Optimal Phase Research Articles

Optimal feeder routing and phase balancing for an unbalanced distribution system: a case study in Cambodia

This paper aims to minimize the distance of the feeder path from high-voltage/medium-voltage (HV/MV) substation to medium-voltage/low-voltage (MV/LV) transformers and minimize power loss in an unbalanced distribution system by the phase-swapping concept-based load balancing. The shortest path algorithm (SPA) and the genetic algorithm (GA) for optimal feeder routing and phase balancing separately in the MV unbalanced distribution network are proposed. First, the relevant data for the system is collected. These data include substation coordinates (X, Y), active and reactive power (P, Q), phase connections, and lines’ impedance (Z). secondly, the performance of the existing configuration of the test system with numerous indications is presented. Finally, the proposed method is performed to minimize the length and power losses. The real 47-bus test system in Cambodia is chosen to demonstrate the proposed method. In this study, overall power losses, the maximum voltage imbalance, and voltage regulation are computed by the backward/forward sweep load flow. The results based on the simulation indicate the importance of the proposed approach, especially for distribution system designers and operators.

First-principles screening of metal–organic frameworks for entangled photon pair generation

The transmission of strong laser light in nonlinear optical materials can generate output photons sources that carry quantum entanglement in multiple degrees of freedom, making this process a fundamentally important tool in optical quantum technology. However, the availability of efficient optical crystals for entangled light generation is severely limited in terms of diversity, thus reducing the prospects for the implementation of next-generation protocols in quantum sensing, communication and computing. To overcome this, we developed and implemented a multi-scale first-principles modeling technique for the computational discovery of novel nonlinear optical devices based on metal–organic framework (MOF) materials that can efficiently generate entangled light via spontaneous parametric down-conversion (SPDC). Using collinear degenerate type-I SPDC as a case study, we computationally screen a database of 114 373 synthesized MOF materials to establish correlations between the structure and chemical composition of MOFs with the brightness and coherence properties of entangled photon pairs. We identify a subset of 49 non-centrosymmetric mono-ligand MOF crystals with high chemical and optical stability that produce entangled photon pairs with intrinsic G(2) correlation times τc∼10−30 fs and pair generation rates in the range 104−1010s−1mW−1mm−1 at 1064 nm. Conditions for optimal type-I phase matching are given for each MOF and relationships between pair brightness, crystal band gap and optical birefringence are discussed. Correlations between the optical properties of crystals and their constituent molecular ligands are also given. Our work paves the way for the computational design of MOF-based devices for optical quantum technology.

Study on overvoltage suppression method of phase modulation of hydroelectric generating units and its cooperative optimization in new energy grid

When a significant portion of renewable energy is transmitted to the power grid through high voltage direct current, when the monopolar blocking occurs, as a result of the converter station’s filter and reactive power compensation equipment’s influence, overvoltage will be generated, which will cause harm to the new energy station, and even lead to large-scale new energy grid disconnection. This paper initially examines the mechanism behind overvoltage in a new energy power grid and then puts forward a method to restrain overvoltage by using phase modulation operation of hydroelectric generating units. To improve the phase modulation capability of hydroelectric generating units and optimize the utilization of new energy sources, this paper proposes a particle swarm optimization to determine the optimal phase modulation capacity. Finally, this paper builds an example of the PSASP platform to verify the correctness and effectiveness of collaborative optimization.

Identifying the Root Causes of DBMS Suboptimality

The query optimization phase within a database management system (DBMS) ostensibly finds the fastest query execution plan from a potentially large set of enumerated plans, all of which correctly compute the same result of the specified query. Sometimes the cost-based optimizer selects a slower plan, for a variety of reasons. Previous work has focused on increasing the performance of specific components, often a single operator, within an individual DBMS. However, that does not address the fundamental question: from where does this suboptimality arise, across DBMSes generally? In particular, the contribution of each of many possible factors to DBMS suboptimality is currently unknown. To identify the root causes of DBMS suboptimality, we first introduce the notion of empirical suboptimality of a query plan chosen by the DBMS, indicated by the existence of a query plan that performs more efficiently than the chosen plan, for the same query. A crucial aspect is that this can be measured externally to the DBMS, and thus does not require access to its source code. We then propose a novel predictive model to explain the relationship between various factors in query optimization and empirical suboptimality. Our model associates suboptimality with the factors of complexity of the schema, of the underlying data on which the query is evaluated, of the query itself, and of the DBMS optimizer. The model also characterizes concomitant interactions among these factors. This model induces a number of specific hypotheses that were tested on multiple DBMSes. We performed a series of experiments that examined the plans for thousands of queries run on four popular DBMSes. We tested the model on over a million of these query executions, using correlational analysis, regression analysis, and causal analysis, specifically Structural Equation Modeling (SEM). We observed that the dependent construct of empirical suboptimality prevalence correlates positively with nine specific constructs characterizing four identified factors that explain in concert much of the variance of suboptimality of two extensive benchmarks, across these disparate DBMSes. This predictive model shows that it is the common aspects of these DBMSes that predict suboptimality, not the particulars embedded in the inordinate complexity of each of these DBMSes. This paper thus provides a new methodology to study mature query optimizers, identifies underlying DBMS-independent causes for the observed suboptimality, and quantifies the relative contribution of each of these causes to the observed suboptimality. This work thus provides a roadmap for fundamental improvements of cost-based query optimizers.

Activated carbon treated with different chemical agents for pertechnetate adsorption

The study investigated the adsorption behaviour of different activated carbon samples for pertechnetate under diverse conditions. Characterizations of A-CF-F and A-CF-Z samples were done using BET, FTIR, XRD, XRF and SEM–EDX methods. In batch experiments, maximum adsorption occured at pH 2, with a swift process and an optimal phase mixing time determined as 1 h for all of the samples. Analyzing experimental data using Langmuir and Freundlich adsorption models revealed coefficients of determination R2 exceeding 90%. The Freundlich isotherm was found to be more suitable for describing the adsorption process.

Efficient implicit constraint handling approaches for constrained optimization problems

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput. Methods Appl. Mech. Eng. 363:112917, 2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.

Nonlinear phase estimation based on nonlinear interferometers with coherent and squeezed vacuum light

Nonlinear interferometers, also known as SU(1,1) interferometers, are unconventional interferometers deploying nonlinear elements. Previous studies showed that they can provide better sensitivity than conventional Mach-Zehnder interferometers regarding the linear phase estimation. In this paper, we make use of nonlinear interferometers with coherent and squeezed vacuum states to perform nonlinear phase estimation. We discuss the optimal phase sensitivities in the absence and presence of photon losses. For a lossless environment, the optimal phase sensitivity can reach the Heisenberg limit scaling of 1/N2. Even with an internal lossy rate of 30% or an external lossy rate of 70%, our scheme can still achieve sub-shot-noise-limited phase sensitivity. These results indicate that our scheme is practical due to high sensitivity and strong robustness.

Joint optimization of component reassignment and working intensity adjusting strategy for multi-state systems with periodic inspection in a shock environment

For some engineering systems performing a mission with long duration, the component positions and working intensity can be dynamically adjusted to improve the efficiency of mission completion and system reliability. Motivated by this reality, a joint strategy of component reassignment and working intensity adjusting is proposed for a multi-component system. Components at different positions can be functionally exchanged. When at least one component fails completely, the system fails. The system operates under a shock environment and the probability that a shock is valid to a component is affected by its position and working intensity. The system can complete more tasks per unit time in a higher working intensity, but the probability that a shock is valid to a component is also larger. The mission is divided into multiple phases, and the action of component reassignment and working intensity adjusting needs to be decided at the beginning of each phase. A Markov decision process is constructed and the optimal mission phase number and strategy are obtained by a finite stage backward recursive iterative algorithm. An Unmanned Aerial Vehicle (UAV) swarm performing an emergency rescue mission is taken as an example to verify the effectiveness of the proposed joint strategy.

Enhancing security for physical layer communication in RIS-aided MIMO-NOMA systems in the presence of an eavesdropper

This paper investigates the potential of leveraging a reconfigurable intelligent surface (RIS) in the multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) wireless communication systems. In fact, NOMA improves spectral efficiency (SE) through power domain utilization, and RIS enhances the system performance by manipulating the propagation environment. In this paper, we investigate the physical layer security (PLS) of a RIS-aided MIMO-NOMA system serving two users in the presence of an eavesdropper (EV), where the channel state information (CSI) of the EV is unknown for the BS. To address the security challenge, we utilize secrecy outage probability (SOP) and minimize it by optimizing the RIS’s phase shift matrix, the beamforming vector, and the power allocation coefficients. Due to the non-convexity of the main optimization problem, we decompose the initial problem into three subproblems, and then, we propose an iterative approach to determine the unknown parameters alternately. The first subproblem involves obtaining the optimal beamforming vector and phase shift matrix using the semidefinite relaxation (SDR) method, which introduces high complexity. As an alternative, another algorithm is proposed based on the duality of the optimization problem, offering lower complexity and better accuracy for high-dimensional optimization problems. In this algorithm, the successive convex approximation (SCA) technique is employed to achieve the optimum phase shift matrix, and the stochastic gradient (SG) approach is used to determine the beamforming vector. Furthermore, the paper establishes the optimal values of power allocation coefficients by minimizing the difference between the SOPs of the two users. Finally, the simulation results demonstrate that the proposed power allocation coefficients scheme significantly enhances system performance compared to the existing fixed power allocation coefficients scheme.

Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems

This study investigates covert communication in multi-sensor systems employing Intelligent Reflecting Surfaces (IRSs). Different from previous works, we focus on optimizing transmission amplitudes and phase angles for a 2-BPSK codebook in the presence of asymmetric noise over complex Gaussian channels. We adopt KL divergence as a covertness constraint and mutual information as a metric for transmission rate. We employ Taylor series expansion to approximate KL divergence and mutual information. Leveraging these approximations, we derive optimal phase angles through a proposed gradient descent algorithm. The numerical simulations validate the effectiveness and precision of our Taylor approximation method. Through validation in different scenarios, our algorithm demonstrates robust convergence, deriving all optimal phase angles. Comparing initial phase angles from prior works to those obtained via our algorithm, we observe a higher covert transmission rate.

Highly sensitive hybrid silicon carbonitride piezoresistive sensors enabled by coupling piezoelectric effect

Piezoresistive sensors with high working temperature and excellent sensitivity are highly desired for in-situ measuring pressure in various high temperature applications. Polymer-derived silicon carbonitrides (SiCNs) are a novel class of materials with excellent stability and piezoresistive performance at high temperature, yet challenging to build to meet the rapid growth of large-area electronics. This work demonstrates a thin and highly sensitive hybrid SiCN piezoresistive films enabled by coupling piezoelectric effect of Al-doped ZnO (AZO). The hybrid thin films with the optimal nano-domain phase of SiCN features ultrahigh gauge factors of 6518 to 31,195 upon various compressive stresses, which are significantly larger than those of single-layer SiCN and other existing piezoresistive materials. First-principles calculations reveal that the charges from AZO layer accumulate and move toward the carbon clusters in SiCN under compressive stress, resulting in greater tunneling currents between carbon clusters and more significant piezoresistive effect. Moreover, superior piezoresistive performance of the optimal hybrid system can be characterized by atomic-scale binding energy and band gap change, as formation of the hybrid structure facilitates interfacial charge separation and increases density of electron at conduction band. This work opens an avenue for improving sensitivity of piezoresistive sensors by effective coupling of piezoelectric materials.

Determining the Right Levels of Health Coaching and Heart Rate Variability Biofeedback in a Workplace Behavior Change Intervention: Multiphase Optimization Strategy Preparation Study.

Work-related stress is associated with poor job performance and negative health outcomes. Changing health behaviors through corporate wellness programs can improve physical and mental health and help employees manage stress. This project sought to pilot the potential addition of brief coaching and biofeedback to an 8-week web-based self-help program to improve employee stress using the multiphase optimization strategy. This study aims to determine which candidate components will be tested in a later optimization phase and at what dose they will be tested, examine the feasibility and acceptability of delivering the different components, investigate whether the outcomes can be feasibly measured, and review evidence to build a conceptual model before the optimization phase. The study was positioned within the preparation phase of the multiphase optimization strategy. It is a 2×2×2×2 design with 4 components: 2 types of health coaching and 2 types of biofeedback. All components were tested by turning them on or off. A total of 16 adult office workers (mean age 40, SD 14.3 years; n=15 women) completed an 8-week self-paced web-based stress management and health behavior change program and were randomly assigned to 1 of the 16 conditions, created from a combination of the 4 candidate components. Assessments included web analytics, surveys, and interviews regarding program recommendations, likes, and dislikes. Findings from the interviews provided suggestions to improve the intervention (eg, separating wellness from stress content) and trial conduct (eg, streamlining the onboarding process). On average, participants logged into the wellness program 83 times (range 36-291), with 75% (12/16) participant retention and 67% (8/12) survey completion. There were no reported problems with coaching or obtaining data from interviews or apps. The interview findings suggested potential mediators to include and assess in a future conceptual model. The results provided areas to improve the intervention content and trial methods. Instead of progressing to the next scheduled large-scale optimization phase, our plan to iterate through a second preparation phase after making changes to the protocol, apps, and corporate coaching partner.

Discovering novel halide perovskite alloys using multi-fidelity machine learning and genetic algorithm.

Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a high-throughput density functional theory (DFT) dataset of halide perovskite alloys can be used to train accurate surrogate models for property prediction and subsequently perform inverse design using genetic algorithm (GA). Our dataset consists of decomposition energies, bandgaps, and photovoltaic efficiencies of nearly 800 pure and mixed composition ABX3 compounds from both the GGA-PBE and HSE06 functionals, and are combined with ∼100 experimental data points collected from the literature. Multi-fidelity random forest regression models are trained on the DFT + experimental dataset for each property using descriptors that one-hot encode composition, phase, and fidelity, and additionally include well-known elemental or molecular properties of species at the A, B, and X sites. Rigorously optimized models are deployed for experiment-level prediction over >150 000 hypothetical compounds, leading to thousands of promising materials with low decomposition energy, band gap between 1 and 2eV, and efficiency of >15%. Surrogate models are further combined with GA using an objective function to maintain chemical feasibility, minimize decomposition energy, maximize PV efficiency, and keep bandgap between 1 and 2eV; thus, hundreds more optimal compositions and phases are discovered. We present an analysis of the screened and inverse-designed materials, visualize ternary phase diagrams generated for many systems of interest using machine learning predictions, and suggest strategies for further improvement and expansion in the future.

Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Optimization of Combustion Cycle Energy Efficiency and Exhaust Gas Emissions of Marine Dual-Fuel Engine by Intensifying Ammonia Injection

The capability of operational marine diesel engines to adapt to renewable and low-carbon fuels is considered one of the most influential methods for decarbonizing maritime transport. In the medium and long term, ammonia is positively valued among renewable and low-carbon fuels in the marine transport sector because its chemical elemental composition does not contain carbon atoms which lead to the formation of CO2 emissions during fuel combustion in the cylinder. However, there are number of problematic aspects to using ammonia in diesel engines (DE): in-tensive formation of GHG component N2O; formation of toxic NOx emissions; and unburnt toxic NH3 slip to the exhaust system. The aim of this research was to evaluate the changes in combustion cycle parameters and exhaust gas emissions of a medium-speed Wartsila 6L46 marine diesel engine operating with ammonia, while optimizing ammonia injection intensity within the limits of Pmax, Tmax, and minimal engine structural changes. The high-pressure dual-fuel (HPDF) injection strategy for the D5/A95 dual-fuel ratio (5% diesel and 95% ammonia by energy value) was investigated within the liquid ammonia injection pressure range of 500 to 2000 bar at the identified optimal injection phases (A −10° CAD and D −3° CAD TDC). Increasing ammonia injection pressure from 500 bar (corresponding to diesel injection pressure) in the range of 800–2000 bar determines the single-phase heat release characteristic (HRC). Combustion duration decreases from 90° crank angle degrees (CAD) at D100 to 20–30° CAD, while indicative thermal efficiency (ITE) increases by ~4.6%. The physical cyclic deNOx process of NOx reduction was identified, and its efficiency was evaluated in relation to ammonia injection pressure by relating the dynamics of NOx formation to local combustion temperature field structure. The optimal ammonia injection pressure was found to be 1000 bar, based on combustion cycle parameters (ITE, Pmax, and Tmax) and exhaust gas emissions (NOx, NH3, and GHG). GHG emissions in a CO2 equivalent were reduced by 24% when ammonia injection pressure was increased from 500 bar to 1000 bar. For comparison, GHG emissions were also reduced by 45%, compared to the diesel combustion cycle.

Comparative Analysis of Simulation Methodologies for Spindle Pumps

This research conducts a comprehensive comparative analysis of simulation methodologies for spindle pumps, with a specific focus on steady-state CFD, transient-CFD, and lumped-parameter approaches. Spindle pumps, renowned for their reliability, efficiency, and low noise emission, play a pivotal role in Thermal Management for Battery Electric Vehicles, aligning with the automotive industry’s commitment to reducing pollutants and CO2 emissions. The study is motivated by the critical need to curtail energy consumption during on-the-road operations, particularly as the automotive industry strives for enhanced efficiency. While centrifugal pumps are commonly employed for such applications, their efficiency is highly contingent on rotational speed, leading to energy wastage in real-world scenarios despite high efficiency at the design point. Consequently, the adoption of precisely designed spindle pumps for thermal management systems emerges as a viable solution to meet evolving industry needs. Recognizing the profound impact of simulation tools on the design and optimization phases for pump manufacturers, this research emphasizes the significance of fast and accurate simulation tools. Transient-CFD emerges as a powerful Tool, enabling real-time monitoring of various performance indicators, while steady-CFD, with minimal simplifications, adeptly captures pressure distribution and machine leakages. Lumped-parameter approaches, though requiring effort in simulation setup and simplifying input geometry, offer rapid computational times and comprehensive predictions, including leakages, Torque, cavitation, and pressure ripple. Breaking new ground, this paper presents, for the first time in the literature, accurate simulation models for the same reference machine using the aforementioned methodologies. The results were rigorously validated against experiments spanning a wide range of pump speeds and pressure drops. The discussion encompasses predicted flow, Torque, cavitation, and pressure ripple, offering valuable insights into the strengths and limitations of each methodology.

A novel multi‐slice electromagnetic field‐circuit coupling method for transient computation of long‐distance gas‐insulated transmission lines

AbstractAccurate calculation of short‐circuit electromagnetic force is crucial for both mechanical strength check and the optimal design of gas‐insulated transmission lines (GIL). Since the full 3D numerical simulation method is highly time‐consuming, a novel lightweight 2D multi‐slice electromagnetic field‐circuit coupled method for computing transient electromagnetic force is proposed, where appropriate port voltage degrees of freedom (DoFs) are introduced for the solid GIL conductor terminals. When the transient magnetic field equations are combined with the constraint equations of circuit part, including nodal voltage and loop current DoFs, a direct field‐circuit coupling scheme is thus derived. The proposed method can simultaneously consider the effect of interphase‐shunts and ground wires, as well as the skin effect and proximity effect. It can accurately capture the transient electromagnetic characteristics of GIL spanning from several to tens of kilometers under different short‐circuit conditions. The transient electromagnetic forces, as well as the induced voltages and currents of the enclosure, are analysed by the proposed method for both single‐phase and three‐phase enclosed GIL under various short‐circuit conditions. The proposed method has the advantages of high accuracy and lightweight computational cost, and thus it is also suitable for conducting important simulation tasks such as mechanical strength checks during the design optimisation phase of long‐distance GIL.

Optimization of flip angle and radiofrequency pulse phase to maximize steady-state magnetization in three-dimensional missing pulse steady-state free precession.

Missing pulse (MP) steady-state free precession (SSFP) is a magnetic resonance imaging (MRI) pulse sequence that is highly tolerant to the magnetic field inhomogeneity. In this study, optimal flip angle and radiofrequency (RF) phase scheduling in three-dimensional (3D) MP-SSFP is introduced to maximize the steady-state magnetization while keeping broadband excitation to cover widely distributed frequencies generated by inhomogeneous magnetic fields. Numerical optimization based on extended phase graph (EPG) simulation was performed to maximize the MP-SSFP steady-state magnetization. To limit the specific absorption rate (SAR) associated with the broadband excitation in 3D MP-SSFP, SAR constraint was introduced in the numerical optimization. Optimized flip angle and RF phase settings were experimentally tested by introducing a linear inhomogeneous magnetic field in a range of 10-20 mT/m and using a phantom with known T1/T2 relaxation and diffusion parameters at 3 T. The experimental results were validated through comparisons with EPG simulation. Image contrasts and molecular diffusion effects were investigated in in vivo human brain imaging with 3D MP-SSFP with the optimal flip angle and RF phase settings. In the phantom measurements, the optimal flip angle and RF phase settings improved the MP-SSFP steady-state magnetization/signal-to-noise ratio by up to 41% under the fixed SAR conditions, which matched well with EPG simulation results. In vivo brain imaging with the optimal RF pulse settings provided T2-like image contrasts. Diffusion effects were relatively minor with the linear inhomogeneous field of 10-20 mT/m for white and gray matter, but cerebrospinal fluid showed conspicuous signal intensity attenuation as the linear inhomogeneous field increased. Numerical optimization achieved significant improvement in the steady-state magnetization in MP-SSFP compared with the RF pulse settings used in previous studies. The proposed flip angle and RF phase optimization is promising to improve 3D MP-SSFP image quality for MRI in inhomogeneous magnetic fields.

Water-exit dynamics of a ventilated underwater vehicle in wave environments with a combination of computational fluid dynamics and machine learning

A ventilated vehicle exiting water in a wave environment is a complex nonlinear process, and the mechanism by which the wave conditions influence this process remains poorly understood. This paper describes realistic simulations of a ventilated vehicle exiting a water body under various wave conditions. Comprehensive analysis is conducted for a range of distinct wave scenarios, and a machine learning-based method is developed for the rapid forecasting of vehicle-related parameters. A three-layer backpropagation neural network is constructed, and its prediction performance is verified. Subsequently, predictive and optimization procedures are employed to determine the optimal wave phase for the water exit of the vehicle. Different wave conditions are shown to significantly affect the evolution of the ventilated cavity as well as the kinematic and loading characteristics of the vehicle. The pitch angular velocity and angle at the moment when the head of the vehicle reaches the free surface exhibit a positive cosine trend under different wave conditions. No regularity of the pitch angular velocity at the moment when the tail reaches the free surface is evident. The neural network exhibits exceptional proficiency in predicting the motion parameters and load characteristics of the vehicle. The optimal point for the vehicle to exit the water is determined to be at a wave phase of 0.125π, while the most hazardous point occurs when the wave phase is 1.1875π.

Spin dynamics of room temperature van der Waals (vdW) ferromagnets and their usage in microwave devices

Quasi-two-dimensional van der Waals (vdW) materials exhibiting room-temperature (RT) long-range ferromagnetic nature have emerged as a significant research field to explore fundamental condensed matter physics due to their intriguing physical properties. These vdW materials enable a futuristic platform for implementing novel spintronics devices. Here, we examined the spin dynamics of polycrystalline Fe5GeTe2 and Fe4.8Co0.2GeTe2 vdW materials using ferromagnetic resonance (FMR) spectroscopy. Vibrating Sample Magnetometer (VSM) study reveals that both materials have a soft ferromagnetic character at room temperature. From room temperature FMR measurements, the effective magnetization of Fe5GeTe2 and Fe4.8Co0.2GeTe2 derived ∼0.54 ± 0.056 and 0.50 ± 0.017 kOe, respectively. These results are consistent with reported VSM data. Fe5GeTe2 and Fe4.8Co0.2GeTe2 exhibit broad FMR linewidths of 0.697 ± 0.036 and 0.748 ± 0.056 kOe, respectively, which can be due to inhomogeneous line broadening. Besides its intrinsic contribution to linewidth, it is also affected by extrinsic Gilbert damping (αext). The value of αext is influenced by conflicting intra-band and inter-band electronic transitions, according to Modified Kambersky's theory. Furthermore, the effective Gilbert damping constant (α) obtained is 0.0513 ± 0.0046 for Fe5GeTe2 and 0.0526 ± 0.0031 for Fe4.8Co0.2GeTe2 at RT. Additionally, we developed microwave signal processing devices using these materials and evaluated their functionality both as a microwave band-reject filter and an adjustable phase shifter. The stop-band response was studied across the 5 to 25 GHz frequency range under an applied magnetic field as high as 7 kOe. For these flip-chip-based devices, attenuation is −5 dB/cm for the Fe5GeTe2-based filter and −3.2 dB/cm on sample Fe4.8Co0.2GeTe2 at 6.95 and 5.37 kOe, respectively. The same micro-strip filter was used as a tunable phase shifter in the off-resonance region. The optimal differential phase shift studied for Fe5GeTe2 and Fe4.8Co0.2GeTe2-based phase shifters in the high-frequency region (22 GHz for Fe5GeTe2 and 18 GHz for Fe4.8Co0.2GeTe2) is 23°/cm and 14°/cm, respectively, at high magnetic fields. These versatile devices find integration across a wide spectrum of applications, such as phased-array antennas, radar systems, and wireless communication systems, offering their benefits to diverse fields.