Configuration Method Research Articles

Identifikasi Bidang Gelincir Zona Rawan Longsor di Kawasan Wisata Puncak Taruko Kabupaten Agam Menggunakan Metode Geolistrik Resistivitas

A research has been conducted to identify the surface of rupture plane in Puncak Taruko area, Agam Regency, West Sumatra Province using 2-dimensional resistivity geoelectric method of Wenner-Schlumberger Configuration. Data collection was carried out on three tracks with track lengths of 80, 100 and 100 m and 5 m electrode spacing. The research area is a plateau with a slope of up to 40º which is directly adjacent to the very steep cliffs of Sianok Canyon with a cliff height of 30-100m. Data processing was carried out using Res2dinv software to display a 2-dimensional image of the subsurface layer structure based on the resistivity values measured in the field. In the cross section of the mapping results in the three tracks, 4 rock layers were identified, namely clay, sand, tuff and granite. The interpretation results show that the sliding plane on each track is tuff. For Track 1 with a depth of 3,75-15,9m, for track 2 it is 5,15-19,8 m and a depth of 6,38-19,8 m for track 3. Based on the interpretation of 2D image results, the area of track 1 and 2 has a translational avalanche type and the area of track 3 has a rotational avalanche type. Based on the depth and thickness of the slide field, the greatest risk of landslide is on track 3.

An improved uncooperative space target de-tumbling method using electromagnetic de-tumbling devices with AC excitation

A large amount of space debris with rotational motion occupies limited orbital resources, necessitating a de-tumbling phase before the capturing phase to mitigate collision risks. To enhance the safety and performance of de-tumbling uncooperative space targets, this paper proposes and investigates a contactless method using electromagnetic de-tumbling devices with alternating current (AC) excitation, which can generate larger de-tumbling torque compared to conventional direct current (DC) excitation. First, the optimal configuration of the devices for de-tumbling the target is studied under the constraint of constant power consumption. Then, the mechanism of de-tumbling torque generated by AC excitation is analyzed, leading to the development of the optimal AC excitation de-tumbling method. Subsequently, the optimal configuration and AC excitation de-tumbling method are validated through finite element method simulations, confirming the advantages of the proposed method. Finally, ground experiments demonstrate the superiority of AC excitation over DC excitation.

Virtually coupled train set control subject to space-time separation: A distributed economic MPC approach with emergency braking configuration

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set (VCTS) at a minimal but safe distance. To guarantee collision avoidance, the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking (EB) trajectories of two successive units during the whole EB process. In this case, the minimal safety distance is usually numerically calculated without an analytic formulation. Thus, the constrained VCTS control problem is hard to address with space-time separation, which is still a gap in the existing literature. To solve this problem, we propose a Distributed Economic Model Predictive Control (DEMPC) approach with computation efficiency and theoretical guarantee. Specifically, to alleviate the computation burden, we transform implicit safety constraints into explicitly linear ones, such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently. For theoretical analysis, sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC, employing compatibility constraints, tube techniques and terminal ingredient tuning. Moreover, we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS. Finally, experimental results demonstrate the performance and advantages of the proposed approaches.

Collaborative fault detection and diagnosis method for dual single-axis RINSs configuration in GNSS-denied environment

To ensure the reliability and safety of the long-endurance navigation, two or more rotational inertial navigation systems (RINSs) are usually equipped on ships. Through the redundant configuration, fault detection and diagnosis (FDD) can be realized. In this paper, a collaborative FDD method applied to dual homogeneous single-axis RINSs is proposed. The method utilizes geometric constraint observation to construct a detection filter, processes the filtering outputs through the modified Bayesian algorithm, and incorporates a fault confirmation stage to achieve fault diagnosis with stronger robustness. Besides, an azimuth gyro drift variation estimation method using least-squares is further proposed by exploiting the difference in Schuler oscillation mode resulting from an azimuth gyro fault as opposed to the fault-free state when the system is damped by the log. The efficiency and reliability are demonstrated through comparative and repeated experiments. According to the results based on repeated experiments, the proposed method achieves a comprehensive correct diagnosis rate of over 99% across various fault scenarios. When relatively significant faults occur, the average diagnosis time for horizontal gyros, horizontal accelerometers, and azimuth gyros is 0.4569h, 0.2686h, and 1.2204h respectively. In the case of more subtle faults, although the average diagnosis time is extended to 0.5920h, 0.2991h, and 1.9641h, the effectiveness of the method is still well demonstrated.

Capacity configuration of cascaded hydro-wind-photovoltaic complementary systems considering comprehensive benefits based on a synergetic-orderly framework

The rational configuration of hydro-wind-photovoltaic complementary system is fundamental to fully leveraging the regulation capacity of hydropower and the complementarity between different natural resources. However, complementary systems involving cascaded reservoirs often have comprehensive utilization requirements, which current techno-economic frameworks struggle to address. To solve the above issues, this study proposes a novel framework based on the theory of synergetics to determine the optimal capacities for wind and photovoltaic power considering the comprehensive benefits. Firstly, an inner operation model aimed at maximizing overall order degree is established to optimize the system’s operational performance. Secondly, Kolmogorov entropy is utilized in the outer layer to characterize the synergy of different wind and photovoltaic capacity schemes, thereby selecting the one with the best synergistic effect. Additionally, current techno-economic evaluation indicators are introduced to validate the framework’s effectiveness. A case study of the clean energy base on the upper Yellow River was conducted, and the results indicate that: (1) The proposed framework effectively addresses the comprehensive benefits of multiple subsystems and the optimal capacity scheme performs well in economic and technical aspects. (2) Compared to variations in wind and solar resources, inflow conditions and agricultural water demand have a greater impact on the capacity configuration and operational performance. (3) The optimal capacity ratio of hydro to renewable energy in the upper Yellow River is around 1:0.59. Therefore, this study provides important theoretical support for expanding capacity configuration methods in multi-energy complementary systems.

Resilience Improvement of Microgrid Cluster Systems Based on Two-Stage Robust Optimization

For microgrids in deep and remote sea areas, ocean currents are widely used as an emerging power generation resource. Implementing ES systems is crucial for smooth power output and grid stability. The stability of power output from sea current energy generation faces challenges due to speed fluctuations. Enhancing resilience requires addressing transmission line failures caused by extreme seabed conditions, and ensuring operational security. An ES configuration method considering line faults based on two-stage robust optimization is presented in this paper. First, in order to simultaneously consider planning and operation, a defender–attacker–defender (DAD) model was established. Additionally, the capacity, rated power, and charging/discharging power of ES during operation were jointly optimized through the column-and-constraint generation (C&CG) algorithm. In addition, the rationality and effectiveness of the proposed method were demonstrated through experiments on modified IEEE six-bus and fifty-seven-bus systems. The results show that a distributed ES configuration increased system resilience by 54.60% and reduced abandoned power rate by 57.06% compared with the situation without ES configuration.

Crystal growth and characterization of Fe1+δSe1-xTex (0.5 ≤ x ≤ 1) from LiCl/KCl flux

An eutectic LiCl/KCl flux method in a horizontal configuration has been used to grow a series of homogeneous Fe1+δSe1-xTex single crystals of high quality with 0.5 ≤ x ≤ 1. Compared with previously used melt-growth method, the stable crystallization process in LiCl/KCl flux below their peritectic temperatures results in better homogeneity and crystalline perfection identified by energy dispersive spectrometer and x-ray diffraction. The interstitial Fe value δ remains small within 0.5 ≤ x ≤ 0.85 where the superconducting temperature TC is not sensitive to the Te content with sharp superconducting transition widths ΔTC < 1 K and a maximum of TC = 14.3 K at x = 0.61. The value δ starts to increase quickly accompanied by a deviation of linear behavior of crystal lattice parameters as well as the broadening of ΔTC = 2.1 K at x = 0.91, then suddenly rises up to δ > 0.1 followed by the disappearance of superconductivity and emergence of antiferromagnetic order at x ≥ 0.96. We also observed a metallic to semiconducting transition in the normal state resistivity of Fe1+δSe1-xTex with increasing Te content which is related to a localized electronic state induced by the interstitial Fe. The interstitial Fe value δ might be a key physical parameter to understand various properties of Fe1+δSe1-xTex system.

Optimal configuration of hydrogen storage capacity of hybrid microgrid considering peak regulation and frequency modulation requirements

This study proposes an innovative hydrogen storage capacity optimization configuration method that considers multiple demand factors, addressing the issue that traditional methods for optimizing hydrogen storage capacity in hybrid microgrids cannot simultaneously balance economic efficiency and grid connection quality. This method breaks through the traditional optimization framework and adopts a double-layer optimization model, combining the peak shaving operation cost of the hybrid microgrid with the interactive power deviation and power fluctuation penalty terms of the interconnection line during the frequency regulation stage, achieving dual optimization of economy and grid connection quality. Through precise mathematical modeling and efficient optimization algorithms, we have successfully obtained the minimum operating cost corresponding to the minimum power fluctuation, the minimum interconnection line interactive power deviation penalty term, and the optimal hydrogen storage capacity configuration scheme. Identify two typical days through cluster analysis. Option 1 (considering peak shaving, frequency regulation, and hydrogen electrolysis) has the lowest net load and a total operating cost of 3.0331 million yuan, which is better than Option 2 (3.0973 million yuan) and Option 3 (5.0398 million yuan). This plan effectively reduces wind and solar power waste, shortens the operating time of thermal power units, and demonstrates the rationality and economy of optimizing hydrogen storage capacity configuration.

Effect of Land Configuration and Planting Depth on Corm and Cormel Production of Gladiolus (Gladiolus spp.)

The present experiment was carried out at department of Floriculture and Landscape Architecture, College of Horticulture, Junagadh Agricultural University, Junagadh (Gujarat) during the years 2021-22 and 2022-23 with randomized block design (Factorial) having three replications. The treatment combinations consisting three land configuration methods (L1- Flat bed, L2- Raised bed with 2 rows and L3- Ridge and furrow) and two planting depth (D1- 3.5 cm and D2- 5.5 cm). The results indicated that maximum number of corms (1.58), number of cormels (26.98), diameter of corms (63.77 mm), diameter of cormels (3.16 mm), weight of corms (64.13 g) and weight of cormels (22.11 g) were recorded with raised bed with 2 rows (L2). The variation due to planting depth was also found significant for all the parameters and maximum number of corms (1.46), number of cormels (25.52), diameter of corms (56.43 mm), diameter of cormels (3.09 mm), weight of corms (56.02 g) and weight of cormels (21.78 g) were obtained with planting at 3.5 cm depth (D1). Raised bed planting had favourable effects on growth and flowering of gladiolus as compared to ridge and furrow and flat bed planting.

Analysis of power load tracking and regulation performance in a distributed multi-energy coupled system with nuclear and solar sources

This paper presents a novel distributed multi-energy coupled system that combines solar PV, nuclear power, and energy storage systems to address the power supply challenges in remote regions. Through parameter analysis and multi-objective optimization, the study aims to minimize the need for frequent reactor adjustments during system operation. The key findings indicate that by regulating the rotational speed of the main-compressor, it is possible to meet the power load requirements for a single nuclear power system. The battery capacity and the number of PV panels have a significant impact on the adjustment frequency of the reactor and the power output of the nuclear power system, respectively. The optimal battery capacity is determined to be 183 kWh, while the optimal number of PV panels is 327. These configurations result in an average power output of 20.86 kW for the PV system and 3458.28 kW for nuclear power system. To maximize the utilization of PV energy and minimize reactor adjustments, an energy dispatch strategy is proposed. With the optimal configuration, the adjustment frequency of the reactor decreases to 339 and 276 during the winter and summer months, respectively. Overall, this paper offers a feasible configuration and energy dispatch method for regional power supply.

Optimal configuration method of photovoltaic energy storage in distribution network based on source-load coordination

Abstract To enhance the configurability of photovoltaic energy storage within distribution network systems and foster synchronized development of power sources and loads, a source-load coordinated approach for optimal photovoltaic energy storage configuration in distribution networks is introduced. An alternative multi-objective framework for optimal allocation of photovoltaic energy storage capacity in distribution networks is formulated, which is the optimal goal of maximum economic benefit of photovoltaic energy storage, the optimal goal of minimum network loss and the optimal goal of source-network load coordination. The constraints of the system’s power flow, energy storage charging and discharging capabilities, and an optimized allocation strategy for energy storage are established, and the objective function is solved with full consideration of source-network load coordination factors. The energy storage optimization is updated in the iterative process. Based on the update results, the process for optimal allocation of photovoltaic energy storage in the distribution network has been devised to attain the most efficient allocation. Experimental results indicate a minimal discrepancy between the actual and specified energy storage output, along with a reduced average output power resulting from the optimized photovoltaic energy storage configuration, which shows excellent performance in energy storage optimization configuration.

Research on the container configuration for revolution-assisted horizontal vibration finishing of aero-engine blades

Vibration finishing has been widely used to improve aero-engine blades' surface quality and service performance. However, there are still problems after processing, such as uneven surfaces and large removal materials of the inlet and exhaust edge. The application of vibratory finishing is greatly limited. In order to realize the uniform processing of blades, a process scheme of revolution-assisted horizontal vibratory finishing was proposed. A reverse method of container configuration is proposed to change the force distribution at each blade position and realize the blade's machining uniformity. The results show that normal force is the main factor affecting the processing effect. When the blade is fixed in the container, the processing effect of the downward surface of the blade is related to the distance from the container bottom. The discrete signal of the normal force is transformed into a vector, and the difference coefficient CD is proposed. Based on the inverse method, it was found that increasing the distance between the blade's lower side and the container bottom and reducing the distance between the blade's upper side and the container bottom will reduce the normal force difference at different revolution stages. After the blade was processed with the reverse container, the surface roughness decreased from 0.85 ± 0.05 μm to 0.22 ± 0.02 μm, and the standard deviation of Ra decreases to 0.046. The average residual stress and its standard deviation are reduced to −332.51 MPa and 5.573. The research provides methods and ideas for designing container configuration and the uniformity of complex curved surface parts in mass finishing.

Automatic recognition of excavator working cycles using supervised learning and motion data obtained from inertial measurement units (IMUs)

This paper proposes an automatic method for excavator working cycle recognition using supervised classification methods and motion information obtained from four inertial measurement units (IMUs) attached to moving parts of an excavator. Monitoring and analyzing tasks that have been performed by heavy-duty mobile machines (HDMMs) are significantly required to assist management teams in productivity and progress monitoring, efficient resource allocation, and scheduling. Nevertheless, traditional methods depend on human observations, which are costly, time-consuming, and error-prone. There is a lack of a method to automatically detect excavator major activities. In this paper, a data-driven method is presented to identify excavator activities, including loading, trenching, grading, and idling, using motion information, such as angular velocities and joint angles, obtained from moving parts, including swing body, boom, arm, and bucket. Firstly, a dataset lasting 3 h is collected using a medium-rated excavator. One experienced and one inexperienced operator performed tasks under different working conditions, such as different types of material, swing angle, digging depth, and weather conditions. Four classification methods, including support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and naive Bayes, are off-line trained. The results show that the proposed method can effectively identify excavator working cycles with a high accuracy of 99%. Finally, the impacts of parameters, such as time window, overlapping configuration, and feature selection methods, on the classification accuracy are comprehensively analyzed.

Finite element solution of coupled multiphysics reaction-diffusion equations for fracture healing in hard biological tissues

This study proposes a computational framework to investigate the multi-stage process of fracture healing in hard tissues, e.g., long bone, based on the mathematical Bailon-Plaza and Van der Meulen formulation. The goal is to explore the influence of critical biological factors by employing the finite element method for more realistic configurations. The model integrates a set of variables, including cell densities, growth factors, and extracellular matrix contents, managed by a coupled system of partial differential equations. A weak finite element formulation is introduced to enhance the numerical robustness for coarser mesh grids, complex geometries, and more accurate boundary conditions. This formulation is less sensitive to mesh quality and converges smoothly with mesh refinement, exhibiting superior numerical stability compared to previously available strong-form solutions. The model accurately reproduces various stages of healing, including soft cartilage callus formation, endochondral and intramembranous ossification, and hard bony callus development for various sizes of fracture gap. Model predictions align with the existing research and are logically coherent with the available experimental data. The developed multiphysics simulation clarifies the coordination of cellular dynamics, extracellular matrix alterations, and signaling growth factors during fracture healing.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

Per-Instance Algorithm Configuration in Homogeneous Instance Spaces: A Use Case in Reconfigurable Assembly Systems

The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithm configuration in such homogeneous contexts traditionally adopts a “one-size-fits-all” approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra—a state-of-the-art PIAC method—and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method’s performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithm configuration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra’s methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra’s inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR.

Adaptive configuration control of combined UAVs based on leader-wingman mode

Modular Unmanned Aerial Vehicles (UAVs) can adapt to rapidly changing payload requirements based on the shape and weight of the load by adding or subtracting units, reconfiguring, or changing the type of units. The existing research has addressed aerial docking and hover control post-docking but fails to achieve coordinated flight following combination, leading to delayed response and oscillations as the number of UAV units increases. Moreover, the configuration of modular UAVs is complex and variable, making it challenging to adjust the controller parameters of each unit online. Therefore, this paper presents: (A) Adaptive attitude allocation method for different combined UAV configurations: establishing a mapping relationship between constant controller parameters of the unit and the combination angular acceleration. The desired torque of the combination is allocated based on the size of the lever arm, enabling adaptive attitude control of the combination for varying configurations by controlling the attitude of the local unit; (B) A power allocation strategy based on a leader-wingman mode: employing a leader to control the entire combination, distributing the combination’s force and torque to wingman units according to the mapping relationship of the attitude allocation method. This transforms the complex control of the combination into unit control in the leader-wingman mode. Compared to current average allocation methods, the step response of attitude angle improves by about 60% on average, and spatial trajectory tracking increases by an average of 11.5%. As the number of units grows, the response of the combination becomes similar to that of a single, independently flying UAV, resolving the oscillation issue in combined flight. Additionally, this approach eliminates the need to change the controller parameters of all units, facilitating convenient reconfiguration and coordinated flight for modular UAVs post-combination.

Research on a Distributed Photovoltaic Two-Level Planning Method Based on the SCMPSO Algorithm

In response to challenges such as voltage limit violations, excessive currents, and power imbalances caused by the integration of distributed photovoltaic (distributed PV) systems into the distribution network, this study proposes at two-level optimization configuration method. This method effectively balances the grid capacity and reduces the active power losses, thereby decreasing the operating costs. The upper-level optimization enhances the distribution network’s capacity by determining the siting and sizing of distributed PV devices. The lower-level aims to reduce the active power losses, improve the voltage stability margins, and minimize the voltage deviations. The upper-level planning results, which include the siting and sizing of the distributed PV, are used as initial conditions for the lower level. Subsequently, the lower level feeds back its optimization results to further refine the configuration. The model is solved using an improved second-order oscillating chaotic map particle swarm optimization algorithm (SCMPSO) combined with a second-order relaxation method. The simulation experiments on an improved IEEE 33-bus test system show that the SCMPSO algorithm can effectively reduce the voltage deviations, decrease the voltage fluctuations, lower the active power losses in the distribution network, and significantly enhance the power quality.

Single bundle hamstrings autograft preparation techniques for anterior cruciate ligament reconstruction: current concepts.

Anterior cruciate ligament reconstruction (ACLR) is one of the most common orthopedic procedures, and huge variation exists in the surgical technique. Single bundle hamstrings autograft reconstruction is a common method and has good clinical outcomes. A criticism of the hamstrings autograft is a small graft diameter, often less than 8-mm, which has been associated with increased re-rupture rates. Several graft preparation techniques for single bundle hamstrings autograft exist. Perioperative decisions include the number of tendons utilized, number of graft strands, graft configuration, and femoral and tibial fixation methods. Awareness of the minimum tendon and graft length required to produce each graft variation is necessary to avoid common pitfalls. However, intraoperative graft modification is possible to maximize graft diameter, and obtain proper fixation. The objective of this current concepts review is to describe the indications, surgical anatomy, technique, intraoperative tips, clinical outcomes, and complications for single bundle hamstrings autograft preparation techniques in ACLR.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.