Varying Load Levels Research Articles

Real-time energy management simulation for enhanced integration of renewable energy resources in DC microgrids

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency.

Precise Fault Location in Active Distribution Systems Using Unsynchronized Source Measurements

This article presents a phase-domain based fault location algorithm for unbalanced power distribution systems with distributed generation. Unsynchronized voltage and current measurements, taken from the sources prior and during the fault, are utilized to calculate the equivalent source impedances and the incremental current quantities required. The latter are substituted into the fault location equations, which are solved to determine the fault resistance(s), the synchronization angles, and the possible fault positions. Multiple possible fault position estimations are eliminated by applying an index-based methodology to find the exact fault position. Extensive simulations are performed for a benchmark distribution system with conventional and inverter-interfaced generation units, considering all types of common short-circuit faults, several fault resistance magnitudes, varying load levels, different distributed generation (DG) mixture, and the impact of line segment length. The results show that fault distance is accurately estimated by the proposed algorithm.

Research on power grid energy saving and loss reduction control methods under the harmonic influence

To reduce energy consumption and power grid operation costs, a control method that considers the influence of harmonics is proposed for power grid energy saving and loss reduction. The zip load model is utilized to build the power grid load model, and voltage characteristic curves are obtained under different parameters to calculate network loss. Furthermore, the voltage RMS and total harmonic distortion of nodes are calculated under varying load levels. Ultimately, a control objective function is established to achieve power grid energy saving and loss reduction. Experimental results indicate that this method outperforms traditional methods in terms of voltage response effect and achieves a more extensive reduction range of network loss, with a maximum reduction exceeding 35%.

Evaluation of asphalt concrete’s fatigue behavior using cyclic semi-circular bending test

Fatigue cracking in asphalt concrete (AC) is one of the major distresses that cause premature failure of flexible pavements. Fatigue tests are often lengthy, cumbersome, and expensive. Therefore, in the performance-related mix-design specifications, some static tests are recommended due to their simplicity. The monotonic semi-circular bending (SCB) test is among the most popular. Even though the SCB test is practical, the actual number of cycles to failure due to cyclic loads cannot be obtained. Implementing SCB testing under cyclic loading conditions for AC performance evaluation could be promising. This study aims to develop a cyclic SCB fatigue testing method. To this end, SCB specimens were produced, and static SCB fracture testing was first conducted to determine the loading levels of the cyclic SCB fatigue test. Then, cyclic SCB tests were conducted by varying load levels to obtain the S-N (stress-fatigue life) curves. To demonstrate the applicability of the proposed framework to differentiate fatigue behavior of mixtures, two AC mixtures were designed and subjected to the proposed protocol, varying the type of aggregate (gneiss and mica schist). Additionally, the S-N curves from cyclic SCB tests were compared to the ones from indirect tensile (IDT) fatigue tests. A considerable similarity in the results between the two approaches (SCB vs. IDT) under cyclic loading was observed, while SCB showed good test repeatability. The SCB configuration with a pre-notch allows fracture mechanics-based testing to monitor the cracking mechanisms and promote rapid cracking evolution, which can reduce testing time.

Improving the combustion and emission performance of a diesel engine powered with mahua biodiesel and TiO2 nanoparticles additive

The dwindling air quality and diminishing fossil fuel reserves have led to an increase in the need for alternative sources of fuel. Biodiesel derived from animal/plant sources seems to be an attractive substitution. However, there are some shortcomings of biodiesel like lower thermal efficiency, poor atomization, inferior heating value, and higher molecular mass. One of the probable novel ways to address this lacuna is by the use of nano based biodiesel in IC engine. The present study performs an endeavor to improve the performance and emulsion qualities of biodiesel generated from mahua oil by employing Titanium oxide (TiO2) nanoparticles as an additive. All trials were conducted in a diesel-powered engine at varying load levels with different test fuel such as diesel fuel, blended biodiesel (80 % diesel + 20 % mahua biodiesel), and nanoparticle-based blended biodiesel (80 % diesel + 20 % mahua biodiesel – 200 mg/L TiO2). When compared to diesel mode, the average CO emission reduction for blended biodiesel and nanoparticle-based blended biodiesel is 37.42 % and 46.54 %, respectively. In comparison to diesel mode, the average reduction in HC emission is 22.54 % for the blended biodiesel and 28.4 % for the nanoparticle-based blended biodiesel, respectively. When compared to diesel mode, the average reduction in NOx emission is 4 % and 2.3 % for the blended biodiesel and nanoparticle-based blended biodiesel, respectively.

Application of nanomaterials for emission reduction from diesel engines powered with waste cooking oil biodiesel

Abstract Diesel engines that predominantly run on fossil-based diesel fuel emit harmful emissions. Biofuels such as biogas and biodiesel are potential alternatives. However, many biofuels have poor heating values and supply inconsistencies. The use of nanoparticles (NPs) offers an appealing alternative for enhancing the combustion and emission reduction. This study aimed to improve the emission performance of biodiesel produced from waste cooking oil biodiesel (WCOB) by adding titanium oxide (TiO2) NPs. Tests were conducted in the diesel-powered engine at varying load levels using fuel mixtures of B1 (100% diesel), B2 (80% diesel + 20% WCOB) and B3 (80% diesel + 20% WCOB with the addition of 200 mg/l TiO2 NPs). In comparison with the diesel mode, the average reduction in carbon monoxide emissions was 8.92% for the B2 blend and 25% for the TiO2 NP-based B3 blend. For the B2 blend and TiO2-based B3 blend, the mean reduction in hydrocarbon emissions was 5.6 and 16.6%, respectively, when compared with diesel mode. When comparing B3 blends with B2 blends, the average reduction in nitrogen oxide emissions was 3.75%. In conclusion, adding NPs to engine fuel is a viable solution for reducing emissions.

Numerical parametric study on the uniaxial and biaxial compressive behavior of H-shaped steel reinforced concrete composite beam-columns

As a follow-up work of previous experimental efforts, this paper aims to further explore the compressive behavior of Steel Reinforced Concrete (SRC) composite beam-columns with assistance of nonlinear Finite Element Analysis. The FE model was developed in ABAQUS software and it delivered desirable accuracy after calibrating against the dataset collected from published literatures. The validated FE model was then employed to evaluate the fundamental performance of SRC beam-columns under uniaxial eccentric compression and biaxial eccentric compression. On top of that, the fiber section analysis was adopted to build the three-dimensional axial force-bending moment (N-M) interaction diagrams, followed by a parametric study to investigate the effect of material grade, steel ratio, cross-section geometry, load eccentricity and load inclination on the load-carrying capacity of SRC beam-columns, and disclose the complex biaxial interaction mechanism that is not well-considered in current design codes. Based on the numerical data generated from parametric study, the Support Vector Machine method is utilized to intelligently estimate the biaxial moment contours under varying load levels, which exhibits improved accuracy and consistency than the code-specified method and conventional empirical formulae.

Multicell Power Supplies for Improved Energy Efficiency in the Information and Communications Technology Infrastructures

The rapid growth of the Information and Communications Technology (ICT) sector requires additional infrastructure, such as more micro-datacenters and telecom stations, to support the higher internet speeds and low latency requirements of 5G networks. The increased power requirements of the new ICT technologies necessitate the proposal of new power supplies, in an attempt to support the increase in energy demand and running costs. This work provides an in-depth theoretical analysis on the losses of the individual stages of commercially available PSU and proposes a new multicell PSU, the buck PFC converter, which offers a higher overall efficiency at varying load levels. The theoretical results are verified using simulation results, via a PSIM Thermal Module, and using experimental data. The results indicate that multicell structures can improve the overall PSU efficiency by 1.2% at 50% rated power and more than 2.1% at full power. Finally, taking into consideration the economic implications of this study, it is shown that the proposed multicell structure may increase the PSU costs by 10.78%, but the payback period is in the order of just 3.3 years.

Experimental and numerical characterization of fatigue crack propagation in high-speed railway wheel ER8 steel

In this work, characteristics of fatigue crack growth in the Paris regime of the rail wheel steel grade ER8 were investigated. Experimental crack growth tests under different cyclic loads of a compact tension (CT) specimen and corresponding FE simulations were carried out. The extended FE method (XFEM) coupled with damage evolution based on the strain energy release rate or the Paris law was applied to describe the crack propagation mechanism in the tested CT samples. Hereby, the fatigue fracture model parameters were firstly determined from the experimental data. It was found that the predicted crack growth rates agreed well with the experimentally gathered results. Subsequently, fatigue crack propagations of the investigated wheel steel under varying load levels, load ratios and lengths of pre-crack were studied using the FE approach and the obtained crack growth characteristics were compared and discussed.

Structural behaviour of CFRP reinforced concrete members under monotonic and cyclic long-term loading

A large percentage of the damages to reinforced concrete structures is caused by corrosion of the reinforcement steel, which often leads to expensive repairs or new construction of existing structures. Due to their high strength and resistance to corrosion, reinforcements made of carbon fibre-reinforced polymers (CFRP) are becoming more and more important in structural engineering. It is expected, that the service life of CFRP reinforced concrete (RC) members can be significantly increased as the strength-reduction due to corrosion is negligible compared to conventional RC members. Therefore, precise knowledge of the long-term behaviour of CFRP RC members is required in order to ensure safe and economic design. This paper presents experimental investigations on the long-term behaviour of CFRP RC members as well as steel-reinforced RC members under monotonic and cyclic long-term loading with varying load levels, different cross-sectional shapes and shear slendernesses. Accompanying experiments on the concrete creep behaviour that were conducted within the investigations are shown. Within the scope of the experiments, the deflections as well as the strains on the top and bottom side of the RC members were measured using displacement sensors and strain gauges. The experimental data is evaluated, especially with regard to the time-dependent deflections. The data is compared to existing mechanical and empirical models, which are usually derived for steel-reinforced RC members. Based on the experimental data, the time-dependent reduction of stiffness and conclusions for the calculation of deflections are shown.

A comparative evaluation of strain measurement techniques in reinforced concrete structures–A discussion of assembly, application, and accuracy

AbstractThe measurement of strain in structural elements is a necessary means of investigating the condition of a structure, both in research and in practice. The measurement methods for recording strain considered in this work represent both well‐established techniques (strain gauges), as well as techniques that are part of rather current research streams (fiber optic sensors, digital image correlation). This work's contribution lies in providing an overarching comparison of these approaches, thereby informing practitioners and researchers as to parameters concerning their assembly, application, and their accuracy. To such ends, two test series were carried out, one on RC tension rods and another on a RC beam in a four‐point bending test. From the latter scenario, for example, certain generalizations were to be deduced for varying load levels: low strains are measured well using the fiber optic technique. Conversely, digital image correlation was discovered to be an adequate choice when assessing higher strain levels and concomitant concrete cracking, as this non‐contact technique avoids imprecisions caused by adhesives. Findings are to assist the future user by contrasting the three techniques in terms of assembly, handling, application and resilience of sensors, external influences as well as measurement resolution and accuracy. Such practice‐oriented remarks should simplify a selection of the suitable measurement techniques catering to the respective, context‐dependent testing scenario.

Cancellous Bone May Have a Greater Adaptive Strain Threshold Than Cortical Bone.

ABSTRACTStrain magnitude has a controlling influence on bone adaptive response. However, questions remain as to how and if cancellous and cortical bone tissues respond differently to varied strain magnitudes, particularly at a molecular level. The goal of this study was to characterize the time‐dependent gene expression, bone formation, and structural response of the cancellous and cortical bone of female C57Bl/6 mice to mechanical loading by applying varying load levels (low: −3.5 N; medium: −5.2 N; high: −7 N) to the skeleton using a mouse tibia loading model. The loading experiment showed that cortical bone mass at the tibial midshaft was significantly enhanced following all load levels examined and bone formation activities were particularly elevated at the medium and high loads applied. In contrast, for the proximal metaphyseal cancellous bone, only the high load led to significant increases in bone mass and bone formation indices. Similarly, expression of genes associated with inhibition of bone formation (e.g., Sost) was altered in the diaphyseal cortical bone at all load levels, but in the metaphyseal cortico‐cancellous bone only by the high load. Finite element analysis determined that the peak tensile or compressive strains that were osteogenic for the proximal cancellous bone under the high load were significantly greater than those that were osteogenic for the midshaft cortical tissues under the low load. These results suggest that the magnitude of the strain stimulus regulating structural, cellular, and molecular responses of bone to loading may be greater for the cancellous tissues than for the cortical tissues. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage

ABSTRACT In this paper, an optimised framework utilising a Differential Evolution algorithm is presented to optimally integrate multiple distributed generation sources simultaneously into the distribution grid. By considering the important power system constraints, the proposed algorithm optimises the location, sizing and power factor setting for each distributed generation source to minimise network losses and maximise distributed generation integration. Various case studies were conducted at constant or varying levels of load and generation in both the planning stage and the real-time operation stage. The results of all case studies revealed that the proposed Differential Evolution-based algorithm delivered better performance in terms of network loss reduction and maximised distributed generation compared to other existing methods. The network loss reduction of 95.71% was achieved when all three parameters of placement, sizing and power factor of distributed generation were optimised simultaneously. In addition, a practical framework with a varying optimal power factor for distributed generation was designed. The optimal power factor setting for each distributed generation source was dynamically adjusted during real-time power grid operation, resulting in further minimisation of the system loss reduction. The overall loss reduction achieved was 96.04% relative to the base case of no distributed generation connection.

Use of Polarization Curves to Determine Kinetics Parameters in K–O2 batteries

Metal–O2 batteries have shown great potential as energy storage devices and are foreseen as one of the potential successors to lithium ion batteries. Unlike either batteries or fuel cells, however, metal-O2 batteries form an insoluble product that diminishes cathode capacity. Cathode capacity is related to the electrochemically-accessible surface area and product accumulation results in a loss of that surface area, which then contributes to the total overpotential during discharge. While typically used to determine the effect of varying load levels on the performance of energy producing devices (i.e. batteries, fuel cells), polarization, or current-potential, measurements are also an indirect measure of the different energy losses that contribute to the total overpotential associated with a particular redox process. Each region of the curve is affected by a specific overpotential that is dominant within a certain potential range. Polarization curves therefore allow for the determination of certain properties that are important for understanding and improving the performance of electrochemical energy devices. As shown in Figure 1, the short steep, but curving, slope at the start of the current-potential curve is attributed to the activation overpotential, which is the energy required to overcome the potential barrier associated with the discharge reaction. The overpotential responsible for the long sloping line is attributed to the ohmic resistance, which is dominated by electrolyte resistance. Finally, the limiting current—sharp decline in the cell potential at higher current densities—is generally attributed to mass-transfer limitations. Using derived analytical expressions, essential kinetic parameters have been determined for fuel cells from the data obtained from polarization analyses.1,2 Proceeding from that previous work involving fuel cells, analytical expressions were derived that can be applied to the data obtained from polarization analyses of metal-O2 batteries. Due to the unique single-electron redox process (K+ + O2 + e− → KO2) in the cathode (oxygen) electrode, the K–O2 superoxide battery provided an ideal case for developing and evaluating those expressions.3,4 Polarization analyses were performed on K–O2 batteries and the data obtained was evaluated using the derived expressions to determine kinetic values essential to the operation and performance of the K–O2 battery.

Atomic Head Movement Analysis for Wearable Four-Dimensional Task Load Recognition.

Physical activity recognition using wearable sensors has achieved good performance in discriminating heterogeneous activities for health monitoring, but there has been less investigation of sedentary activities, e.g., desk work, which is often physically homogenous, to improve health in office environments. In this study, we explored head movement as a new sensing modality for physical and mental activity analysis. A new algorithm which segments gyroscope signals into atomic head movement events is proposed. Instead of recognizing activities in terms of predefined categories, we recognized four dimensions of task load: cognitive, perceptual, communicative, and physical, analogous to current manual workload assessment methods like NASA-TLX. We collected head movement data from 24 participants who wore a tri-axial inertial sensor at head while performing multiple tasks with varying load levels at office. An average of 70% accuracy was achieved for recognizing cognitive load levels, and more than 80% for the other three load types. The proposed event features outperformed a set of 181 features from previous physical activity recognition studies. We also demonstrated that these atomic event features are diagnostic of different load types in cross-load type classification, showing the promise of physical and mental load monitoring for health.

From Tension to Compression: Asymmetric Mechanical Behaviour of Trabecular Bone\u2019s Organic Phase

Trabecular bone is a cellular composite material comprising primarily of mineral and organic phases with their content ratio known to change with age. Therefore, the contribution of bone constituents on bone’s mechanical behaviour, in tension and compression, at varying load levels and with changing porosity (which increases with age) is of great interest, but remains unknown. We investigated the mechanical response of demineralised bone by subjecting a set of bone samples to fully reversed cyclic tension–compression loads with varying magnitudes. We show that the tension to compression response of the organic phase of trabecular bone is asymmetric; it stiffens in tension and undergoes stiffness reduction in compression. Our results indicate that demineralised trabecular bone struts experience inelastic buckling under compression which causes irreversible damage, while irreversible strains due to microcracking are less visible in tension. We also identified that the values of this asymmetric mechanical response is associated to the original bone volume ratio (BV/TV).

Characterization of Stress-Diffusion Coupling in Lithiated Germanium by Nanoindentation

There is currently a growing demand for low-cost, high-performance electrochemical energy storage solutions to consumer electronics, vehicle electrification and stationary power management. The successful development and deployment of such solutions necessitate a fundamental understanding of the mechanical properties of electrochemical materials, as well as the intricate coupling between the electro-chemo-mechanical processes in these materials. In this work, we performed a combined experimental and modelling investigation of the stress-diffusion coupling behavior of lithiated germanium (Ge) for its use in high-performance lithium-ion batteries. Thin films of Ge were fabricated using sputtering deposition and then electrochemically lithiated, after which they were subjected to nanoindentation at varying load levels to study indentation-induced creep deformation. Concurrently, a continuum chemo-mechanical model of the nanoindentation test was developed and used to investigate the fundamental mechanisms underlying the stress-gradient-driven creep deformation. The stress-diffusion coupling coefficient and diffusivity of lithium in Ge were obtained by quantitatively comparing the simulated nanoindentation response with the experimental measurements. This integrative experimental and computation work provides important insights into the chemo-mechanical coupling process in high-performance rechargeable battery electrodes.

Data-driven bearing fault identification using improved hidden Markov model and self-organizing map

Abstract Efficient condition monitoring and fault diagnosis of bearings are of great practical significance since bearings are key elements in most rotating manufacturing machineries. In this study, a condition monitoring index of bearings is developed based on self-organizing map (SOM) in order to detect incipient bearing faults quickly. It requires low computation cost and is robust to the change of load level and motor speed, hence is quite suitable for online condition monitoring of bearings. Furthermore, a novel hybrid algorithm combining diversified gradient descent (DGD) method and Bayesian model selection (BMS) called DGD-BMS for the optimization of discrete hidden Markov model (DHMM) parameters is formulated under a general Bayesian framework. The flexibility of the DGD-BMS consists in that the algorithm can increase the diversity of the searching paths generated for DHMM parameters so that the true underlying parameters are more likely to be found out. Thus it provides an effective way to avoid trapping in one local maximum. Both simulation and industrial case study are presented to validate the proposed approach. Results show that the monitoring index can detect incipient bearing faults efficiently with 100% accuracy even under varying load levels, and the DGD-BMS method achieves on average the classification rate of 99.58%. The proposed method exhibits excellent performance compared to the conventional gradient descent (GD) and Baum-Welch (BW) methods.

Mesoscopic simulations of concrete strains incompatibilities under high creep stress level and consequences on the mechanical properties

The available models for estimating the creep strains of concrete generally assume that concrete is a homogeneous material. Since concrete is a composite, such models are incapable of taking into account incompatible strains between the cement paste and aggregates during creep loading. The main objective of this paper is to demonstrate that microcracks could increase the creep strain level and justify the use of a coupling between creep and damage at the macroscopic scale. To achieve this objective, a viscoelastic model has been adopted to compute the creep strains of a mesostructure composed of aggregates and cement paste under varying load levels, in both tension and compression. Creep tests drawn from the literature are used to show that the microcracks generated by incompatible strains significantly increase the creep strains under high stress/strength ratios. It is then proven that the mesoscopic approach is capable of predicting concrete failure during a ring test involving basic creep. Residual Concrete behavior is also predicted and modifications observed especially following creep in compression which is not consistent with the few experimental data available meaning that apparent non-linearity cannot be completely attributed to strains incompatibilities at the mesoscale.

Efficient Cellular Load Balancing Through Mobility-Enriched Vehicular Communications

Supporting effective load balancing is paramount for increasing network utilization efficiency and improving the perceivable user experience in emerging and future cellular networks. At the same time, it is becoming increasingly alarming that current communication practices lead to excessive energy wastes both at the infrastructure side and at the terminals. To address both these issues, this paper discusses an innovative communication approach enabled by the implementation of device-to-device (d2d) communication over cellular networks. The technique capitalizes on the delay tolerance of a significant portion of Internet applications and the inherent mobility of the nodes to achieve significant performance gains. For delay-tolerant messages, a mobile node can postpone message transmission—in a store–carry and forward manner—for a later time to allow the terminal to achieve communication over a shorter range or to postpone communication to when the terminal enters a cooler cell, before engaging in communication. Based on this framework, a theoretical model is introduced to study the generalized multihop d2d forwarding scheme where mobile nodes are allowed to buffer messages and carry them while in transit. Thus, a multiobjective optimization problem is introduced where both the communication cost and the varying load levels of multiple cells are to be minimized. We show that the mathematical programming model that arises can be efficiently solved in time. Furthermore, extensive numerical investigations reveal that the proposed scheme is an effective approach for both energy-efficient communication and offering significant gains in terms of load balancing in multicell topologies.