limb alignment on radiography in knee osteoarthritis (OA) remains unclear. Therefore, the present study investigated the association between the tibial inclination angle (TA), which was noninvasively measured from the body surface, and radiographic parameters. In Addition, this study analyzed how TA changes under different loading conditions (<i>Δ</i>TA) relate to VT acceleration (VTA) during early stance using an inertial measurement unit (IMU) sensor. <b>Methods:</b> Nineteen female patients (mean age: 63.5 ± 8.6 years) with knee OA or medial meniscus injury were included. The TA was defined as the angle between the tibial mechanical axis and a vertical line from the floor, which was measured in standardized standing and supine positions. The <i>Δ</i>TA was calculated as the difference between these positions. To assess lower limb alignment, the femorotibial angle (FTA) and joint line convergence angle (JLCA) were measured. The VTA was measured using IMU sensors on the thigh and tibia, and the differences between lateral and medial VTA were defined as femoral and tibial <i>Δ</i>VTA, respectively. Spearman’s correlation coefficient and linear regression were used for analysis. <b>Results:</b> The standing TA was significantly correlated with the FTA (<i>ρ</i> = 0.47, <i>p</i> = 0.04) and JLCA (<i>ρ</i> = 0.80, <i>p</i> < 0.01). The <i>Δ</i>TA was significantly associated with femoral <i>Δ</i>VTA (<i>β</i> = 0.70, <i>p</i> < 0.01) and tibial <i>Δ</i>VTA (<i>β</i> = 0.67, <i>p</i> < 0.01). <b>Conclusions:</b> Surface-measured TA reflects radiographic alignment. The <i>Δ</i>TA also captures dynamic instability not explained by static measures, suggesting its potential utility as an assessment indicator, although further validation is warranted.
Mechanics of engineering. Applied mechanics, Descriptive and experimental mechanics
In this work, a high-order modal discontinuous Galerkin (dG) method is employed to solve the Euler equations using entropy variables. Entropy conservation and stability are ensured at the spatial semi-discrete level through entropy-conserving/stable numerical fluxes and the over-integration technique. For time integration, linearly implicit Rosenbrock-type Runge–Kutta schemes are used. However, since these schemes are not provably entropy-conserving/stable, their use to predict unsteady flows may lead to solutions that lack the desired entropy properties. To address this issue, a relaxation technique is applied to enforce entropy conservation or stability at the fully discrete level. The accuracy, conservation/stability properties and robustness of the fully-discrete scheme equipped with the relaxation technique are assessed through the following numerical experiments: (1) the isentropic vortex, (2) the Kelvin-Helmholtz instability, (3) the Taylor–Green vortex.
Thermodynamics, Descriptive and experimental mechanics
This paper reviews a paper from 1906 by J. Henri Poincaré on statistical mechanics with a background in his earlier work and notable connections to J. Willard Gibbs. Poincaré's paper presents important ideas that are still relevant for understanding the need for probability in statistical mechanics. Poincaré understands the foundations of statistical mechanics as a many-body problem in analytical mechanics (reflecting his 1890 monograph on The Three-Body Problem and the Equations of Dynamics) and possibly influenced by Gibbs independent development published in chapters in his 1902 book, Elementary Principles in Statistical Mechanics. This dynamical systems approach of Poincaré and Gibbs provides great flexibility including applications to many systems besides gasses. This foundation benefits from close connections to Poincaré's earlier work. Notably, Poincaré had shown (e.g. in his study of non-linear oscillators) that Hamiltonian dynamical systems display sensitivity to initial conditions separating stable and unstable trajectories. In the first context it precludes proving the stability of orbits in the solar system, here it compels the use of ensembles of systems for which the probability is ontic and frequentist and does not have an a priori value. Poincaré's key concepts relating to uncertain initial conditions, and fine- and coarse-grained entropy are presented for the readers' consideration. Poincaré and Gibbs clearly both wanted to say something about irreversibility, but came up short.
Thermodynamics with internal variables is a common approach in continuum mechanics to model inelastic (i.e., non-equilibrium) material behavior. While this approach is computationally and theoretically attractive, it currently lacks a well-established statistical mechanics foundation. As a result, internal variables are typically chosen phenomenologically and lack a direct link to the underlying physics which hinders the predictability of the theory. To address these challenges, we propose a machine learning approach that is consistent with the principles of statistical mechanics and thermodynamics. The proposed approach leverages the following techniques (i) the information bottleneck (IB) method to ensure that the learned internal variables are functions of the microstates and are capable of capturing the salient feature of the microscopic distribution; (ii) conditional normalizing flows to represent arbitrary probability distributions of the microscopic states as functions of the state variables; and (iii) Variational Onsager Neural Networks (VONNs) to guarantee thermodynamic consistency and Markovianity of the learned evolution equations. The resulting framework, called IB-VONNs, is tested on two problems of colloidal systems, governed at the microscale by overdamped Langevin dynamics. The first one is a prototypical model for a colloidal particle in an optical trap, which can be solved analytically, and thus ideal to verify the framework. The second problem is a one-dimensional phase-transforming system, whose macroscopic description still lacks a statistical mechanics foundation under general conditions. The results in both cases indicate that the proposed machine learning strategy can indeed bridge statistical mechanics and thermodynamics with internal variables away from equilibrium.
Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz
et al.
Recovering dynamical equations from observed noisy data is the central challenge of system identification. We develop a statistical mechanics approach to analyze sparse equation discovery algorithms, which typically balance data fit and parsimony via hyperparameter tuning. In this framework, statistical mechanics offers tools to analyze the interplay between complexity and fitness similarly to that of entropy and energy in physical systems. To establish this analogy, we define the hyperparameter optimization procedure as a two-level Bayesian inference problem that separates variable selection from coefficient inference and enables the computation of the posterior parameter distribution in closed form. Our approach provides uncertainty quantification, crucial in the low-data limit that is frequently encountered in real-world applications. A key advantage of employing statistical mechanical concepts, such as free energy and the partition function, is to connect the large data limit to thermodynamic limit and characterize the sparsity- and noise-induced phase transitions that delineate correct from incorrect identification. We thus provide a method for closed-loop inference, estimating the noise in a given model and checking if the model is tolerant to that noise amount. This perspective of sparse equation discovery is versatile and can be adapted to various other equation discovery algorithms.
Saïf ed-Dîn Fertahi, Tarik Belhadad, Anass Kanna
et al.
This critical review delves into the impact of Computational Fluid Dynamics (CFD) modeling techniques, specifically 2D, 2.5D, and 3D simulations, on the performance and vortex dynamics of Darrieus turbines. The central aim is to dissect the disparities apparent in numerical outcomes derived from these simulation methodologies when assessing the power coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>p</mi></msub></semantics></math></inline-formula>) within a defined velocity ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>) range. The examination delves into the prevalent turbulence models shaping <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>p</mi></msub></semantics></math></inline-formula> values, and offers insightful visual aids to expound upon their influence. Furthermore, the review underscores the predominant rationale behind the adoption of 2D CFD modeling, attributed to its computationally efficient nature vis-à-vis the more intricate 2.5D or 3D approaches, particularly when gauging the turbine’s performance within the designated <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula> realm. Moreover, the study meticulously curates a compendium of findings from an expansive collection of over 250 published articles. These findings encapsulate the evolution of pivotal parameters, including <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>p</mi></msub></semantics></math></inline-formula>, moment coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>m</mi></msub></semantics></math></inline-formula>), lift coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>l</mi></msub></semantics></math></inline-formula>), and drag coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>d</mi></msub></semantics></math></inline-formula>), as well as the intricate portrayal of velocity contours, pressure distributions, vorticity patterns, turbulent kinetic energy dynamics, streamlines, and Q-criterion analyses of vorticity. An additional focal point of the review revolves around the discernment of executing 2D parametric investigations to optimize Darrieus turbine efficacy. This practice persists despite the emergence of turbulent flow structures induced by geometric modifications. Notably, the limitations inherent to the 2D methodology are vividly exemplified through compelling CFD contour representations interspersed throughout the review. Vitally, the review underscores that gauging the accuracy and validation of CFD models based solely on the comparison of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>p</mi></msub></semantics></math></inline-formula> values against experimental data falls short. Instead, the validation of CFD models rests on time-averaged <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>p</mi></msub></semantics></math></inline-formula> values, thereby underscoring the need to account for the intricate vortex patterns in the turbine’s wake—a facet that diverges significantly between 2D and 3D simulations. In a bid to showcase the extant disparities in CFD modeling of Darrieus turbine behavior and facilitate the selection of the most judicious CFD modeling approach, the review diligently presents and appraises outcomes from diverse research endeavors published across esteemed scientific journals.
Thermodynamics, Descriptive and experimental mechanics
Alex G. Kuchumov, Anastasiya Makashova, Sergey Vladimirov
et al.
The complicated interaction between a fluid flow and a deformable structure is referred to as fluid–structure interaction (FSI). FSI plays a crucial role in the functioning of the aortic valve. Blood exerts stresses on the leaflets as it passes through the opening or shutting valve, causing them to distort and vibrate. The pressure, velocity, and turbulence of the fluid flow have an impact on these deformations and vibrations. Designing artificial valves, diagnosing and predicting valve failure, and improving surgical and interventional treatments all require the understanding and modeling of FSI in aortic valve dynamics. The most popular techniques for simulating and analyzing FSI in aortic valves are computational fluid dynamics (CFD) and finite element analysis (FEA). By studying the relationship between fluid flow and valve deformations, researchers and doctors can gain knowledge about the functioning of valves and possible pathological diseases. Overall, FSI is a complicated phenomenon that has a great impact on how well the aortic valve works. Aortic valve diseases and disorders can be better identified, treated, and managed by comprehending and mimicking this relationship. This article provides a literature review that compiles valve reconstruction methods from 1952 to the present, as well as FSI modeling techniques that can help advance valve reconstruction. The Scopus, PubMed, and ScienceDirect databases were used in the literature search and were structured into several categories. By utilizing FSI modeling, surgeons, researchers, and engineers can predict the behavior of the aortic valve before, during, and after surgery. This predictive capability can contribute to improved surgical planning, as it provides valuable insights into hemodynamic parameters such as blood flow patterns, pressure distributions, and stress analysis. Additionally, FSI modeling can aid in the evaluation of different treatment options and surgical techniques, allowing for the assessment of potential complications and the optimization of surgical outcomes. It can also provide valuable information on the long-term durability and functionality of prosthetic valves. In summary, fluid–structure interaction modeling is an effective tool for predicting the outcomes of aortic valve surgery. It can provide valuable insights into hemodynamic parameters and aid in surgical planning, treatment evaluation, and the optimization of surgical outcomes.
Thermodynamics, Descriptive and experimental mechanics
Jose Pablo Folch, James Odgers, Shiqiang Zhang
et al.
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be adaptable to such cases, since we can model the reactions of interest as expensive black-box functions. Sometimes, the cost of this black-box functions can be separated into two parts: (a) the cost of the experiment itself, and (b) the cost of changing the input parameters. In this short paper, we extend the SnAKe algorithm to deal with both types of costs simultaneously. We further propose extensions to the case of a maximum allowable input change, as well as to the multi-objective setting.
The effect of the excitation frequency of synthetic jet actuators on the mean jet velocity issuing from an array of circular orifices is investigated experimentally, focusing on the acoustic excitation characteristics of the actuator’s cavity. Two cavity configurations are considered. In the first configuration, synthetic jets are generated by exciting a single, large cavity having an array of sixteen orifices via sixteen piezoelectric elements. In the second configuration, the cavity volume of the first configuration is divided into eight isolated compartments, each with two orifices and two piezoelectric elements. Several distinct resonant peaks were observed in the frequency response of the synthetic jet actuator built with a single large-aspect-ratio cavity, whereas the case of compartmentalised cavities exhibited a single resonant peak. Acoustic simulations of the large-aspect-ratio-cavity volume showed that the multiple peaks in its frequency response correspond to the acoustic standing-wave mode shapes of the cavity. Due to its large aspect ratio, several acoustic mode shapes coexist in the excitation frequency range aside from the Helmholtz resonance frequency. When the actuator’s cavity volume is compartmentalised, only the Helmholtz resonance frequency is observed within the excitation frequency range.
Thermodynamics, Descriptive and experimental mechanics
Ahmed A. Sheikh Al-Shabab, Bojan Grenko, Dimitrios Vitlaris
et al.
The flow field development through a simplified shock absorber orifice geometry is investigated using a single phase Large Eddy Simulation. Hydraulic oil is used as the working fluid with a constant inlet velocity and an open top boundary to allow the study to focus on the free shear layer and the flow development in the vicinity of the main orifice. The flow field is validated using standard mixing layer dynamics. The impact of the orifice shape is discussed with regards to the initial free shear layer growth, boundary layer development and the potential appearance of cavitation bubbles. Observations are made regarding the presence of flow field disturbances upstream of and through the orifice, thereby, leading to a notable turbulence intensity level in those regions.
Thermodynamics, Descriptive and experimental mechanics
This work employs single-mode equations to study convection and double-diffusive convection in a porous medium where the Darcy law provides large-scale damping. We first consider thermal convection with salinity as a passive scalar. The single-mode solutions resembling steady convection rolls reproduce the qualitative behavior of root-mean-square and mean temperature profiles of time-dependent states at high Rayleigh numbers from direct numerical simulations (DNS). We also show that the single-mode solutions are consistent with the heat-exchanger model that describes well the mean temperature gradient in the interior. The Nusselt number predicted from the single-mode solutions exhibits a scaling law with Rayleigh number close to that followed by exact 2D steady convection rolls, although large aspect ratio DNS results indicate a faster increase. However, the single-mode solutions at a high wavenumber predict Nusselt numbers close to the DNS results in narrow domains. We also employ the single-mode equations to analyze the influence of active salinity, introducing a salinity contribution to the buoyancy, but with a smaller diffusivity than the temperature. The single-mode solutions are able to capture the stabilizing effect of an imposed salinity gradient and describe the standing and traveling wave behaviors observed in DNS. The Sherwood numbers obtained from single-mode solutions show a scaling law with the Lewis number that is close to the DNS computations with passive or active salinity. This work demonstrates that single-mode solutions can be successfully applied to this system whenever periodic or no-flux boundary conditions apply in the horizontal.
Thermodynamics, Descriptive and experimental mechanics
Bayesian mechanics is a new approach to studying the mathematics and physics of interacting stochastic processes. Here, we provide a worked example of a physical mechanics for classical objects, which derives from a simple application thereof. We summarise the current state of the art of Bayesian mechanics in doing so. We also give a sketch of its connections to classical chaos, owing to a particular $\mathcal{N}=2$ supersymmetry.
A recently introduced numerical model to calculate relaxation rates and relaxation time of superconductors is revisited. Relaxation time is needed to reorganise, after a disturbance, the electron system of the superconductor to new dynamic equilibrium. The idea is to extend this model to evaluation of experimental results reported in the literature for critical current density, JCrit, for levitation height and force, for stability functions, persistent currents, and, in principle, for a check of all observables that depend on JCrit. It is only after completion of the relaxation process that experimental, JCrit-dependent results can be verified uniquely. In its second part, using the same numerical model, this paper, as a corollary, investigates correlation between densities of critical current and concentration of electron pairs. As a highlight, it suggests existence of a second "critical" temperature, TQuench, expected at temperature below standard critical temperature in a High Temperature Superconductor. If under a disturbance sample temperature increases to T > TQuench, relaxation of the electron system of the superconductor to a new dynamic equilibrium might not be completed within given process time. Critical current density then cannot develop to its potentially possible, full value, JCrit(T), to provide zero-loss current transport. After decay of electron pairs under disturbances, why should the decay products at all be motivated to re-combine (relax) to electron pairs? To answer this question, the paper finally calculates entropy differences as the driving force for relaxation, and it investigates a probably existing correlation between entropy production and relaxation process.
We introduce a reaction-path statistical mechanics formalism based on the principle of large deviations to quantify the kinetics of single-molecule enzymatic reaction processes under the Michaelis-Menten mechanism, which exemplifies an out-of-equilibrium process in the living system. Our theoretical approach begins with the principle of equal a priori probabilities and defines the reaction path entropy to construct a new nonequilibrium ensemble as a collection of possible chemical reaction paths. As a result, we evaluate a variety of path-based partition functions and free energies using the formalism of statistical mechanics. They allow us to calculate the timescales of a given enzymatic reaction, even in the absence of an explicit boundary condition that is necessary for the equilibrium ensemble. We also consider the large deviation theory under a closed-boundary condition of the fixed observation time to quantify the enzyme-substrate unbinding rates. The result demonstrates the presence of a phase-separation-like, bimodal behavior in unbinding events at a finite timescale, and the behavior vanishes as its rate function converges to a single phase in the long-time limit.