bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation of Tip Leakage Vortex Breakdown in a High-Speed Multistage Axial Compressor
Authors: Seki Ryosuke , Azuma Toshihiko , Iwatani Junji , Nakaniwa Akihiro , Okui Hidetaka , Shibata Takanori 
Published: February 2023

AbstractIn this article, an unsteady tip leakage flow instability is identified and investigated for an axial compressor at near-surge conditions. We describe the results of experimental verification of a new compressor developed by improving the blade geometry based on the criterion for the occurrence of this unsteady phenomenon. In a high-speed multistage axial flow compressor having a subsonic high stagger rotor blade, a surge test was carried out by changing the tip clearance. Under a condition of large tip clearance, a drastic decrease in the static pressure rise coefficient near the surge point was observed. At this operating condition, large, unsteady pressure fluctuation at the blade tip was confirmed, and the occurrence of tip leakage vortex breakdown was clarified by unsteady multipoint pressure measurement and detailed unsteady numerical simulations. Due to the blockage effect caused by vortex breakdown of the tip leakage, double leakage and axially reversed flow near the trailing edge were observed. It was found that the vortex breakdown region of the tip leakage vortex propagated in the circumferential direction and caused the rotating instability. In order to investigate the relationship among this unsteady flow phenomenon, tip clearance size, and flow pattern, unsteady calculation was conducted by changing the blade tip stagger and tip clearance size. A new concept of tip clearance of staggered pitch reference was proposed, which makes it possible to include the effect of blade loading on the clearance and clarifies that there exists a threshold at which vortex breakdown occurs/does not occur. On the basis of the aforementioned results, a high-speed multistage improved compressor was designed and manufactured to prevent tip leakage vortex breakdown. A clearance change test using active clearance control technology was conducted, and an increase in the static pressure rise coefficient near the surge point was confirmed for each clearance. The design concept of the improved blade, which suppressed the unsteady tip leakage flow instability, was tested and verified, and the effectiveness of the design guideline in actual gas turbines for power generation was confirmed.
Blade Vibration Analysis for Radial Turbine Featuring a Multi-Channel Casing Design
Authors: Hassan Ahmed Farid Ayad , Müller Fabian F. , Schatz Markus , Vogt Damian M. 
Published: February 2023

AbstractMulti-channel casing (MC) shows promising results in controlling the performance of the radial turbines. It has neither movable parts nor complicated control mechanisms therefore it withstands higher thermal loads compared to the commonly used control systems such as the Variable Geometry Turbine (VGT). This advantage makes the MC applicable for a wider range of applications which is difficult to be covered with the common control systems. Replacing the traditional spiral casing with the MC affects the blade's vibration behavior. First, a 3D unsteady computational fluid dynamic (CFD) simulation is performed to investigate the influence of using MC on the turbine flow field for both full and partial admission operation. Second, a forced response analysis is performed based on the CFD result to calculate the blade vibration amplitude at different resonance crossings. Finally, an MC is manufactured and tested experimentally to validate the numerical study. The results show that ignoring the casing replacement effect on the blade vibration during the MC design phase led to a high vibration amplitude and consequently causes high cycle fatigue.
Machine-Learning-Based Hydraulic Fracturing Flowback Forecasting
Authors: Guo Jinyuan , Guo Wei , Kang Lixia , Zhang Xiaowei , Gao Jinliang , Liu Yuyang , Liu Ji , Yu Haiqing 
Published: February 2023

AbstractHydraulic fracturing is an indispensable procedure to the economic development of shale gas. The flowback of the hydraulic fracturing fluid is one of the most important parameters recorded after shale gas wells are put into production. Generally, the flowback ratio is used as the flowback indicator during hydraulic fracturing. The flowback ratio has a great influence on shale gas production. However, the flowback ratio is subjected to various affecting factors with their correlativity unclear. Based on a large amount of original geological, engineering, and dynamic data acquired from 373 hydraulically fractured horizontal wells, the flowback characteristics were systematically studied based on machine learning. Based on the data analysis and random forest forecasting, a new indicator, single-cluster flowback ratio, was proposed, which can more effectively reflect the inherent relationship between flowback fluid volume and influencing factors. The results of training random forests for big data show that this indicator has better learnability and predictability. A good linear relationship exists between single-cluster flowback ratios in different production stages. Accordingly, the 30-day single-cluster flowback ratio can be used to predict the 90-day and 180-day single-cluster flowback ratios. The main controlling factors of production and flowback ratio were also systematically analyzed. It is found that the main controlling factors of the flowback ratio include the number of fracturing clusters, the total amount of sand, number of fracturing stages, and fluid injection intensity per cluster. This study can provide a fundamental reference for analyzing the hydraulically fracturing fluid flowback for shale gas reservoirs.
An Efficient Decoupled Reliability-Based Topology Optimization Method Based on a Performance Shift Strategy
Authors: Zheng Jing , Yuan Liang , Jiang Chao , Zhang Zhe 
Published: February 2023

AbstractIn this paper, a new efficient reliability-based topology optimization (RBTO) method is proposed for structures, in which the double-loop optimization is equivalently decoupled into a sequential process based on a performance shift strategy. First, a volume minimization RBTO formulation is built for structures considering displacement or compliance reliability constraints. Second, an efficient decoupling scheme is proposed to turn the double-loop RBTO into a series of deterministic topology optimization and reliability analysis. For the reliability analysis, the reliability probability is calculated based on the probability distribution function of the performance function, and the probability distribution function can be solved by the maximum entropy method based on the raw moments calculated by the multiplicative dimension reduction method. A performance shift strategy is then applied to build an equivalence between probabilistic constraint and deterministic constraint to formulate the deterministic topology optimization. Thirdly, the adjoint variable method is applied to obtain the sensitivity information for topological design variables, and a gradient-based optimization algorithm is used to update the design variables. Finally, four typical numerical examples are used to verify the advantage of the proposed method. Compared with the traditional sequential optimization and reliability assessment (SORA) method, the proposed RBTO method results in a design with a 5.4% smaller volume value to reach the same reliability index requirement for the cantilever beam, and the function calls are 354 times less than that of the SORA method.
A Novel Method to Design Translational Parallel Manipulators With an Assigned Maximum Orientation Error
Authors: Di Gregorio Raffaele , Simas Henrique , Simoni Roberto 
Published: February 2023

AbstractGeometric Design and Tolerancing (GD&T) is the language that specifies several design features and, in this paper, is used to deal with the tolerances of Translational Parallel Manipulators (TPMs). TPMs intend to make a platform have only translational motion with respect to the frame (base), but, in practice, TPMs’ platforms present undesirable rotation motions due to unavoidable geometric errors in links’ sizes. During the machine-element design, geometric errors can be limited by imposing given tolerances on links’ sizes and the tighter tolerances are the higher manufacturing costs are. Therefore, dealing with tolerances is the key for designing a TPM with assigned requirements in terms of platform’s rotation limits. In this context, this paper moves from the fact that machining processes generate link sizes with values distributed according to Gaussian distributions, whose mean values are the nominal sizes and whose standard deviations depend on the machining process accuracy, to build a novel method that makes a designer able to satisfy an assigned maximum orientation error on the TPM platform. The proposed method consists of two main phases: (a) identification of the geometric parameters that affect platform’s position and orientation, and (b) an analysis, based on numerical simulations, that relates the tolerances assigned to the identified parameters and the positioning precision of the platform. The method can be adapted to other types of lower-mobility robots (LMRs). A case study is also discussed to better illustrate the method.
Jerk-Optimal Piecewise Planning of Tool Orientation for 5-Axis Ball-End Machining With Linearized Kinematic Constraints
Authors: Wu Lei , Xu Jinting , Yin Xiaolong , Sun Yuwen 
Published: February 2023

Abstract
When planning tool orientations for 5-axis machining from the perspective of kinematics, existing works often tend to optimize the angular velocity or acceleration of the rotary axes rather than the angular jerk due to its calculation sensitivity to discretization variations, though the jerk is fundamentally tied to the tracking errors and residual vibrations of the actuators. In this paper, a study is reported on how to optimize the angular jerk of the rotary axes while comprehensively considering the kinematic constraints, thus achieving a jerk-optimal tool orientation along the tool path, with driving capacity of the rotary axes respected. In this method, the displacements of the rotary axes are continuously represented by two quintic B-spline curves, and then the angular velocity, acceleration, and jerk of the rotary axes, which are the derivatives of the displacements, can be succinctly represented as a B-spline curve. Taking advantage of the convex hull property of B-spline curve, the linear analytical representations of the kinematic constraints of the rotary axes can be successfully derived in form of control coefficient combinations. To prevent the machining interference at the same time, a greedy strategy that incorporates a process of alternately smoothing tool orientation and checking machining interference is employed. Then, the smooth displacement splines of the rotary axes can be obtained by solving a constructed quadratic programming (QP) model that minimizes the angular jerk along the tool path, while satisfying kinematic constraints and without machining interference. Moreover, to generate efficiently tool orientations for long tool paths, a piecewise planning strategy that optimizes the tool orientation from coarse to fine is developed. Finally, the conducted experiments validate the proposed method.
A Hybrid Deep Generative Network for Pore Morphology Prediction in Metal Additive Manufacturing
Authors: Song Zheren , Wang Xinming , Gao Yuanyuan , Son Junbo , Wu Jianguo 
Published: February 2023

AbstractMetal additive manufacturing (AM) has been receiving unprecedented attention for its transformational role in extending the AM materials from polymers to various metals. However, various quality issues, especially porosity, significantly impacts the mechanical properties and fatigue life of the final products, which imposes barriers for the widespread adoption of metal AM processes. In this study, we use the deep learning (DL) techniques to comprehensively investigate the relationships between pore microstructure and processing parameters. Specifically, a novel hybrid deep generative prediction network (HDGPN) that leverages both variational autoencoder and generative adversarial network is proposed to characterize the complex pore microstructure with in-depth representations and predict pore morphology under arbitrary processing parameters. By visualizing the predicted pore morphology, the complicated interaction dynamics between the processing parameters and pore microstructure are directly revealed, which may guide the optimization of metal AM manufacturing processes to fabricate defect-free products. A case study of a selective laser melting (SLM) process is conducted to validate the proposed modeling and prediction framework.
Ultrahigh dielectric permittivity in oxide ceramics by hydrogenation
Authors: Duong Nguyen Xuan , Jang Ji-Soo , Jung Min-Hyoung , Bae Jong-Seong , Ahn Chang Won , Jin Jong Sung , Ihm Kyuwook , Kim Gyehyeon , Lim So Yeon , Lee Jongmin , Dung Dang Duc , Lee Soonil , Kim Young-Min , Lee Sanghan , Yang Sang Mo , Sohn Changhee , Kim Ill Won , Jeong Hu Young , Baek Seung-Hyub , Kim Tae Heon 
Published: February 2023

Boosting dielectric permittivity representing electrical polarizability of dielectric materials has been considered a keystone for achieving scientific breakthroughs as well as technological advances in various multifunctional devices. Here, we demonstrate sizable enhancements of low-frequency dielectric responses in oxygen-deficient oxide ceramics through specific treatments under humid environments. Ultrahigh dielectric permittivity (~5.2 × 10
6
at 1 Hz) is achieved by hydrogenation, when Ni-substituted BaTiO
3
ceramics are exposed to high humidity. Intriguingly, thermal annealing can restore the dielectric on-state (exhibiting huge polarizability in the treated ceramics) to the initial dielectric off-state (displaying low polarizability of ~10
3
in the pristine ceramics after sintering). The conversion between these two dielectric states via the ambient environment–mediated treatments and the successive application of external stimuli allows us to realize reversible control of dielectric relaxation characteristics in oxide ceramics. Conceptually, our findings are of practical interest for applications to highly efficient dielectric-based humidity sensors.
Real-time (nanoseconds) determination of liquid phase growth during shock-induced melting
Authors: Renganathan Pritha , Sharma Surinder M. , Turneaure Stefan J. , Gupta Yogendra M. 
Published: February 2023

Melting of solids is a fundamental natural phenomenon whose pressure dependence has been of interest for nearly a century. However, the temporal evolution of the molten phase under pressure has eluded measurements because of experimental challenges. By using the shock front as a fiducial, we investigated the time-dependent growth of the molten phase in shock-compressed germanium. In situ x-ray diffraction measurements at different times (1 to 6 nanoseconds) behind the shock front quantified the real-time growth of the liquid phase at several peak stresses. These results show that the characteristic time for melting in shock-compressed germanium decreases from ~7.2 nanoseconds at 35 gigapascals to less than 1 nanosecond at 42 gigapascals. Our melting kinetics results suggest the need to consider heterogeneous nucleation as a mechanism for shock-induced melting and provide an approach to measuring melting kinetics in shock-compressed solids.
Hospital bed replacement for acute care of children at home during the COVID-19 pandemic through a Hospital-in-the-Home programme
Authors: Bryant Penelope A , Lawrence Joanna , Boyce Suzanne , Simpson Catherine M , Sinclair Gemma , Chong Candie , Lewis Phillipa , Lee Stephanie , Hughes Rebecca , Dalton Samuel , Lacey Cara , Nisbet Lauren C , Smith Tessa E , Chapman Sarah , Lakshminarayanan Swathi , Hurd Kahlia , Smith Katie , Savill Brenda , Ibrahim Laila F 
Published: February 2023

ObjectivesDuring the COVID-19 pandemic, we expanded our Hospital-in-the-Home (HITH) programme to increase capacity and manage COVID-19-positive children. We aimed to assess impact on overall HITH activity and COVID-19-positive outcomes.DesignProspective comparative cohort study.SettingThe largest paediatric HITH in Australasia, at The Royal Children’s Hospital Melbourne.PatientsChildren 0–18 years admitted to HITH during the pandemic.InterventionWe developed a COVID-19 responsive service, and a guideline for COVID-19-positive patients. We compared overall activity prior to and during the pandemic, and COVID-19-positive admissions with different variants.Main outcomesWe compared outcomes for all HITH patients before and during the pandemic, and for COVID-19-positive patients admitted first to hospital versus directly to HITH.ResultsHITH managed 7319 patients from March 2020 to March 2022, a 21% increase to previously, with a 132% telehealth increase. 421 COVID-19-positive patients (3 days–18.9 years) were admitted to HITH, predominantly high risk (63%) or moderately unwell (33%). Rates of childhood infection in Victoria, with proportion admitted to HITH were: original/alpha variant—3/100 000/month, 0.7%; delta—92/100 000/month, 0.8%; omicron—593/100 000/month, 0.3%. Eligible parents of only 29 of 71 (41%) high-risk children were vaccinated. COVID-19-positive children admitted directly to HITH were less likely to receive COVID-19-specific treatment than those admitted to hospital first (14 of 113 (12%) vs 33 of 46 (72%), p<0.001), reflecting more severe respiratory, but not other features in inpatients. 15 of 159 (10%) were readmitted to hospital, but none deteriorated rapidly.ConclusionsCOVID-19-positive children at high risk or with moderate symptoms can be managed safely via HITH at home, the ideal place for children during the pandemic.
Unusual fusion gene rearrangements in patients with nodular fasciitis: a study of rare and novel USP6 fusion partners with a review of the literature
Authors: Balko Jan , Stanek Martin , Krskova Lenka , Zamecnik Josef 
Published: February 2023

AimsThis retrospective non-randomised study aims to identify new and rare fusion partners withUSP6in the setting of nodular fasciitis. It has been proven, that nodular fasciitis can harbour different variants ofUSP6fusions, which can be used in routine diagnostics and even determine the biological behaviour of the process.MethodsA total of 19 cases of nodular fasciitis examined between 2011 and 2022 at Motol University Hospital in Prague were included into this study. Next to the histopathological evaluation, all cases were assessed using immunohistochemistry, RT-PCR and Anchored multiplex RNA methods. Patient’s main demographic characteristics and corresponding clinical data were also analysed.ResultsThis study presents one novel (KIF1A) and five rare examples (TMP4, SPARC, EIF5A, MIR22HG, COL1A2) of fusion partners withUSP6among 19 cases of nodular fasciitis.ConclusionIdentification ofUSP6fusion partners in nodular fasciitis helps to understand the biology of such lesions. Moreover, it can be useful in routine histopathological practice of soft-tissues diagnostics, especially in preventing possible misdiagnosis of malignancy.
Sustainability of Stack Exchange Q&A communities: the role of trust
Authors: Vranić Ana , Tomašević Aleksandar , Alorić Aleksandra , Mitrović Dankulov Marija 
Published: February 2023

AbstractKnowledge-sharing communities are fundamental elements of a knowledge-based society. Understanding how different factors influence their sustainability is of crucial importance. We explore the role of the social network structure and social trust in their sustainability. We analyze the early evolution of social networks in four pairs of active and closed Stack Exchange communities on topics of physics, astronomy, economics, and literature and use a dynamical reputation model to quantify the evolution of social trust in them. In addition, we study the evolution of two active communities on mathematics topics and two closed communities about startups and compare them with our main results. Active communities have higher local cohesiveness and develop stable, better-connected, trustworthy cores. The early emergence of a stable and trustworthy core may be crucial for sustainable knowledge-sharing communities.
Multi-photon-addition amplified coherent state
Authors: Zhan Xue-feng , Ke Qiang , Li Min-xiang , Xu Xue-xiang 
Published: February 2023

State [Formula: see text] and state [Formula: see text] are same to state [Formula: see text], which is called as multi-photon-addition amplified coherent state (MPAACS) by us. Here, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] are photon number operator, creation operator, coherent state, gain factor, and an integer, respectively. We study mathematical and physical properties for these MPAACSs, including normalization, photon component analysis, Wigner function, effective gain, quadrature squeezing, and equivalent input noise. Actually, the MPAACS, which contains more nonclassicality, is an amplified version of photon-added coherent state (PACS) introduced by Agarwal and Tara [Phys. Rev. A 43, 492 (1991)]. Our work provides theoretical references for implementing amplifiers for light fields.
Coarse computability, the density metric, Hausdorff distances between Turing degrees, perfect trees, and reverse mathematics
Authors: Hirschfeldt Denis R. , Jockusch Carl G. , Schupp Paul E. 
Published: February 2023

For [Formula: see text], the coarse similarity class of A, denoted by [Formula: see text], is the set of all [Formula: see text] such that the symmetric difference of A and B has asymptotic density 0. There is a natural metric [Formula: see text] on the space [Formula: see text] of coarse similarity classes defined by letting [Formula: see text] be the upper density of the symmetric difference of A and B. We study the metric space of coarse similarity classes under this metric, and show in particular that between any two distinct points in this space there are continuum many geodesic paths. We also study subspaces of the form [Formula: see text] where [Formula: see text] is closed under Turing equivalence, and show that there is a tight connection between topological properties of such a space and computability-theoretic properties of [Formula: see text]. We then define a distance between Turing degrees based on Hausdorff distance in the metric space [Formula: see text]. We adapt a proof of Monin to show that the Hausdorff distances between Turing degrees that occur are exactly 0, [Formula: see text], and 1, and study which of these values occur most frequently in the senses of Lebesgue measure and Baire category. We define a degree a to be attractive if the class of all degrees at distance [Formula: see text] from a has measure 1, and dispersive otherwise. In particular, we study the distribution of attractive and dispersive degrees. We also study some properties of the metric space of Turing degrees under this Hausdorff distance, in particular the question of which countable metric spaces are isometrically embeddable in it, giving a graph-theoretic sufficient condition for embeddability. Motivated by a couple of issues arising in the above work, we also study the computability-theoretic and reverse-mathematical aspects of a Ramsey-theoretic theorem due to Mycielski, which in particular implies that there is a perfect set whose elements are mutually 1-random, as well as a perfect set whose elements are mutually 1-generic. Finally, we study the completeness of [Formula: see text] from the perspectives of computability theory and reverse mathematics.