Converging narrow-channel flow of a super-critical fluid

The solution of a supercritical fluid flowing into a constricted narrow channel is presented in this study. The compressible Navier-Stokes equations in the lubrication limit coupled with the energy equation and the isothermal and non-isothermal van der Waals fluid and perfect gas have been solved. In order to find the semi-analytical solution of these non-linear coupled equations, homogenization technique in the transverse direction has been applied. Because of the high compressibility and high thermal expansion of supercritical fluids, waviness is observed in the flow and thermal fields near the exit of the channel. This effect is attributed to the channel constriction where the slope is maximum, where a strong coupling between the pressure and density gradients exists. Moreover, the density difference between the exit and inlet of the channel drastically increases when one approaches the critical point, corroborating the data from existing literature. © 202

A location-based model using GIS with machine learning, and a human-based approach for demining a post-war region

Locating and removing landmines and other ERW (Explosive Remnants of War) is dangerous, hazardous, and time-consuming. It requires implementing multilevel on-site surveys: general non-technical surveys to mark the areas affected and technical surveys to determine the perimeter of related minefields. This paper introduces a landmine location-based prediction model, combining military experience with machine-learning techniques and spatiotemporal data, by introducing a new approach for area selection and adding military-based features for context modelling and model training. Besides predicting landmine’s location areas, this model classifies the affected regions by priority and difficulty of clearance, in such a way as to minimise the long time needed by surveys and reduce the danger related to that task, thus providing the clearance organisations with a good resource allocation for their operations. We applied several machine learning techniques that combine Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST), taking into consideration the imbalanced data problem and tweaking for the best performance and accuracy. The experimental results show that the model has the potential to provide reliable predictions and valuable services for demining operations on the field

Neurophysiological basis of respiratory discomfort improvement by mandibular advancement in awake OSA patients

Patients with obstructive sleep apneas (OSA) do not complain from dyspnea during resting breathing. Placement of a mandibular advancement device (MAD) can lead to a sense of improved respiratory comfort (“pseudo‐relief”) ascribed to a habituation phenomenon. To substantiate this conjecture, we hypothesized that, in non‐dyspneic awake OSA patients, respiratory‐related electroencephalographic figures, abnormally present during awake resting breathing, would disappear or change in parallel with MAD‐associated pseudo‐relief. In 20 patients, we compared natural breathing and breathing with MAD on: breathing discomfort (transitional visual analog scale, VAS‐2); upper airway mechanics, assessed in terms of pressure peak/time to peak (TTP) ratio respiratory‐related electroencephalography (EEG) signatures, including slow event‐related preinspiratory potentials; and a between‐state discrimination based on continuous connectivity evaluation. MAD improved breathing and upper airway mechanics. The 8 patients in whom the EEG between‐state discrimination was considered effective exhibited higher Peak/TTP improvement and transitional VAS ratings while wearing MAD than the 12 patients where it was not. These results support the notion of habituation to abnormal respiratory‐related afferents in OSA patients and fuel the causative nature of the relationship between dyspnea, respiratory‐related motor cortical activity and impaired upper airway mechanics in this setting

Spatio-temporal physics-informed neural networks to solve boundary value problems for classical and gradient-enhanced continua

Recent advances have prominently highlighted physics informed neural networks (PINNs) as an efficient methodology for solving partial differential equations (PDEs). The present paper proposes a proof of concept exploring the use of PINNs as an alternative to finite element (FE) solvers in both classical and gradient-enhanced solid mechanics. To this end, spatio-temporal PINNs are designed to represent continuous solutions of boundary value problems within spatio-temporal space. These PINNs directly incorporate the equilibrium and constitutive equations in their differential and rate forms, bypassing the requirement for incremental implementation. This simplifies application of PINNs to solve complex mechanical problems, particularly those involved in the context of gradient-enhanced continua. Moreover, traditional meshing is no longer required as it is replaced by a point cloud, making it possible to overcome meshing drawbacks. The results of this investigation prove the effectiveness of the proposed methodology, especially with regards to non-monotonic loading conditions and irreversible plastic deformation. Compared to classical FE approaches, the proposed spatio-temporal PINNs are more readily applied to complex problems, which are tackled in their raw form. This is especially true for gradient-enhanced continuum problems, where there is no need to introduce additional degrees of freedom as in classical FE approaches. However, PINNs training generally requires more computation time, a challenge that can be mitigated by employing the concept of transfer learning as shown in this paper. This concept, which is very useful when performing parametric studies, involves applying knowledge grained from solving one problem to another different but related one. The use of PINNs as mechanical solvers is shown to be highly promising in the forthcoming era, where advancements in GPU technology can further enhance their performance in terms of computation time

Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys

The present study proposes a physics-informed machine learning (PIML) algorithm-based approach aimed at predicting the martensitic transformation temperature (Ms) for the design of “NiTi-like” high entropy shape memory alloys (HESMAs). A previously established HESMAs database is enriched and extended to include bi­nary, ternary, quaternary, quinary and senary alloys containing the most employed alloying elements for HEAs design such as Ni equivalents (Fe, Cu, Co, Pd, Pt and Au), Ti equivalents (Zr and Hf), Nb and Ta. The Extremely Randomized Trees algorithm, based on the concept of multiple random decision tree predictions, is adopted as the regression method for Ms temperature prediction. Two strategies for the algorithm inputs have been adopted, discussed, and compared in terms of reliable predictions. The first relies on the composition of the alloying el­ements, whereas the second exploits a defined set of intrinsic material descriptors. The latter are based on mixing enthalpy, atomic radius, electronegativity, atomic number and number of elements. A high accuracy of the M S prediction has been reached when considering the material descriptors. In fact, the second strategy induces a mean absolute error that is less than 30C for alloys containing up to 4 elements. For more elements there are more discrepancies due to the homogenization state required for HEAs. The validation of the developed approach has been performed using 6 home-made HESMAs prepared specifically for this study. It demonstrated the pre­dictive capabilities of the developed physics-informed machine learning based approach. Finally, a HESMA design tool has been implemented to virtually design new HESMAs with a targeted Ms temperature above 400C. It is worth noting that this aspect is one of the most challenging engineering issues for such alloys. An illustrative case applied to the (NiCuPd) 50 (TiZr) 50 family of alloys demonstrates the predictive capabilities of the developed approach to design such alloys to achieve a Ms temperature in the range of 300C to 700C

Evidence of Dislocation Mixed Climb in Quartz From the Main Central and Moine Thrusts: An Electron Tomography Study

In this study we apply electron tomography to characterize 3D dislocation microstructures in two quartz mylonite specimens from the Moine and Main Central Thrusts, both of which accommodated displacements by dislocation creep in the presence of water. Both specimens show dislocation activity with dislocation densities of the order of 3–4 × 1012 m−2 and evidence of recovery from the presence of subgrain boundaries. ⟨a⟩ slip occurs predominantly on pyramidal and prismatic planes (⟨a⟩ basal glide is not active). [c] Glide is not significant. On the other hand, we observe a very high level of activation of ⟨c + a⟩ glide on the , , (n = 1,2) and even planes. Approximately 60% of all dislocations show evidence of climb with a predominance of mixed climb, a deformation mechanism characterized by dislocations moving in a plane intermediate between the glide and the climb planes. This atypical mode of deformation demonstrates comparable glide and climb efficiency under natural deformation conditions. It promotes dislocation glide in planes not expected for the quartz structure, probably by inhibiting lattice friction. Our quantitative characterization of the microstructure enables us to assess the strain that dislocations can generate. We show that glide systems indicated by the observed dislocations are sufficient to accommodate any arbitrary 3D strain by themselves. Although historically dislocation glide has been regarded as being primarily responsible for producing strain, activation of climb can also directly contribute to the finite strain. On the basis of this characterization, we propose a numerical modeling approach for attempting to characterize the local stress state that gave rise to the observed microstructure

Methods for three-dimensional characterization of the acetabulum prior to pelvic reorientation osteotomy: a scoping review

Periacetabular osteotomy is the gold standard treatment for acetabular dysplasia. The great variability of acetabular dysplasia requires a personalized preoperative planning improved by 3D reconstruction and computer-assisted surgery. To plan the displacement of the acetabular fragment by a pelvic osteotomy, it is necessary to define a reference plane and a method to characterize 3D acetabular orientation. A scoping review was performed on PubMed to search for articles with a method to characterize the acetabulum of native hips in a 3D reference frame. Ninety-eight articles out of 3815 reports were included. Three reproducible reference planes were identified: the anterior pelvic plane, the Standardization and Terminology Committee plane used in gait analysis, and the sacral base plane. The different methods for 3D analysis of the acetabulum were divided in four groups: global orientation, triplanar measurements, segmentation, and surface coverage of the femoral head. Two methods were found appropriate for reorientation osteotomies: the global orientation by a vector method and the triplanar method. The global orientation method relies on the creation of a vector from the acetabular rim, from the acetabular surface or from successive planes. Normalization of the global acetabular vector would correct acetabular dysplasia by a single alignment maneuver on an ideal vector. The triplanar method, based on angle measurements at the center of the femoral head, would involve correction of anomalies by considering axial, frontal, and sagittal planes. Although not directly fit for reorientation, the two others would help to candidate patients and verify both planning and postoperative result

Thermodynamic assessment of two-step nucleation occurrence in supercritical fluid

For the crystallization of an API in supercritical CO2, a two – step nucleation mechanism involves the apparition of metastable liquid droplets in the vapour phase composed of the API dissolved in the CO2, before crystallization. To find out the pressure and temperature conditions such a two – step mechanism could be observed, we studied the stability / metastability / instability for {(S)-Naproxen + CO2} and {(RS)-Ibuprofen + CO2} vapour binary mixtures. Thermodynamic computations proposed in the paper, have shown that a mixture of API and CO2 at elevated pressures can be unstable and/or metastable with respect to a liquid-vapour equilibrium and at the same time with respect to a solid-vapour equilibrium. Depending on the degree of supersaturation, such a mixture can potentially first decompose via spinodal decomposition into coexisting liquid and vapour phases, which turn due to nucleation and growth theory to a solid-fluid equilibrium

Enhancing weight perception in virtual reality: an analysis of kinematic features

This study investigates weight perception in virtual reality without kinesthetic feedback from the real world, by means of an illusory method called pseudo-haptic. This illusory model focuses on the dissociation of visual input and somatosensory feedback and tries to induce the sensation of virtual objects' loads in VR users by manipulating visual input. For that, modifications on the control-display ratio, i.e., between the real and virtual motions of the arm, can be used to produce a visual illusionary effect on the virtual objects' positions as well. Therefore, VR users perceive it as velocity variations in the objects' displacements, helping them achieve a better sensation of virtual weight. A primary contribution of this paper is the development of a novel, holistic assessment methodology that measures the sense of the presence in virtual reality contexts, particularly when participants are lifting virtual objects and experiencing their weight. Our study examined the effect of virtual object weight on the kinematic parameters and velocity profiles of participants' upward arm motions, along with a parallel experiment conducted using real weights. By comparing the lifting of real objects with that of virtual objects, it was possible to gain insight into the variations in kinematic features observed in participants' arm motions. Additionally, subjective measurements, utilizing the Borg CR10 questionnaire, were conducted to assess participants' perceptions of hand fatigue. The analysis of collected data, encompassing both subjective and objective measurements, concluded that participants experienced similar sensations of fatigue and changes in hand kinematics during both virtual object tasks, resulting from pseudo-haptic feedback, and real weight lifting tasks. This consistency in findings underscores the efficacy of pseudo-haptic feedback in simulating realistic weight sensations in virtual environments

Machine learning-based 3D scan coverage prediction for smart-control applications

Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which are capable of taking precise account of real working conditions and enabling first-time-right control. As part of such a smart-control strategy, this paper introduces a machine learning-based approach capable of accurately predicting a priori the 3D coverage of a part according to a scan configuration given as input, i.e. predicting before scanning it which areas of the part will be acquired for real. This corresponds to a paradigm shift, where coverage estimation no longer relies on theoretical visibility criteria, but on rules learned from a large amount of data acquired in real-life conditions. The proposed 3D Scan Coverage Prediction Network (3DSCP-Net) is based on a 3D feature encoding and decoding module, which is capable of taking into account the specifics of the scan configuration whose impact on the 3D coverage is to be predicted. To take account of real working conditions, features are extracted at various levels, including geometric ones, but also features characterising the way structured-light projection behaves. The method is thus able to incorporate inter-reflection and overexposure issues into the prediction process. The database used for the training was built using an ad-hoc platform specially designed to enable the automatic acquisition and labelling of numerous point clouds from a wide variety of scan configurations. Experiments on several parts show that the method can efficiently predict the scan coverage, and that it outperforms conventional approaches based on purely theoretical visibility criteria