Estimation Of Capacity Research Articles

Orbital Tolerance and Intrinsic Orbital Capacity for Electric Propulsion Constellations

Large constellations are typically designed using sets of periodic orbits and satellite control boxes sized to ensure compatible phasing between coorbital constellation planes to prevent conjunction events. This work investigates how the control action for orbit maintenance influences feasible intrashell and intershell distances in a satellite constellation. Two-dimensional lattice flower constellations are simulated in an environment inclusive of orbital perturbations, with onboard sensors and an electric propulsion system constituting the satellite guidance, navigation, and control system. An analysis to define estimates for minimum separation distances is performed as a function of control, propulsion, orbit, environmental, and spacecraft characteristics. An estimate of the intrinsic (geometric) orbital capacity is proposed, based on current and advanced technologies, in order to quantify the number of admissible spacecraft and orbital shells in a selected altitude range. Simulation results are used to improve the fidelity of intrinsic orbital capacity estimates and to understand factors that influence the number of admissible satellite locations in low Earth orbit.

Using Machine Learning to Predict Axial Pile Capacity

Accurate estimation of the ultimate axial load bearing capacity of piles is necessary to ensure the safety of the supported structures and to prevent cost overruns. Traditional mechanics-based design methods do not always predict pile capacity accurately, or precisely, leaving room for improvement. This study focuses on the potential of machine learning (ML) in estimating pile capacity. A dataset of 546 load tests was compiled from three databases. The baseline performance of traditional design methods was first established by comparing the capacities computed using four traditional approaches, against the capacities interpreted from load tests using Davisson’s criterion. Sixteen different ML techniques were explored. First, the optimal feature selection technique for model training was investigated. Second, hyperparameters of each technique were optimized. The process involved the training of 32,000 different models and tuning their hyperparameters. Next the dataset was randomly split into training (70%) and testing (30%) for comparing the 16 different ML regression models. Each of the optimized models was then trained using six feature sets. The performance of each of the 16 ML models with the best performing feature subset was compared with the baseline performance from traditional methods. Evaluation criteria included measured versus predicted capacities, influence of soil type on accuracy, as well as the absence of pile diameter, or length effects on accuracy and precision. In general, the ML methods performed significantly better than the best traditional method. The current research demonstrated that ML may offer advantages in geotechnical design when large datasets are available.

Influence of pedestrian movement on speed of vehicles, capacity and level of service of urban midblock sections

Side friction factors are all those actions related to the activities taking place by the sides of the road and sometimes within the road, which interfere with the traffic flow on the travelled way. They include but not limited to pedestrians, bicycles, non-motorized vehicles, parked and stopping vehicles. These factors are normally very frequent in densely populated areas in developing countries, while they are random and sparse in developed countries making it of less interest for research and consequently there is comparatively little literature about them. The objective of this thesis is to analyze the effect of these factors on capacity of urban roads. The present study is mainly focused on individual effect of the friction factors like pedestrians crossing the road on capacity of urban roads. Data on two sections of 4-lane divided road were collected in Hyderabad with pedestrian cross flow. These are taken as the base sections. Three basic parameters of traffic speed, volume and density are used for estimation of traffic carrying capacity of a road. For determination of speed-volume relationship in heterogeneous traffic condition, the volume calculated by total vehicles recorded for each counting period were converted into equivalent number of PCUs. Mean stream speed or weighted space mean speed is calculated and used in the present study. The present study demonstrates that mix traffic stream can be converted in to an homogeneous equivalent of passenger cars by multiplying the total traffic volume (in veh/hr) by a stream equivalency factor (K). It will avoid the problem of estimation of PCU values for individual vehicle categories in the traffic stream. The stream equivalency factor (K) will depend on traffic composition and a regression equation is developed in this study to estimate K-value on urban roads.

Evolution of shale wetting properties under long-term CO2/brine/shale interaction: Implications for CO2 storage in shale reservoirs

In the case of Carbon Geological Storage (CGS), the wettability is one of the main parameters controlling the CO2 injection capacity and CO2 plume movement, which affects the safety of long-term CO2 storage. A full evaluation of this parameter is essential for risk evaluation and storage capacity estimation. The objective of this study was to investigate the wettability changes of shales from the Chang 7 Formation in the Ordos Basin by exposing the samples to ScCO2/brine for 10/20/30/and 60 days. The mineral composition, pore structure, chemical functional groups, and surface morphology changes of the shale were analyzed using x-ray diffractometer (XRD), low-temperature nitrogen adsorption (LN-N2), infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). In addition, the solid-drop method was used to measure the contact angle of shale samples at different times of ScCO2 soaking. The results show that after 60 days of continuous ScCO2 soaking, the shale water contact angle and chemical group peak spectrum generally show an increasing trend, which is caused by the reduction of carbonate and clay mineral content. The prolonged time of ScCO2 soaking weakens the shale's water wettability and reduces the water-locking effect (water has a sealing effect in the pore channel of the reservoir), capillary force, and the height of CO2 sequestration columns, which is favorable for shale gas extraction (a method of extracting natural gas from shale rock by CO2 fracturing), but it may increase the CO2 geological leakage risk. The results have important implications for regulating the change of shale water wettability, and this study can also better provide theoretical references for Carbon Geological Storage programs.

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

Experimental research on mechanical behavior of UHPCFST under repeated axial compression

This paper investigates the mechanical behavior of ultra-high performance concrete-filled steel tubes (UHPCFST) subjected to repeated axial compression. A total of 34 specimens of UHPCFST were systematically designed, constructed, and evaluated experimentally. The design parameters encompassed steel tube wall thickness, UHPC type, specimen size (varying diameters while preserving a consistent diameter-to-thickness ratio), and loading scheme. The failure patterns, stress-strain relationships, axial load-bearing capacity, and stiffness were meticulously examined. Predominantly, shear failure and drum-shaped upsetting failure were identified as the primary failure mechanisms in the specimens. The axial load-bearing capacity was found to increase notably with the use of thicker steel tubes and higher-grade UHPC. Under repeated loading, a reduction in stiffness was noted, which was dependent on factors such as the steel content, tube diameter, and the volume of coarse aggregate of UHPC. Current predictive equations for the axial load-bearing capacity of CFST were assessed using the experimental results of UHPCFST and were determined to over-predict the axial load-bearing capacity of UHPCFST. Consequently, a refined equation is proposed to yield a more precise estimation of the axial load-bearing capacity for UHPCFST. Furthermore, an empirical model was developed to characterize the stress-strain behavior of UHPCFST under repeated axial compression, offering a tool for practical engineering design and analysis.

Seismic Bearing Capacity of a Footing on Sloping Frictional Soil

This study presents the analysis of surficial strip footing on sloping frictional ground for an easy estimation of its seismic bearing capacity (by means of a seismic slope coefficient) to be used in conjunction with any other analytical solution provided for the static case. The influence of the slope angle and height, proximity of the slope to the crest, and value of the pseudo-static seismic coefficient is presented. A parametric study performed for the range of these geometrical and geotechnical parameters is conducted by the finite element method, also presenting the analysis of failure modes. The results are summarized in the form of non-dimensional charts that could be easily used for the estimation of the seismic bearing capacity by practitioners. An example of the application of charts is presented. It can be concluded that there is no influence of the sloping ground when the distance of the footing is greater than four times the width. For the horizontal seismic coefficients greater than 0.3 g, the shallow foundations should not be placed near the slope.

Enhancing pile bearing capacity estimation through random forest-based hybridization approach

Enhancing pile bearing capacity estimation through random forest-based hybridization approach

Anthropogenic pressure limits the recovery of a postwar leopard population in central Mozambique

AbstractThe population size and conservation status of wildlife in post‐conflict areas is often uncertain. In Mozambique, decades of armed conflict resulted in large‐scale wildlife population depletion with limited conservation and research opportunities. The African leopard (Panthera pardus) is a large carnivore with great ecological and economic significance, yet their population status is largely unknown within Mozambique. Using camera trapping in conjunction with robust spatially explicit capture‐recapture modeling, we estimated leopard density in 2021 for Coutada 11, a wildlife management area in the postwar Zambezi Delta landscape of central Mozambique. Leopard density was relatively low (1.57 ± 0.37 SE [latent‐mixture‐model] and 1.84 ± 0.41 [sex‐mixture‐model] leopards/100 km2), occurring in the bottom fourth of 161 range‐wide leopard densities, and similar to those from semiarid and human‐dominated landscapes. Prey‐based carrying capacity estimates suggested that leopard density should be at least twice as large. Despite a recent and substantial reduction in poaching activity, evidence of snared leopards indicates that sustained bushmeat poaching, combined with sustainable, but additional legal offtake is suppressing leopard population recovery. This study provides important baseline insight into leopard population density in Mozambique and joins mounting evidence indicating that anthropogenic pressures limit large carnivore populations which is of major national and global concern. We suggest long‐term monitoring of this leopard population to determine trends over time and implement effective conservation interventions in response to population changes. This population clearly has the capacity to recover if hunting quotas are reduced to account for illegal offtake and, more importantly, if anti‐poaching efforts are redoubled to reduce unsustainable anthropogenic mortality of leopards.

Prospects for Geological Storage of CO2 in Carbonate Formations of the Adriatic Offshore

Croatia has both significant CO2 emissions from the point sources and a history of oil and gas exploration, and this is why the CCS technology surfaced as a viable solution for curbing CO2 emissions on a national level. Since approximately half of emissions from the stationary industrial sources occur along the Adriatic coastline, the entire offshore area became an exploration target. Regional studies revealed the potential storage plays, one of which is in the aquifer of the Mesozoic carbonate complex with dual porosity extending all along the Croatian offshore area. Three structures were chosen in its central part–Klara, Kate and Perina. For the first two, the models were constructed based on the data from old exploration wells and a regional structural map, while for the Perina structure, a new seismic interpretation was added to better characterise its properties. It came out that the Kate structure appears to be the most prospective in general (45 Mt), with neighbouring Klara as the second (39 Mt), while the initially promising Perina (7 Mt) turned out to be of far lesser importance. The Perina structure case is an example that new seismic interpretation can reduce the capacity estimate if it reveals certain limiting factors, in this case, the limitation of structural closure.

Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system

The recycling of lithium-ion batteries (LIBs) from electric vehicles (EVs) for augmenting the capacity of battery energy storage systems (BESS) presents a sustainable approach to leverage investment in LIBs, mitigating economic losses. This study highlights the critical role of accurate capacity estimation and remaining-useful-life (RUL) prediction in determining optimal replacement and augmentation schedules. We introduce a semi-empirical (SE) model that integrates experimental aging data with an adaptive method and an electrochemical model to capture the degradation trends of LIBs accurately. The adoption of the Moore-Penrose pseudoinverse (Pinv) method, suited for the SE model's multi-parameter, single-output nature, significantly improves capacity estimation accuracy, achieving a maximum error below 0.2 Ah. This improved SE (ISE) model accounts for capacity trends and addresses non-stationary phenomena, including noise and signal distortion during parameter identification and capacity recovery phases.To address these challenges, we propose using discrete wavelet transform (DWT) for the decomposition of non-stationary signals, adjusting the window size to signal characteristics for enhanced noise reduction. The application of DWT to denoise α and β parameters, characterized by their non-constant periodicity and noise interference, facilitates the development of DWT combined with the ISE (DWT-ISE) aging model for early RUL prediction. Experimental validation reveals that our DWT-based approach minimizes root-mean square error (RMSE) and mean absolute error (MAE) for early RUL prediction, demonstrating the model's superiority at 300 cycles for identification and achieving the lowest prediction error at 400 cycles. Furthermore, comparison results of other models (Particle filter, and long short-term memory) reveal that our DWT-ISE aging model exhibits the best RMSE and MAE on limited data. Specifically, the RMSE and MAE values for RUL prediction, underscored as 0.032 % and 0.027 % respectively at 300 cycles, are confirmed as the highest evaluation metrics.

A charge-discharge optimization strategy considering the spatiotemporal distribution of electric vehicles and the operational safety of the power distribution network in the power-transportation coupling network

An electric vehicle (EV) charge-discharge optimization (CDO) strategy that accommodates both grid-side and user-side demands is conducive to mitigating the adverse effects of disordered EV charging on the power distribution network (PDN). To tackle the issue of inaccurate estimation of the schedulable capacity of EVs in existing research, a high temporal resolution dynamic spatiotemporal distribution simulation model for EVs is developed. Furthermore, leveraging the characteristics of the power-transportation coupling network (PTCN), a sub-districted dynamic electricity pricing (DEP) is proposed to assist in improving the PDN node voltage distribution. Subsequently, an incentive coefficient is introduced to incentivize the discharge of EVs during potential peak periods. The proposed CDO strategy, considering the non-cooperative behavior of EV owners (EVOs), can provide personalized charge-discharge plans for them. The temporal sequence experiments reveal a significant decrease in the peak-to-valley disparity of the load, achieving an optimization rate of load mean square error that exceeds 80%. The sub-districted DEP exhibits a significant advantage in maintaining PDN voltage stability compared to traditional time-of-use electricity pricing (TOUEP) and DEP, while also achieving reductions in active power losses of more than 15% and 10% for PDN, respectively. Moreover, in comparison to the conventional scenario, the CDO strategy leads to a maximum reduction of up to 942.5% and 22.3% in the total economic cost for electric private cars and electric taxis respectively. Lastly, the impact of seasonal factors is discussed. Numerical results indicate effective alleviation of load peak-valley differences, load fluctuations, and voltage drop phenomena in both winter and summer. Additionally, the maximum reduction in active power losses reaches 20.1% and 21.9%, respectively, while the total economic cost for EVOs participating in the CDO strategy is reduced by up to 97.7% and 114.7%.

A holistic approach for tourism carrying capacity estimation in sensitive ecological areas

AbstractMediterranean ecosystems are in the spotlight of tourism activities, with the local populations trying to make the most of them, while in parallel, the stress signs, such as habitat and biodiversity degradation, increased pollution, or beach erosion, have begun to emerge. Furthermore, this combination of exotic and delicate qualities accompanied by excessive tourist flows leads to the imperative need for sustainable tourism development studies in these areas. In the current study, aiming to develop a new holistic framework for assessing Carrying Capacity in sensitive coastal ecosystems, a combined methodology was created and tested in Balos Lagoon, a Natura 2000 area in Western Crete. The method encompasses calculating different Carrying Capacity indicators, environmental quality measurements, visitors’ perceptions identification, and finally, a multicriteria analysis to capture the stakeholders’ and local community’s viewpoints. The combined methodologies identified vital issues, including overcrowding—Effective Carrying Capacity is exceeded by 1000 people per day during the peak season—tar residue pollution, microplastics, insufficient road infrastructure and excessive car numbers exceeding capacity. Stakeholder involvement was pivotal, prioritizing twelve proposed actions to address those issues. Notably, “frequent beach clean-ups,” targeting visual impacts, emerged as the most critical action, while parking reallocation and setting a maximum daily ferry limit were also highly ranked, promising solutions to alleviate overcrowding issues. The paper offers valuable insights for future research, emphasizing the need for continuous environmental monitoring, implementation of high-priority measures, and economic valuation of natural capital. Ultimately, this research contributes to the literature by presenting a pioneering methodology for holistic assessment and sustainable tourism development in Mediterranean sensitive coastal ecosystems.

Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements

Abstract Computing the axial load capacity of pile foundations is often challenging for geotechnical engineers. Different theoretical and empirical approaches were proposed for determining the ultimate bearing capacity. Among these approaches, the in situ testing methods are based on cone penetration test (CPT) measurements. This study compares five CPT methods in estimating the axial load capacity of precast concrete piles evaluated at four piling construction projects in Northwest and Central Florida. The analytical methods used in this work are the Aoki and De Alencar method (1975), the Schmertmann method (1978), the Bustamante and Gianeselli Laboratoire Central des Ponts et Chausees method (1982), the Eslami and Fellenius method (1997), and the Eurocode 7 (2007). The outcomes indicated that the Eurocode method 7 had the most increased load capacity estimates, whereas the Eslami and Fellenius method yielded the lowest estimates. The results also indicated that the Aoki and De Alencar (1975) method consistently demonstrated the slightest differences among these values, showing its potential for reliable and consistent pile-bearing capacity estimations.

A note on the capacity estimate in metastability for generic configurations

In this paper we further develop the ideas from Geometric Function Theory initially introduced in Avelin et al. (Commun Math Phys 404:401–437, 2023), to derive capacity estimate in metastability for arbitrary configurations. The novelty of this paper is twofold. First, the graph theoretical connection enables us to exactly compute the pre-factor in the capacity. Second, we complete the method from Avelin et al. (Commun Math Phys 404:401–437, 2023) by providing an upper bound using Geometric Function Theory together with Thompson’s principle, avoiding explicit constructions of test functions.

A Lithium-Ion Battery Remaining Useful Life Prediction Method with A New Algorithm Based on Incremental Capacity Analysis

Safe use of lithium-ion batteries requires accurately assessing state of charge (SoC), state of health (SOH) and capacity estimation techniques. Due to numerous charge and discharge cycles, lithium ion batteries undergo a degradation process during their use leading to failures, accidents, and fire. Traditional ICA/DVA methods have been used to overcome these issues, but they are subject to changes in battery resistance and polarization processes during battery aging. Evaluation of the SoC as a function of incremental capacity is proposed in this work to overcome this problem. This article used a new algorithm to perform, through simulations carried out with Matlab® software, incremental capacity analysis for a preventive estimate of remaining useful life (RUL). In addition, the comparison between IC curves and the SoC here used fully represents the relationship between the IC values and the internal parameters of the battery. The validity of the proposed algorithm against the phenomenon of battery aging was evaluated based on experimental data from NASA's PCoE research center.

Fusion model based RUL prediction method of lithium-ion battery under working conditions

Under working conditions, since the remaining useful life (RUL) prediction of lithium-ion battery is subject to uncertainties of random charging and discharging, and infeasibility of battery capacity test, a fusion model based RUL prediction method was proposed. First, the feature learning method of lithium-ion batteries was developed by synthesizing manual extraction and one-dimensional convolutional neural network (1DCNN) extraction. Then, a fused method was proposed to estimate the historical available capacity through exploring the spatial and temporal relationship of features, and the long short-term memory (LSTM) network model was adopted for predicting the RUL of lithium-ion battery. The proposed method was verified through the comparison of different methods, and the results show that it can realize highly precise and stable capacity estimation and RUL prediction under working conditions.

Lower boundary capacity estimations of frequency-selective communication channels with PAM-n-signals achievable using resolution time theory

The issue of ensuring high specific capacity of promising wire telecommunication systems is one of the key problems of the last decade. At the same time, despite the existing spectrally efficient modulation methods that are used in existing communication systems, the majority of leading researchers associate further solution of this issue with the transition to the mode of information transmission at a rate "Faster-than-Nyquist" rate (FTN), in which the information receiving about the channel symbol occurs with pronounced intersymbol interference. In turn, this causes difficulties in the practical implementation of the receiver when using optimal signal processing algorithms with nonpolynomial computational complexity, and in the case of suboptimal demodulation methods implementing various types of channel equalization, due to conceptual limitations cannot provide a wide coverage of speeds in this mode. In this regard, the study of an alternative approach to processing signals with multi-position PAM-n-signals on the output of baseband frequency-selective channels (FSC) is relevant. The object of this paper is the study of the possibilities offered by the theory of resolution time for baseband FSC of the, in which information is transmitted using the PAM-n-signal, in the FTN mode. A modified mathematical model of FSC is presented, thanks to which analytical expressions are obtained for estimating the specific capacity in terms of the low bound capacity estimation introduced by Sh. Shamai, expressions for estimating the BER, including the case under the action of destabilizing factors on the symbol synchronization system. Auxiliary capacity estimations and rules for calculating the conditions for achieving them are introduced, convenient for engineering analysis. By means of numerical simulating for the most typical conditions inherent in wired FSC, which are modeled in this work by Chebyshev filters with ripple level of 3 dB and Butterworth filters, it is shown that in the FSN mode with a S/N ratio of no more than 40 dB and instability of the symbol synchronization system operation of 2.5% of the channel symbol duration with a bit error probability of 10–12, it is possible to expect a specific capacity of 5.7–8 bits/Hz*s. It is shown that these results are achieved by using hybrid forms of partial pulses. Recommendations for choosing a partial pulse shape are presented, their main properties are studied from the point of view of their engineering application.

Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression

Reliable capacity estimation is crucial for safe operation of lithium-ion batteries (LIBs). This work combines the temporal convolutional network (TCN) and Gaussian process regression (GPR) to establish a novel probabilistic capacity estimation method. The proposed TCN-GPR method can not only provide accurate capacity estimation but also quantify the uncertainty of the estimation. Besides, the TCN-GPR method can automatically extract degradation features from partial charging segments, overcoming the limitations of manual experience. In addition, the TCN-GPR method can be applied to different types of LIBs through transfer learning using only a small amount of training data. For validation, the Oxford battery dataset is used to demonstrate the accuracy and robustness of the TCN-GPR method, where a mean absolute percentage error (MAPE) of less than 0.3% can be achieved with only a 15-min partial charging segment. Furthermore, our own experimental dataset is used to demonstrate the generalization ability of the TCN-GPR method through transfer learning, where a MAPE of less than 0.7% can be achieved by using only one battery cell as the training sample.

A comparative study on the estimation of ultimate bearing capacity of rock masses using finite element and limit equilibrium methods

Most rock masses are excellent foundation materials due to their bearing capacities of MPa. However, the ultimate bearing capacity of rock masses should be accurately estimated in the design of structures with high foundation loads. In this study, the ultimate bearing capacities of a strip footing built on rock masses with different geotechnical properties are determined using the finite element method (FEM) and the failure criterion of Hoek & Brown. The results of FE-analyses are compared to those obtained from the limit equilibrium methods (LEM) in the literature. It has been shown that the FEM with associated flow rule and Terzaghi`s limit equilibrium method give similar failure surfaces for most cases, and the ratio of ultimate bearing capacities determined according to the Terzaghi´s method to FEM varies between 1.5 and 4. In cases, in which the failure surfaces obtained from both methods differ, this ratio can rise up to 11.aring capacities determined according to the Terzaghi´s method to FEM varies between 1.5 and 4. In cases, in which the failure surfaces obtained from both methods differ, this ratio can rise up to 11.