Refinement Technique Research Articles

Unveiling A-site substituent’s influence on magnetocaloric response in La2-xNaxNiMnO6 (0.0 ≤ x ≤ 1.0) double perovskite manganite materials

Abstract In this study, the compounds of La2-xNaxNiMnO6 (x = 0.0, 0.1, 0.2, 0.3, 0.5, and 1.0) double perovskite manganites have been produced by employing the sol-gel method to investigate their magnetocaloric effect. From X-Ray Diffraction analysis, all compounds crystallized in rhombohedral structure form with the $${\rm{R}}\bar{3}{\rm{c}}$$ R 3 ¯ c space group confirmed using the Rietveld refinement technique. The compounds show a magnetic phase transition from ferromagnetic to paramagnetic according to the temperature-dependent magnetization measurement. The phase transition temperatures sharply increased with Na doping (x = 0.1) to 277.8 K which can be considered as under room temperature level and then slowly decreased with further Na concentration to 262.3 K. Using an external magnetic field-dependent magnetization measurements, the isothermal magnetization curves were obtained, and these curves used to find the magnetic entropy change values, which give info about compounds’ characteristics of magnetic cooling performance. Under 5 T magnetic field change, the maximum magnetic entropy change value increased 10 times with a small amount of Na doping (x = 0.1), and compounds’ values changed from 0.21 to 1.29 Jkg−1K−1 for x = 0.0 to 1.0 compounds, respectively. Arrott plots were graphed by using isothermal magnetization curves to demonstrate all the compounds have a 2nd order magnetic phase transition which supports that these compounds can be candidates as magnetic refrigerants. Graphical Abstract

Gradient projection method for enforcing crack irreversibility as box constraints in a robust monolithic phase-field scheme

A phase-field monolithic scheme based on the gradient projection method is developed to model crack propagation in brittle materials under cyclic loading. As a type of active set method, the gradient projection method is particularly attractive to enforce the irreversibility condition imposed on the phase-field variables as bound constraints, or box constraints. This method has the advantages of allowing the rapid change of active constraints during iterations and computing the projected gradient with a negligible cost. The gradient projection method is further combined with the limited-memory BFGS (L-BFGS) method to overcome the convergence difficulties arising from the non-convex energy functional. A compact representation of the BFGS matrix is adopted as the limited-memory feature to avoid the storage of fully dense matrices, making this method practical for large-scale finite element simulations. By locating the generalized Cauchy point on the piecewise linear path formed by the projected gradient, the active set of box constraints can be determined. The variables in the active set, which are at the boundary of the box constraints, are kept fixed to form a subspace minimization problem. A primal approach and a dual approach are presented to solve this subspace minimization problem for the remaining free variables at the generalized Cauchy point. Several two-dimensional (2D) and three-dimensional (3D) examples are provided to demonstrate the capabilities of the proposed monolithic scheme, particularly in enforcing the phase-field irreversibility during crack propagation under cyclic loading. In these numerical examples, the proposed monolithic scheme is combined with an adaptive mesh refinement technique to alleviate the heavy computational cost incurred by the fine mesh resolution required around the crack region. The proposed method is further compared with two other phase-field solving techniques regarding the convergence behavior. To ensure a fair comparison, the same problem settings and implementation techniques are adopted. The proposed monolithic scheme provides a unified framework to overcome the numerical difficulties associated with the non-convex energy functional, effectively enforce the phase-field irreversibility to ensure the thermodynamic consistency, and alleviate the heavy computational cost through adaptive mesh refinement in 2D and 3D phase-field crack simulations.

An Analytically Modified Finite Difference Scheme for Pricing Discretely Monitored Options

Finite difference methods are commonly used in the pricing of discretely monitored exotic options in the Black–Scholes framework, but they tend to converge slowly due to discontinuities contained in terminal conditions. We present an effective analytical modification to existing finite difference methods that greatly enhances their performance on discretely monitored options with non-smooth terminal conditions. We apply this modification to the popular Crank–Nicolson method and obtain highly accurate option pricing results with significantly reduced CPU cost. We also introduce an adaptive mesh refinement technique that further improves the computational speed of the modified finite difference method. The proposed method is especially useful for options with high monitoring frequencies, which are difficult to price using other existing methods.

A hybrid harmonic polynomial cell and STF strip theory method for marine hydrodynamics and ship motion analysis

ABSTRACT This paper presents a hybrid method that combines the Harmonic Polynomial Cell (HPC) method and STF strip theory to solve marine hydrodynamics and ship motion problems. The efficient and precise HPC method is extended to three-dimensional ship hydrodynamics using STF strip theory. A new single-node hybridisation condition is introduced to enhance the stability of hydrodynamic predictions. To improve computational accuracy for complex hull shapes, automatic structured grid generation and mesh refinement techniques are applied. The hybrid HPC-STF method is then used to solve 3D ship radiation and diffraction problems in regular waves with forward speed. The method’s performance is validated through comparisons of hydrodynamic coefficients, wave forces, and motion response amplitude operators (RAOs) against experimental data and results from other numerical methods. The findings demonstrate that this hybrid approach provides accurate and effective solutions for ship hydrodynamics, making it a valuable tool for analysing marine structures and motions.

Exploring Structural, Magnetic, and Electric Transport Properties of Sol–Gel Derived Nd0.65Ca0.35MnO3

This paper investigates the phase separation phenomena in low bandwidth manganites, such as Nd1-xCaxMnO3, focusing on the nanoscale effects. Specifically, it aims to understand the competing phase tendencies concerning particle size in the prototypical phase‐separated compound Nd0.65Ca0.35MnO3 (NCMO). Nanosized material was synthesized through sintering sol–gel derived powders at a temperature of 900°C. Using the Rietveld refinement technique, the structure of the NCMO was investigated. It was found that NCMO has an orthorhombic shape with the Pnma spatial group. Mn2p and Mn2s core‐level X‐ray photoemission spectroscopy confirms that Mn has +3, +4 ions coexist in the intended ratio. The transition from paramagnetic (PM) to ferromagnetic (FM) states is shown by magnetization measurements performed at H = 100 Oe. Observations of note include the disparity between zero‐field cooled (ZFC) and field cooled warming (FCW) magnetization, indicating a glassy behavior at lower temperatures, and the hysteresis loop between cooling and warming cycle in the presence of field magnetization. Isothermal magnetization loop analysis confirms the dominance of the antiferromagnetic (AFM) component at higher magnetic fields. However, the resistivity versus temperature data does not reveal an insulator–metal transition (IMT). Instead, the PM state in the sample shows conduction via variable range hopping.

CFD-based prognosis of hydrate dissociation operation inside X-mas tree with slickline intervention

This study evaluates the use of a retrievable heating tool, deployed via slickline, for dissociating hydrate plugs within a subsea well Christmas Tree (WCT). The focus was on a specific hydrate plug located within the WCT bore, analyzed through multi-phase Computational Fluid Dynamics (CFD) simulations enhanced by adaptive mesh refinement techniques. Findings indicate that a power setting of approximately 3.1 kW is optimal, effectively balancing operational efficiency with safety. This power level maintains temperatures just below the critical threshold of 120°C and achieves complete hydrate dissociation in around three hours. The investigation underscores the importance of precise power calibration and management of its associated uncertainties to ensure the integrity and effectiveness of hydrate dissociation operations.

A Novel Adaptive Refinement Technique for Least-Squares-Based Numerical Solution of the Water–Hammer Problem

A Novel Adaptive Refinement Technique for Least-Squares-Based Numerical Solution of the Water–Hammer Problem

Comparison of extraction and refinement techniques for volatile compound analysis in camellia oil.

The processing techniques of camellia oil, containing freshly squeezed (FSCO), refined (DFCO), cold-pressed (OFCO), and hot-pressed (RFCO), significantly influence flavor compounds and organoleptic properties. In this study, the preference for FSCO and RFCO was revealed by sensory evaluation due to the "fruity" and "roasted" flavors, respectively. Flavor differences among oils were accurately distinguished by the E-nose. A total of 77 and 116 odorants were identified by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and gas chromatography-mass spectrometry (GC-MS), respectively. The gas chromatography-olfactometry (GC-O) and detection frequency analysis identified 34 active-aroma compounds with DF≥2. Aroma recombination and omission experiments further confirmed that (E)-2-nonenal and nonanal, derived from the degradation of unsaturated fatty acids, significantly contributed to "green" and "fruity" aromas. Notably, these concentrations positively correlated with sensory preference. This study provides valuable insights into the flavor identification of camellia oil and the improvement of processing techniques.

Adaptive Spline Surface Fitting With Arbitrary Topological Control Mesh.

Reconstructing a spline surface from a given arbitrary topological triangle mesh is a fundamental and challenging problem in computer-aided design and engineering. This article introduces a novel surface fitting method utilizing G-NURBS capable of handling control meshes with arbitrary topologies. This method employs adaptive control point adjustment, guided by the geometric attributes of the input model, ensuring precise representation of sharp features such as edges and corners. Two primary strategies are employed: A parameter correspondence approach designed for sharp features and a control mesh iterative refinement technique that incorporates geometrical feature information. The proposed method has been tested and evaluated on various CAD models to demonstrate its effectiveness. This method can achieve higher fitting accuracy while faithfully preserving the geometrical features with fewer control points.

A Customized Augmented Lagrangian Method for Block-Structured Integer Programming.

Integer programming with block structures has received considerable attention recently and is widely used in many practical applications such as train timetabling and vehicle routing problems. It is known to be NP-hard due to the presence of integer variables. We define a novel augmented Lagrangian function by directly penalizing the inequality constraints and establish the strong duality between the primal problem and the augmented Lagrangian dual problem. Then, a customized augmented Lagrangian method is proposed to address the block-structures. In particular, the minimization of the augmented Lagrangian function is decomposed into multiple subproblems by decoupling the linking constraints and these subproblems can be efficiently solved using the block coordinate descent method. We also establish the convergence property of the proposed method. To make the algorithm more practical, we further introduce several refinement techniques to identify high-quality feasible solutions. Numerical experiments on a few interesting scenarios show that our proposed algorithm often achieves a satisfactory solution and is quite effective.

Digital-twin-based active input refinement for insertion loss estimation and QoT optimization in C and C + L networks

Quality of transmission (QoT) prediction is a fundamental function in optical networks. It is typically embedded within a digital twin and used for operational tasks, including service establishment, service rerouting, and (per-channel or per-amplifier) power management to optimize the working point of services and hence to maximize their capacity. Inaccuracy in QoT prediction results in additional, unwanted design margins. A key contributor to QoT inaccuracy is the uncertain knowledge of fiber insertion loss, e.g., the attenuation due to connector losses at the beginning or at the end of each fiber span, as such loss cannot be directly monitored. Indeed, insertion losses drive the choice of the launch power in fiber spans, which in turn drive key physical effects, including the Kerr and stimulated Raman scattering (SRS) effects, which affect services’ QoT. It is thus important to estimate (and detect possibly anomalous) fiber insertion losses at each span. We thereby propose a novel active input refinement (AIR) technique using active probing to estimate insertion losses in C and C + L systems. Here, active probing consists of adjusting amplifier gains span by span to slightly alter SRS. The amount of adjustment must be sufficient to be measurable (such that insertion losses can be inferred from the measures) but small enough to have a negligible impact on running services in a live network. The method is validated by simulations on a European network with 30 optical multiplex sections (OMSs) in C and C + L configurations and by lab experiments on a C-band network, demonstrating that AIR significantly improves insertion loss estimation, network QoT optimization, and QoT prediction compared with other state-of-the-art monitoring techniques. This work underscores the critical role of accurate estimation of QoT inputs in enhancing optical network performance.

An improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra

We developed a novel adaptive finite element method (FEM) to address the problem of 3-D direct current (DC) resistivity forward modeling with complex surface topography and arbitrary conductivity anisotropy. The tetrahedra-based FEM and secondary virtual potential algorithm are first used to handle arbitrary complex geo-models. Then, to ensure the accuracy of the simulation solution, an improved goal-oriented adaptive mesh refinement (AMR) algorithm is proposed to realize an optimized mesh density distribution. To avoid the drawback of the traditional goal-oriented AMR algorithm for the DC forward modeling problem, we incorporate a volume-based weighting factor into the posterior error estimation procedure to further optimize the density distribution of the forward modeling grid. In addition, instead of traditional open source mesh generation software, we propose using the longest-edge bisection (LEB) algorithm to perform the mesh refinement process, which can preserve the topological structure between different-level meshes. Finally, the comprehensive test using a two-layered model and two complex 3-D models demonstrate the capability of our newly developed code to obtain highly accurate solutions even on relatively coarse initial grids. By incorporating the volume factor, our novel AMR algorithm achieves a more uniform and reasonable mesh density distribution during these experiments. The LEB refinement technique can generate a series of nested tetrahedral elements and provide fewer tetrahedral elements compared to the traditional Delaunay-based AMR method. The proposed 3-D DC forward modeling method has been implemented into an open source C++ code, which will contribute to the advancement of the 3-D DC resistivity imaging field.

Shear-induced oil separation from a sand particle moving in water

Removing oil attached to fine solid particles in water purification presents challenging technical, economic, and ecological issues. The present work numerically investigates the shear effect on oil that contains a 100-μm-diameter sand particle in a water medium. In this research, we employ the coupled level set and volume of fluid method to track interface evolution accurately. An adaptive mesh refinement technique allows us to resolve an interface down to 60 nm. Computations at various particle Reynolds numbers (Rep=2−200) and oil film thicknesses (5%, 10%, 20%, and 40% of the diameter) reveal four coating behaviour scenarios: deformation, tail formation, partial separation, and full separation. The oil film negligibly deforms at Rep≤5 at most film thicknesses. The separation occurs in full or partial regimes at higher Rep and thicker films. A thinner film forms a tail that periodically fluctuates. The analysis shows that these fluctuations uniquely reshape the oil tail at each time period.

The impact of heating rate on the synthesis of Ca3Co4O9 using rice starch as a soft template

This study examines how the heating rate at calcination temperature (5 °C/min, 10 °C/min, 15 °C/min, and 20 °C/min) on the synthesis of Ca3Co4O9 (CCO) material using soft template (rice starch) affects the thermal conductivity (κ), electrical resistivity (ρ), particle size, and crystallite size in addition to the Ca3Co4O9 phase. The sol-gel combustion method uses the precursor compounds CaCO3, Ca(NO3)2∙4H2O, and Co(NO3)2·6H2O. The Rietveld refinement technique verified the crystal structure, and the Williamson-Hall (WH) plot was used to calculate crystallite size. The heating rate variations were related to the phase transition, reaction rate, and combustion energy that affected the crystal size and p-type thermoelectric (TE) capabilities. Using calcium nitrate as the precursor yields a higher purity of CCO than calcium carbonate. With an increase in heating rate temperature, the values of κ and ρ tend to rise; they vary from 1.7414 to 1.9827 W/m·K and 90.8222–194.1667 mΩ cm, respectively. The occurrence of CaCO3 in the final product and the anomaly of crystallite size at 20 °C/min are discussed.

Rietveld refined crystal structure, magnetic, dielectric, and electric properties of Zn substituted Ni-Mg ferrites

Sol − gel technique was used to synthesize Ni0.7-xZnxMg0.3Fe2O4 (Ni – Zn – Mg) ferrites with x = 0.1, 0.2, 0.3, 0.4, 0.5. Formation of single phase cubic spinel structure of the synthesized samples was confirmed by employing Rietveld refinement technique. Spherical and inhomogeneous distribution of grains was observed from Transmission Electron Microscopy (TEM) image. The frequency dependent initial permeability (μi), real part of initial permeability (μ′), imaginary part of initial permeability (μ″) was studied for synthesized samples. For zinc content x = 0.2 in Ni – Zn – Mg ferrites μi is observed to be high as compare to other compositions and that attributed to grain size. DC electrical resistivity (ρdc) analysis shows both conducting and semiconducting behavior for all the samples. Room temperature electrical resistivity values were observed to be lower for lower content of zinc and higher for higher content of zinc and that attributed to the hopping mechanisms at octahedral site. The decreasing trend of Curie temperature was observed with increase in zinc content in Ni – Zn – Mg ferrites. Frequency dispersive dielectric properties study of Ni – Zn – Mg ferrites revealed normal dielectric behavior.

Numerical simulation of shock attenuation with real gas effects and a turbulent boundary layer in the expansion tube

The influence of real gas effects and a turbulent boundary layer on shock wave attenuation in the expansion tube is studied by numerically solving the axisymmetric compressible Navier–Stokes equations with an adaptive mesh refinement technique. Numerical simulation results reveal that the ideal gas assumption is not applicable to the expansion tube, and the turbulent boundary layer plays a major role in decreasing the shock wave speed in the acceleration tube of the expansion tube. Shock wave attenuation is attributed to the turbulent boundary layer decreasing the pressure behind the shock wave. The numerical simulations that include the real gas effects and the development of turbulent boundary layers qualitatively agree with analytical solutions in the shock tube, and they show good agreement with the experimental results, especially for the shock speed in the acceleration tube of the expansion tube. Both effects should be considered in the numerical simulation model aimed to support experiments in expansion tubes.

Structural characteristics, P-V-L theory, Raman spectra, and microwave dielectric properties of (La1-xNdx)2(Zr0.96Ti0.04)3(MoO4)9 ceramics for LTCC applications

This article explores the microwave dielectric properties of (La1-xNdx)2(Zr0.96Ti0.04)3(MoO4)9 (x=0.1,0.3,0.5, and 0.7) ceramics prepared by solid-state reaction. Utilizing the Rietveld refinement technique in XRD, the sample is identified as a trigonal crystal system belonging to the R3¯c space group. SEM analysis indicates that relative density has a significant impact on the εr and Q×f of ceramics. The Q×f values of ceramics are closely related to the FWHM of the Raman spectra. At 750℃, (La0.5Nd0.5)2(Zr0.96Ti0.04)3(MoO4)9 ceramic exhibited excellent dielectric properties: εr = 10.4 (±0.08), Q×f = 149,300 (±3211) GHz, τf = −34.8 (±0.7) ppm/℃. To gain a deeper understanding of the intrinsic mechanisms governing the microwave dielectric properties of ceramics, analysis based on the P-V-L theory reveals that the La(Nd)-O bond and Mo-O bond contribute significantly to εr and Q×f, respectively.

High-Fidelity Simulations of Flight Dynamics and Trajectory of a Parachute–Payload System Leaving the C-17 Aircraft

This article examines the flight dynamics and trajectory analysis of a parachute–payload system deployed from a C-17 aircraft. The aircraft is modeled with an open cargo door, extended flaps, and four turbo-fan engines operating at an altitude of 2000 feet Above Ground Level (AGL) and an airspeed of 150 knots. The payloads consist of simplified CONEX containers measuring either 192 inches or 240 inches in length, 9 feet in width, and 5.3 feet in height, with their mass and moments of inertia specified. At positive deck angles, gravitational forces cause these payloads to begin a gradual descent from the rear of the aircraft. For aircraft at zero deck angle, a ring-slot parachute with approximately 20% geometric porosity is utilized to extract the payload from the aircraft. This study specifically employs the CREATE-AV Kestrel simulation software to model the chute-payload system. The extraction and suspension lines are represented using Kestrel’s Catenary capability, with the extraction line connected to the floating confluence points of the CONEX container and the chute. The chute and payload will experience coupled motion, allowing for an in-depth analysis of the flight dynamics and trajectory of both elements. The trajectory data obtained will be compared to that of a payload (without chute and cables) exiting the aircraft at positive deck angles. An adaptive mesh refinement technique is applied to accurately capture the engine exhaust flow and the wake generated by the C-17, chute, and payloads. Friction and ejector forces are estimated to align the exit velocity and timing with those recorded during flight testing. The results indicate that the simulation of extracted payloads aligns with expected trends observed in flight tests. Notably, higher deck angles result in longer distances from the ramp, leading to increased exit velocities and reduced payload rotation rates. All payloads exhibit clockwise rotation upon leaving the ramp. The parachute extraction method yields significantly higher exit velocities and shorter exit times, while the payload-chute acceleration correlates with the predicted drag of the chute as demonstrated in prior studies.

Assertion-Based Validation using Clustering and Dynamic Refinement of Hardware Checkers

Post-silicon validation is a vital step in System-on-Chip (SoC) design cycle. A major challenge in post-silicon validation is the limited observability of internal signal states using trace buffers. Hardware assertions are promising to improve observability during post-silicon debug. Unfortunately, we cannot synthesize thousands (or millions) of pre-silicon assertions as hardware checkers (coverage monitors) due to hardware overhead constraints. Therefore, we need to select the most profitable assertions based on design constraints. However, the design constraints can also change dynamically during the device lifetime due to changes in use-case scenarios as well as input variations. Therefore, assertion selection needs to dynamically adapt based on changing circumstances. In this article, we propose an assertion-based post-silicon validation framework to address the above challenges. Specifically, this article makes two important contributions. We propose a clustering-based assertion selection technique that can select the most profitable pre-silicon assertions to maximize the fault coverage. We also present a cost-aware dynamic refinement technique that can select beneficial hardware checkers during runtime based on changing design constraints. Experimental evaluation demonstrates that our proposed pre-silicon assertion selection can outperform state-of-the-art assertion ranking methods (Goldmine and HARM). The results also highlight that our proposed post-silicon dynamic refinement can accurately predict area (less than 5% error) and power consumption (less than 3% error) of hardware checkers at runtime. This accurate prediction enables the identification of the most profitable hardware checkers based on design constraints.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.