High Energy Load Research Articles

Influence of mode and design factors on the temperature condition of automobile drum brakes

The current state of the highway pavement makes it possible to implement prompt transportation operations. These circumstances lead to the movement of vehicles at high speeds, which is impossible without a braking system capable of ensuring high braking efficiency and optimal flow of the process from the standpoint of stability and controllability. One of the main requirements for a modern automatic transmission braking system is the stability of the initial parameters, that is, parametric reliability. Therefore, it is important to have data on the brakes' operating modes and energy consumption. Only with such data is it possible to create a braking system whose output characteristic will be sufficiently stable under conditions of high energy load. Therefore, it is no coincidence that the international methodology for testing the effectiveness of vehicle brakes (UNECE Rule 13) provides for Test I, which is characterized by cyclic braking (urban conditions), and Test II, which is characterized by prolonged braking (mountain conditions). The brake mechanism is the most unstable link of the brake system; one way to increase its efficiency is to ensure sufficient energy capacity, which is limited by the temperature of the friction surface. The object of the study is the question of the equivalence of the change in the drum radius and the width of the friction belt of the brake, taking the invariance of the temperature of the friction surface under the selected test mode as the criterion of equivalence. It is shown that the role of the drum's side wall on the brake's temperature mode under different test modes can also be evaluated on grid thermal models with the involvement of the "Fourier–2 x,y,z" software complex. The effect of the heat transfer coefficient on the temperature mode of the brake due to the consideration of the gap between the drum wall and the wheel rim is shown. Derived formulas for determining the friction belt's equivalent width under the equality of heat flows, masses, and cooling surfaces.

Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

AbstractThe most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

Optimal planning of integrated electricity-natural gas distribution systems with hydrogen enriched compressed natural gas operation

In future integrated electricity-natural gas distribution systems (IEGDS), hydrogen and methane converted from excessive renewable generation will be mixed with natural gas to form hydrogen enriched compressed natural gas (HCNG) for fewer carbon emissions. Consequently, optimal planning of the HCNG-integrated IEGDS plays an essential role in increasing the economy and technical benefits. However, integrating HCNG operation into the planning of IEGDS still has many challenges, such as unrealistic modeling, nonlinear constraints, and the potential risk of voltage violations. The purpose of this paper is to develop a tractable optimization model for planning and operation of the HCNG-integrated IEGDS, aiming to minimize the investment and operating cost, reduce the voltage violation, and improve the renewable power penetration rate. To achieve this, a bilevel optimization model is developed for optimal planning of wind turbines and soft open points (SOPs) in the IEGDS considering comprehensive HCNG operation, in which both hydrogen and methane injection are considered and optimized for different system operation scenarios. Furthermore, the proposed nonlinear model is reformulated to a tractable bilevel mixed-integer second-order cone model using several reformulation techniques and solved by the reformulation and decomposition algorithm. Case studies, performed using the publicly available datasets, are conducted to demonstrate the economic and technical improvement by implementing the proposed planning model. The annual planning results show that incorporating the coordination between SOPs and methane injection results in a 6.77% reduction in investment cost, a 22.64% reduction in operating cost, a 62.69% decrease in voltage violation, and a 23.58% increase in the wind power penetration rate. Moreover, SOPs are more applicable to the safe operation of the IEGDS under high energy load conditions.

Unmanned sailboat with self-balancing rotating sail based on elastic rope: Modeling, optimization, and sea trials

Unmanned sailboats use wind energy to assist in propulsion during navigation and are featured by the advantages of clean energy and long endurance, making a hotspot of research on maritime unmanned vehicles. Sails are a core component of unmanned sailboats, and most existing studies on rotating sail have the shortcomings of high energy consumption and load. Therefore, this article proposes a novel method to achieve self-balancing sail rotation based on elastic rope and reduce the energy consumption and load of unmanned sailboats, and establishes a model for the self-balancing rotating sail. We introduced the sum of angular thrust of spherical surface SL as a performance evaluation index for unmanned sailboats, which considers both thrust and navigable range. Based on this index, the mechanical and positional parameters of the model are optimized to obtain the optimal solutions. The performance is increased by 24% after the optimization, and by 76% compared with the sailboat with the fixed sail angle when the same initial sail angle of 38° is adopted. Following the optimal solutions, we develop a prototype of the unmanned sailboat with self-balancing rotating sail. Subsequently, wind turbine experiments are conducted to preliminarily verify the correctness of the model for self-balancing rotating sail and the optimization results. Finally, sea trials are conducted to further verify the performance and seaworthiness of the unmanned sailboat under realistic sea conditions, successfully achieving a 15-day voyage under harsh sea conditions of levels 2 to 4. This study to some extent compensates for the shortcomings of existing studies on rotating sail and provides guidance for subsequent theoretical and engineering research in the field of unmanned sailboats.

Power system operational reliability assessment based on the data center energy consumption elastic space

In the era of big data, data centers with high energy consumption, interconnectivity, and load flexibility have developed rapidly. However, due to data privacy issues, the traditional power-system operational reliability assessment (ORA) struggles to precisely consider the load flexibility of data centers, leading to inaccurate evaluation. To this end, this article proposes an ORA method considering the load flexibility of data centers via the energy consumption elastic space. By transforming the inner operation constraints of data centers into an equivalent elastic space, the ORA does not require any private data to complete the evaluation. Specifically, the energy consumption model of data centers is established to accurately describe the load flexibility. Then, based on multi-parametric programming techniques, the energy consumption elastic space of data centers is characterized by data centers’ power demand constraints, which do not involve privacy data, and no privacy concerns exist. Finally, the ORA model and the evaluation method based on the energy consumption elastic space can be constructed. With a lot of data center operation constraints being replaced by power demand constraints, the proposed method can complete an evaluation faster without accuracy loss. Its effectiveness is validated through simulations using the IEEE RTS 24-bus system and a provincial 661-bus system.

Hybrid energy storage configuration method for wind power microgrid based on EMD decomposition and two-stage robust approach

Data centers are usually characterized by high energy loads, which raises increasing sustainability concerns in both academic and daily usage. To mitigate the uncertainty and high volatility of distributed wind energy generation, this paper proposes a hybrid energy storage allocation strategy by means of the Empirical Mode Decomposition (EMD) technique and the two-stage robust method. First, this paper conducts the evolution analyses for the over- and under-evaluated uncertainty of wind power fluctuation under different time scales. Second, we employ the EMD technique to configure a high-frequency flywheel energy storage device, realizing the wind power transformation from large fluctuations to small fluctuations and the convergence of the wind power fluctuation curves in minute- and hour levels. Finally, based on the hour-level wind energy stable power curves, we carry out two-stage robust planning for the equipment capacity of low-frequency cold storage tanks and lithium bromide chillers. The case study on a data center microgrid in northeastern China confirms the effectiveness of our proposed strategy.

Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

Method for Investigation of the Cooling Process of a Layout of a Lithium Divertor Module (LDM) under High Energy Loads

Method for Investigation of the Cooling Process of a Layout of a Lithium Divertor Module (LDM) under High Energy Loads

Numerical Modeling of Carbides Behavior under High-Energy Loading

Numerical Modeling of Carbides Behavior under High-Energy Loading

A multi-model probabilistic framework to evaluate seismic resilience of UHV converter stations

Ultra-high voltage converter stations are significant electric power infrastructures with a high electric energy load. To quantitatively evaluate the seismic and recovery capability of a UHV converter station, this paper proposes a multi-model probabilistic resilience assessment framework divided into four analysis modules for hierarchical evaluation. The conditional probability set of the functional state between network nodes is determined using a matrix-based approach. Taking connectivity and power transmission capacity into account, the functional reliability model was built through the improved Bayesian network. A multi-parameter quantification of the post-earthquake recovery process was carried out by constructing the stepped functional recovery function as well as the time-varying seismic resilience function. The economic loss assessment model was built from multiple aspects. Taking a typical ± 800 kV UHV converter station as a case study for seismic resilience analysis, the probabilistic resilience assessment was realized, and variation trends of the multi-parameter expected value were obtained by mathematical expectation. This article shows that taking measures to postpone the sensitive interval of the system helps to avoid the seismic active area. This avoidance of seismic activity is the objective of optimization. The proposed framework can serve as a reference for the operation and optimization of power systems.

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.

Characteristics of changes in microstructure and mechanical characteristics under high energy load

Purpose. Verification of the hypothesis about the possibility of obtaining a fine-grained structure of a metal as a result of processing with high-density energies. The methods. The research was carried out on cylindrical samples with a diameter of 50 mm and a length of 300 mm, which were made of structural steel 45 in a normalized state. The thickness of the inner coaxial layer BP1 with a detonation speed D=7.5 km/s was 3 mm, the thickness of the outer layer BP2 with a detonation speed D=3.5 km/s was 40 mm. The two-layer BP charge used in the experiment increases the duration of the shock wave and at the same time protects the samples from destruction. Findings. The mechanism of nanostructuring, in general terms, consists in the accumulation of the degree of deformation without destruction, which leads to the defragmentation of the structure with a significant increase in the density of dislocations. One of the methods that allows you to achieve a high density of dislocations, comparable to IPD, is treatment with shock waves. The change in the grain structure in the places of the pores indicates extremely high degrees of inhomogeneous deformation in these areas. During the passage of the shock wave in the middle of the sample, at the boundaries of the interface of phases with different densities, diffraction wave effects occur, which lead to the occurrence of shear deformations. It can be assumed that in the area of pores and microcracks, the deformation conditions are similar to those realized by some methods of intensive plastic deformation. This leads to a sharp grinding of the grain structure and a corresponding change in mechanical properties. The originality. The paper found that the processing of steel parts with the help of high-density energy leads to a sharp grinding of the grain to the nanostructure size and, as a result, to an increase in mechanical properties. The revealed regularities make it possible to obtain optimal parameters of high-energy processing, which lead to an increase in hardness, a redistribution of internal stresses and a decrease in surface roughness. Practical implementation. Obtaining grains with nanostructural characteristics in the structure of the material will allow to create fundamentally new devices and materials. Such materials will have properties significantly exceeding their achieved level – which is important for many fields of engineering, medicine, biotechnology, environmental protection, defense, etc.

Demand response scheduling based on blockchain considering the priority of high load energy enterprises

In order to improve the consumption capacity of wind power plants and deal with the problems of high cost, high risk and low efficiency in traditional demand response, this paper proposes a new optimal scheduling strategy of demand response based on blockchain and the priority of high-capacity enterprises. Based on the analysis of different high-energy load types, the load response dispatching priority and hierarchical electricity price strategy are first put forward. Then the transaction framework of load aggregator, wind power, high-energy enterprise based on blockchain is constructed. Furthermore, the optimization model of load aggregator participating in dispatching is established, which is optimized by multi-objective particle swarm optimization (MOPSO) algorithm. Finally, through the simulation of a certain day in a certain area, the income of aggregators and the cost of wind power plants participating in this dispatching transaction before and after adopting this demand response dispatching scheme are analyzed and compared. It is verified that the demand response scheduling scheme proposed in this paper can effectively guarantee the revenue of load aggregators and improve the wind power consumption level.

Regulation of Star Formation by a Hot Circumgalactic Medium

Galactic outflows driven by supernovae (SNe) are thought to be a powerful regulator of a galaxy’s star-forming efficiency. Mass, energy, and metal outflows (η M , η E , and η Z , here normalized by the star formation rate, the SNe energy, and metal production rates, respectively) shape galaxy properties by both ejecting gas and metals out of the galaxy and by heating the circumgalactic medium (CGM), preventing future accretion. Traditionally, models have assumed that galaxies self-regulate by ejecting a large fraction of the gas, which enters the interstellar medium (ISM), although whether such high mass loadings agree with observations is still unclear. To better understand how the relative importance of ejective (i.e., high mass loading) versus preventative (i.e., high energy loading) feedback affects the present-day properties of galaxies, we develop a simple gas-regulator model of galaxy evolution, where the stellar mass, ISM, and CGM are modeled as distinct reservoirs which exchange mass, metals, and energy at different rates within a growing halo. Focusing on the halo mass range from 1010 to 1012 M ⊙, we demonstrate that, with reasonable parameter choices, we can reproduce the stellar-to-halo mass relation and the ISM-to-stellar mass relation with low-mass-loaded (η M ∼ 0.1–10) but high-energy-loaded (η E ∼ 0.1–1) winds, with self-regulation occurring primarily through heating and cooling of the CGM. We show that the model predictions are robust against changes to the mass loading of outflows but are quite sensitive to our choice of the energy loading, preferring η E ∼ 1 for the lowest-mass halos and ∼0.1 for Milky Way–like halos.

S1P Released by SGPL1-Deficient Astrocytes Enhances Astrocytic ATP Production via S1PR2,4, Thus Keeping Autophagy in Check: Potential Consequences for Brain Health.

Astrocytes are critical players in brain health and disease. Sphingosine-1-phosphate (S1P), a bioactive signaling lipid, is involved in several vital processes, including cellular proliferation, survival, and migration. It was shown to be crucial for brain development. Its absence is embryonically lethal, affecting, inter alia, the anterior neural tube closure. However, an excess of S1P due to mutations in S1P-lyase (SGPL1), the enzyme responsible for its constitutive removal, is also harmful. Of note, the gene SGPL1 maps to a region prone to mutations in several human cancers and also in S1P-lyase insufficiency syndrome (SPLIS) characterized by several symptoms, including peripheral and central neurological defects. Here, we investigated the impact of S1P on astrocytes in a mouse model with the neural-targeted ablation of SGPL1. We found that SGPL1 deficiency, and hence the accumulation of its substrate, S1P, causes the elevated expression of glycolytic enzymes and preferentially directs pyruvate into the tricarboxylic acid (TCA) cycle through its receptors (S1PR2,4). In addition, the activity of TCA regulatory enzymes was increased, and consequently, so was the cellular ATP content. The high energy load activates the mammalian target of rapamycin (mTOR), thus keeping astrocytic autophagy in check. Possible consequences for the viability of neurons are discussed.

Beryllium dust generation caused by HHF tests

Beryllium will be used as a plasma-facing material of the ITER reactor components. One such component is the ITER first-wall panel, which contains a layer of beryllium tiles on the plasma-facing surface. Full-Scale Prototypes (FSP) of these panels are undergoing qualification through a series of cyclic High-Heat-Flux (HHF) tests at the HELCZA (High Energy Load Czech Assembly) facility. Such process, directly and indirectly, causes disintegration of the beryllium surface and results in beryllium dust formation. With regards to the beryllium toxicity, it is crucial to monitor the occurrence and the spread of the mentioned dust to ensure personnel safety. Moreover, it is necessary to decontaminate FSPs from the beryllium dust at the end of HHF tests. This study deals with the monitoring of the beryllium dust concentration levels on the FSP after HHF testing at HELCZA and its spread to the vicinity of the testing area, with the aim to provide a basis for safe operation.

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed, a multi-time scale model of coordination optimization is built. An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs, as well as to find the most optimal energy configuration within the system. Lastly, we take an example of regional power grid in Gansu Province for simulation analysis. Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

Update in combined musculoskeletal and vascular injuries of the extremities.

Combined musculoskeletal and vascular injuries of the extremities are conditions in which a multidisciplinary approach is a sine qua non to ensure life initially and limb viability secondarily. Vascular injuries as part of musculoskeletal trauma are usually the result of the release of a high energy load in the wound site so that the prognosis is determined by the degree of soft-tissue damage, duration of limb ischemia, patient’s medical status and presence of associated injuries. The management of these injuries is challenging and requires a specific algorithm of action, because they are usually characterized by increased morbidity, amputation rate, infection, neurological and functional deficits, and they could be life threatening. Although vascular injuries are rare and occur either isolated or in the context of major combined musculoskeletal trauma, the high index of suspicion, imaging control, and timely referral of the patient to organized trauma centers ensure the best functional outcome of the extremity in such challenging cases. Even after a successful initial treatment of a combined trauma pattern, long-term follow-up is crucial to prevent and detect early possible complications. The purpose of this manuscript is to provide an update on diagnosis and treatment of combined musculoskeletal and vascular injuries of the extremities, from an orthopedic point of view.

Iron loss analysis and calculation of high energy density permanent magnet machine

Abstract It is a research hotspot of electric propulsion system that a high energy density permanent magnet machine is used as its main power. In general, the power system of electric propulsion consists of batteries, inverters and high energy density permanent magnet machines and loads. Based on harmonic effect of PWM sine alternating current generated by inverter, iron loss model of high energy density permanent magnet machine is established under inverter power harmonic, and iron loss calculation flow chart of high energy density permanent magnet machine for electric propulsion system is shown. The influences of different stator outer diameter and rotor inner diameter on iron loss are analyzed by using the finite element method. Through the above analysis, a 30 kW high energy density permanent magnet machine was designed. Then the noload test and iron loss separation test were carried out, verifying that the machine has very low core loss.

Comprehensive analysis of in-plane tensile characteristics of thin carbon/aramid hybrid composites using experimental and RVE- based numerical study

The phenomenal growth in the developments of micro/mini Unmanned Aerial Vehicles needs thin composite materials for its structural applications for weight reduction. As these structures experience high energy loading during their life cycle, the performance improvement of overall failure strain and toughness is deemed compulsory. This study investigates the application of woven aramid fabric with thin woven CFRP in enhancing the strain to failure behaviour. In this study, the in-plane tensile properties of hybrid carbon/aramid polymer composite were investigated using experimental test followed by numerical validation. Composite laminates with different hybrid fraction and stacking sequences at the inter-ply level were tested under uniaxial tensile load. The results showed that the stacking sequences and location of high strain materials had evidently influenced the primary and secondary tensile modulus, toughness, failure strains and damage morphology. The numerical simulation was performed in ANSYS Composite Prep-Post module using homogenized materials properties based on the microscale RVE model. The Puck failure criteria with material performance degradation for progressive damage analysis is employed to capture the stress-strain relations. The hybrid samples exhibited significant improvement in the failure strain and energy absorption compared to CFRP. The numerical and experimental data showed a good correlation.