Efficiency Curves Research Articles

Vibration and Flow Characteristics of a 200 MW Kaplan Turbine Unit under Off-Cam Conditions

Kaplan turbine units can adjust their blades to achieve wider outputs without a significant loss of efficiency. The combination of guide vane angle (GVA) and blade angle (BA) is selected based on efficiency curves obtained from cam tests. However, the vibration characteristics are not considered in the test. The vibration and flow characteristics are complex with different combinations of guide vane and blade angles. Different cam relation selection principles lead to varying machine vibration and flow characteristics. In this research, the flow and vibration characteristics were obtained by means of field test and numerical simulation. Vibration, pressure pulsation, and other stability indicators have been extracted and investigated under off-cam conditions. The flow and variation rules of different indicators have been thoroughly researched. The findings suggest that the magnitude of vibration in the X direction surpassed that in the Y direction for the head cover, upper frame, and lower frame under 22 experimental conditions. The disparity between the head cover and upper frame in both directions was not significant, whereas a substantial contrast existed between the lower frame in the X and Y directions. The calculation results indicate that when the guide vane angle was small, vortices appeared near the high-pressure edge of the runner in the vaneless region and caused disorganized flow lines in the runner, and this complex vortex behavior led to multiple frequency components in the pressure pulsation frequency domain. The conclusions provide references for the designers of Kaplan turbine units and improves the operating safety of Kaplan turbine power stations.

Semi-analytical solutions for land subsidence due to groundwater withdrawal

This paper introduces novel semi-analytical models tailored for estimating land subsidence resulting from groundwater extraction in confined aquifers. These models offer high scalability, allowing them to be applied to various well configurations and pumping schedules. Their development involves the numerical integration of two key analytical solutions: the “nucleus of strain” (NoS) (Mindlin and Chen, 1950), which represents a localised zone within the aquifer where a unit change in pore pressure leads to deformation and subsequent surface displacement, and the classic Theis equation (Theis, 1935) for the pore pressure changes induced by a constant-rate well pumping from a laterally unbounded aquifer. These integrations yield surface displacement components, both horizontal and vertical, expressed as functions of two dimensionless spatial–temporal variables, which encompass aquifer depth, thickness, well placement, pumping schedules, and critical hydro-geomechanical parameters like hydraulic conductivity, porosity, vertical compressibility, and water compressibility. Proposed are two distinct modelling approaches: one employing a lookup table (LT) derived from numerical integration results, and the other providing direct closed-form surface displacement solutions by fitting LT data with “hinge models”, which use piecewise-linear functions linked by sigmoidal curves for computational efficiency. In both cases, surface displacement components are estimated by plugging in the dimensionless variables. Conditions of variable pumping from multiple wells can be addressed by applying superposition of solutions. In essence, these semi-analytical models offer swift computational capabilities for understanding and forecasting land subsidence dynamics. Their scalability makes them adaptable to a wide array of well configurations and scheduling scenarios, rendering them valuable for numerous applications. They are particularly significant for providing preliminary estimates of the impacts of groundwater development, conducting “what-if” tests, and performing sensitivity analyses to identify key factors affecting land subsidence risk. This underscores the importance of these models in sustainable groundwater resource management and in mitigating land subsidence and its associated consequences.

Novel Efficiency Calculation Model Based on Fine Particle Tracking Behavior

The underflow entrainment of fine particles occurs during the hydrocyclone separation process, resulting in a “fishhook” effect on grade efficiency. Traditional efficiency models fail to address this phenomenon. This study examines the tracking behavior of fine particles, using variations in centrifugal settling velocity to characterize separation performance. The effect of this behavior on particle separation is quantified through a tracking coefficient for small particles and an entrainment coefficient for large particles, together forming a novel efficiency calculation model. The experimental research shows that the new model is applicable for the efficiency calculation of particles with different shapes, and can calculate grade efficiency curves with fishhook segments. By comparing with the existing research results, the accuracy and universality of the new model have been demonstrated. This model facilitates the accurate computation of grade efficiency curves, thereby significantly enhancing the precision of efficiency calculations, which provides guidance for the design and selection of hydrocyclones.

Microcontroller-based integrated data logging system for solar air heater performance assessment

ABSTRACT Efficiency curves for assessing solar air heater (SAH) performance are determined by measuring different parameters under certain operating conditions recommended by different test standards such as American Society of Heating, Refrigerating and Air-Conditioning Engineers. In this work, development and testing of a low cost integrated datalogging system for tracing efficiency curves are presented. Pyranometer (CMP11) and temperatures sensors (DS18B20) are integrated with microcontroller (AT89S52) for continuous data recording at desired time interval. Switching devices are integrated to automatically control operations of electric heater for maintaining inlet air stream temperature as per testing requirements. Using the system, efficiency curves of a 1.46 m2 SAH installed in Tezpur University, India, are determined at two mass flow rates of air. Results are comparable with published literatures revealing negative slope (energy loss component, F R U L ) and positive intercept (energy absorption component, F R (τα) n ) of efficiency curves. SAH shows better performance at lower mass flow rate of air (0.0235 kg/m2-s), with F R U L as 13.5 W/m2-K compared to operation at 0.0430 kg/m2-s mass flow rate of air where slope increases by around 23% with same intercept. Higher slopes of the SAH demonstrate its highest sensitivity to prevailing wind compared to three previously published designs. Present study shows that the integrated system can replace individual traditional measuring instruments for respective parameters like solar irradiance and temperatures as well as additional control equipment required to maintain desired test conditions. Further development of the system can lead to a modular and portable efficiency curve tracer for SAH performance assessment.

Numerical and experimental evaluation of the performance of agravitational vortex turbine rotor

In this study, the evaluation of the rotor performance of agravitational vortex turbine (TVG) for its application indistributed power generation is presented. A numerical analysiswas carried out on a specific configuration of a TVG,characterized by its spiral inlet and conical discharge. Thedimensions of the discharge chamber and the inlet channel, suchas the inlet channel width (w), length (L), and height (h), thedischarge cone height (H) and diameter (d), and the envelopeangle (γ) were determined through previous optimization studiesdocumented in the literature. These dimensions are related to thebasin diameter (D), which was set at 500 mm for this study. Theratios used, such as L/D, h/D, H/D, γ w/D and d/D were 1.518,0.565, 1.572, 92.41°, 0.362, and 0.108, respectively.The rotor has a curvature and a helical pitch angle of 68.8°, has 6blades, a rotor height of 200 mm and the middle section of therotor is 314.4 mm (0.4H) from the top of the discharge cone, withan lower and upper diameter of151.60 mm and 284.44 mm. Usinga three-dimensional computational domain and unsteady flowsimulations, efficiency curves as a function of angular velocitywere obtained. In addition, the TVG was manufactured andexperimental tests were carried out on a laboratory-scale testbench to validate its performance. These tests allowed us tocompare the experimental results (33.84% at 88 RPM) with thenumerical results (32.67% at 106,667 RPM), revealing adifference of 3.37% between the maximum efficiency values. Itis essential to continue the turbine optimization process to ensurethat the designed TVG plays a role in fostering sustainability andfacilitating the shift towards cleaner and sustainable energyalternatives.

Regional coordinated development of low-carbon sports industry in China

This study develops a systematic modeling framework, comprising a prediction model, a super-SBM model, and a spatial autocorrelation analysis model, to explore the spatial–temporal evolution tendencies of development efficiency within China's 30 regions in the low-carbon sports industry from 2006 to 2025. This framework aims to provide theoretical insights for the formulation of more targeted policies. Based on the empirical findings, the main conclusions of this study are as follows: (1) The optimal buffer operator grey prediction model demonstrates the highest accuracy among the prediction models examined. (2) The development efficiency curves of the 30 regions exhibit a significant increasing trend from 2006 to 2021, with values generally peaking between 0.4 and 0.6. (3) Notably, the disparity in development efficiency between developed and less developed regions is expected to progressively widen. (4) The development efficiency of the low-carbon sports industry across the 30 regions typically displays high-high clustering and low-low clustering during China's four five-year plan periods. This underscores the importance and urgency of promoting regional coordinated development within the low-carbon sports industry.

Profile of Round-Trip Efficiency of Lithium-Ion Battery Energy Storage System

Lithium-ion battery energy storage systems (Li-BESSs) are expected to adjust variable renewable energies such as photovoltaic energy. In the viewpoint of the energy management cost, profiles of round-trip efficiency (RTE) are important characteristics of Li-BESSs. The profiles of RTE are related to the characteristics of both LIBs and inverters installed on Li-BESSs. The characteristics of Li-BESSs would be various because of reusing and degradation of lithium-ion batteries (LIBs). And inverters made by a lot of manufacturers would be so on. Accordingly, it could be said that the understanding of the profile of each Li-BESS’s RTE is complex and difficult. Therefore, we studied the time series analysis of operation data and tried to estimate the characteristics of both LIBs and inverters. The important ones of LIB are full-charge capacity (FCC), open-circuit voltage (OCV), and internal resistance (R). In addition, characteristics of OCV and R can be expressed as function profiles with state of charge (SOC) as elements. Similarly, the important one of inverter is the typical efficiency curve. In this study, we proposed the estimation method of the profiles of OCV and R and inverter efficiency curve. On the other hand, FCC was given as assuming that general diagnosis results would be provided. Then, a RTE profile of Li-BESS (including 9.94kWh LIB and 10kW inverter) was tried to establish from the results of it. The profiles of OCV and R were estimated by the method called MGFFD (Modified Gaussian Free-Form Deformation) that we have been established [1]. The novelty of this study could be described as: Development of the estimation method of an efficiency curve of inverter installed on BESS during operation (Fig.1). Quantitatively visualized profile of RTE of Li-BESS by combining results of MGFFD estimation applied for LIBs and inverter (Fig.2). The profile described on Fig.2 is the relationship between SOC, input-output power rate of inverter, and RTE of Li-BESS. To understand Li-BESS’s charge-discharge efficiency of from state at that time to the end of an operation plan, it would be necessary information. This proposed method could clarify various RTE of Li-BESSs, consequently, be useful for its efficient operation by information communication technology.Reference[1] M. Arima, L. Lin, and M. Fukui, "Kalman-Filter-Based Learning of Characteristic Profiles of Lithium-Ion Batteries", Sensors, vol. 22, no. 14, 5156. Figure 1

Prediction and practical application of bauxite mineralization in Wuzhengdao area, Guizhou, China.

Wu-Zheng-Dao District in China is the world's most famous mining areas. It hosts several world-class deposits, such as Xinming, Datang and Luolong bauxite deposits. Although this area still has significant potential for the discovery of new deposits, mineral prediction has become increasingly diffcult as the number of shallow deposits diminishes. Therefore, it is necessary to explore new and effective metallogenic prediction methods.Weights of evidence and machine-learning algorithms were used for mineral prospecting in this study. This study used a confusion matrix, receiver operating characteristic (ROC) curve,and prediction efficiency curve to evaluate the prediction results of each machine algorithm. The results showed that 95.9% of the deposits were located in high and distant scenic areas, accounting for 10% of the total area.The prospectivity map of the Wu-Zheng-Dao district shows that the high prospective areas are generally confined to the claystone and carbonatite rocks of the Eastern region, in particular, of the clay layers, and several areas of high prospectivity also occur in the Southern Cross Domain. According to the predicted results, after on-site exploration, design, and construction, Yanfengqian bauxite deposit was discovered, with an average thickness of 1.82 meters; The average content of Al2O3 is 61.24%; The resource amount is 28.9503 million tons.

An Empirical Study on the Upcycling of Glass Bottles into Hydrocyclone Separators

Cyclones are pivotal in mechanical process engineering and crucial in the complex field of separation technology. Their robustness and compact spatial requirements render them universally applicable and versatile across various industrial domains. Depending on the utilized fluid and field of application, both gas-based cyclones and hydrocyclones (HCs) are well established. Regarding HC design, enduring elongated flat cones have seen minimal alterations in shape and structure since their introduction over more than a hundred years ago. Experimental investigations regarding unconventional cone designs within scientific studies remain the exception. Therefore, this study focuses on alternative geometric configurations of the separation chambers and highlights their impact on separation and energy efficiency. To achieve this objective, different geometric shapes are investigated and retrofitted into HCs. The geometric foundation is derived from upcycled glass bottles. The repurposed bottles with a volume of 750 mL are used in conjunction with an inlet part, following the established Rietema design. Experimental tests are conducted with dilute phase separation, using 0.1–200 µm test particles in water. Comparisons between a bottle-based HC and a conventional Rietema design were conducted, establishing a benchmark against the standard. The findings revealed a noticeable correlation between separation efficiency and cone geometry. Conical designs demonstrated enhanced separation, particularly at lower volume flows. At the highest volume flow of 75 L min−1, the best performing bottle cyclones showed separation efficiencies of 78.5%, 78.4% and 77.9% and therefore are in a competitive range with 78.0% efficiency, achieved using the commercial Rietema design. Minimal disparities in cut sizes were observed in terms of separation grade efficiency among the models tested. Variations in separation efficiency and fractional efficiency curves indicated nuanced differences in classification efficiency.

Holistic evaluation of a machine learning-based timing calibration for PET detectors under varying data sparsity

Objective. Modern PET scanners offer precise TOF information, improving the SNR of the reconstructed images. Timing calibrations are performed to reduce the worsening effects of the system components and provide valuable TOF information. Traditional calibration procedures often provide static or linear corrections, with the drawback that higher-order skews or event-to-event corrections are not addressed. Novel research demonstrated significant improvements in the reachable timing resolutions when combining conventional calibration approaches with machine learning, with the disadvantage of extensive calibration times infeasible for a clinical application. In this work, we made the first steps towards an in-system application and analyzed the effects of varying data sparsity on a machine learning timing calibration, aiming to accelerate the calibration time. Furthermore, we demonstrated the versatility of our calibration concept by applying the procedure for the first time to analog readout technology. Approach. We modified experimentally acquired calibration data used for training regarding their statistical and spatial sparsity, mimicking reduced measurement time and variability of the training data. Trained models were tested on unseen test data, characterized by fine spatial sampling and rich statistics. In total, 80 decision tree models with the same hyperparameter settings, were trained and holistically evaluated regarding data scientific, physics-based, and PET-based quality criteria. Main results. The calibration procedure can be heavily reduced from several days to some minutes without sacrificing quality and still significantly improving the timing resolution from (304±5)ps to (216±1)ps compared to conventionally used analytical calibration methods.Significance. This work serves as the first step in making the developed machine learning-based calibration suitable for an in-system application to profit from the method’s capabilities on the system level. Furthermore, this work demonstrates the functionality of the methodology on detectors using analog readout technology. The proposed holistic evaluation criteria here serve as a guideline for future evaluations of machine learning-based calibration approaches.

Techno-economic optimization of photovoltaic (PV)-inverter power sizing ratio for grid-connected PV systems

- The accurate sizing of the inverter, specifically the power sizing ratio (PSR) plays a vital role in maximizing energy production and economic benefits. Existing studies often overlook the complex interplay between maximizing energy capture and minimizing inverter-related economic costs when selecting the optimal PSR. This research presents a techno-economic approach to optimizing the PSR for grid-connected photovoltaic (PV) systems. A simulation model is developed, incorporating real weather data and inverter efficiency curves. The model is calibrated using a Pattern Search Algorithm (PSA) to ensure an accurate representation of real-world inverter behavior by achieving a minimum relative difference of 13.78 % (Norm-RMS) in AC power output. The analysis explores the trade-off between PSR, annual energy yield, and inverter clipping. An optimal PSR of 1.19 is identified, balancing energy capture (up to 2000W inverter capacity) and economic efficiency. This approach promotes cost-effective inverter selection and wider PV adoption.

Accurate Determination of Camera Quantum Efficiency from a Single Image.

Knowledge of spectral sensitivity is important for high-precision comparison of images taken by different cameras and recognition of objects and interpretation of scenes for which color is an important cue. Direct estimation of quantum efficiency curves (QECs) is a complicated and tedious process requiring specialized equipment, and many camera manufacturers do not make spectral characteristics publicly available. This has led to the development of indirect techniques that are unreliable due to being highly sensitive to noise in the input data, and which often require the imposition of additional ad hoc conditions, some of which do not always hold. We demonstrate the reason for the lack of stability in the determination of QECs and propose an approach that guarantees the stability of QEC reconstruction, even in the presence of noise. A device for the realization of this approach is also proposed. The reported results were used as a basis for the granted US patent.

Optimal planning of renewable energy park for green hydrogen production using detailed cost and efficiency curves of PEM electrolyzer

Installing multi-renewable energy (RE) power plants at designated locations, known as RE parks, is a promising solution to address their intermittent power. This research focuses on optimizing RE parks for three scenarios: photovoltaic (PV)-only, wind-only, and hybrid PV-wind, with the aim of generating green hydrogen in locations with different RE potentials. To ensure rapid response to RE fluctuations, a Proton Exchange Membrane (PEM) electrolyzer is employed. Furthermore, this research proposes detailed models for manufacturer-provided wind power curves, electrolyzer efficiency against its operating power, and electrolyzer cost towards its capacity. Two optimization cases are conducted in MATLAB, evaluating the optimum sizes of the plants in minimizing levelized cost of hydrogen (LCOH) using classical discrete combinatorial method and determining the ideal PV-to-wind capacity ratio for operating PEM electrolyzer within hybrid PV-wind parks using particle swarm optimization. Numerical simulations show that wind power-based hydrogen production is more cost-effective than PV-only RE parks. The lowest LCOH, $4.26/kg H2, and the highest LCOH, $14.378/kg H2, are obtained from wind-only and PV-only configurations, respectively. Both occurred in Adum-Kirkeby, Denmark, as it has highest average wind speed and lowest irradiance level. Notably, LCOH is reduced with the hybrid PV-wind configuration. The results suggest the optimum PV-to-wind capacity ratio is 65:35 on average and indicate that LCOH is more sensitive to electrolyzer's cost than to electricity tariff variation. This study highlights two important factors, i.e., selecting the suitable location based on the available RE resources and determining the optimum size ratio between the plants within the RE park.

Exploring the mechanisms behind dual-peak formation in energy harvesting efficiency of semi-passive flapping airfoils with varied spring stiffness

Flapping airfoil energy harvesting has emerged as a promising paradigm inspired by birds and marine organisms. In this investigation, a semi-passive flapping airfoil device with a predetermined pitching motion was examined using a transient numerical simulation method employing an overlapping grid technique. The impact of the spring stiffness on the energy harvesting characteristics of the flapping airfoil was analyzed through the implementation of the integral momentum theorem and dynamic mode decomposition. The results demonstrated that the efficiency and power curves of the flapping airfoil exhibited a distinctive bimodal M shape in response to variations of the spring stiffness. The initial peak efficiency point, which corresponded to the system's natural frequency in proximity to the pitching frequency, was found to be heavily influenced by the presence of leading-edge vortexes, thereby influencing lift generation. By utilizing the integral momentum theorem, it was determined that the Lamb vector term, fluid acceleration term, and total pressure term predominantly contributed to the lift generation. The emergence of the second peak efficiency point was primarily linked to torque variations induced by trailing-edge vortexes.

Enhancing overall performance of thermophotovoltaics via deep reinforcement learning-based optimization

Thermophotovoltaic (TPV) systems can be used to harvest thermal energy for thermoelectric conversion with much improved efficiency and power density compared with traditional photovoltaic systems. As the key component, selective emitters (SEs) can re-emit tailored thermal radiation for better matching with the absorption band of TPV cells. However, current designs of the SEs heavily rely on empirical design templates, particularly the metal-insulator-metal (MIM) structure, and lack of considering the overall performance of TPV systems and optimization efficiency. Here, we utilized a deep reinforcement learning (DRL) method to perform a comprehensive design of a 2D square-pattern metamaterial SE, with simultaneous optimization of material selections and structural parameters. In the DRL method, only the database of refractory materials with gradient refraction indexes needs to be prepared in advance, and the whole design roadmap will automatically output the SE with optimal Figure-of-Merit (FoM) efficiently. The optimal SE is composed of a novel material combination of TiO2, Si, and W substrate, with its thickness and structure precisely optimized. Its emissivity spectra match well with the external quantum efficiency curve of the GaSb cell. Consequently, the overall performance of TPV is significantly enhanced with an output power density of 5.78 W/cm2, an energy conversion efficiency of 38.26%, and a corresponding FoM of 2.21, surpassing most existing designs. The underlying physics of optimal SE is explained by the coupling effect of multiple resonance modes. This work advances the practical application potential of TPV systems and paves the way for addressing other multi-physics optimization problems and metamaterial designs.

Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis

ObjectivesTo investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT). Methods21 stroke patients and 20 healthy subjects were enrolled, with the stroke patients randomized to either control group (n = 11) or robot group (n = 10), and resting-state functional magnetic resonance imaging data were collected. The global network metrics were obtained using graph theory analysis and compared between stroke patients and healthy subjects, and the effect of the RAGT on the whole-brain networks was explored. ResultsCompared to healthy subjects, area under the curve (AUC) for small-worldness (σ), clustering coefficient (Cp), global efficiency (Eg) and mean local efficiency (Eloc) were significantly lower in stroke patients, whereas AUC for characteristic path length (Lp) were significantly higher. Compared with the control group, patients in robot group showed significant improvement in lower limb motor function, balance function and walking function after intervention, with a significant reduction in the AUC of Cp. Moreover, the improvement of walking function was positively correlated with the changes of AUC of σ and Eg, and negatively correlated with the changes of AUC of Cp. ConclusionsSmall-worldness and network efficiency were significantly reduced after stroke, whereas RAGT decreased characteristic path length and promoted normalization of the whole-brain network, and this change was associated with improvement in walking function. Our findings reveal the mechanism by which RAGT regulates network reorganization and neuroplasticity after stroke.

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control.

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms.

Fulvenallenyl Radical (C7H5·)-Mediated Gas-Phase Synthesis of Bicyclic Aromatic C10H8 Isomers: Can Fulvenallenyl Efficiently React with Closed-Shell Hydrocarbons?

To understand the reactivity of resonantly stabilized radicals, often found in relevant concentrations in gaseous environments, it is important to determine their main reaction pathways. Here, it is investigated whether the fulvenallenyl radical (C7H5·) reacts preferentially with closed-shell molecules or radicals. Electronic structure calculations on the C10H9 potential energy surface accessed by the reactions of C7H5· with methylacetylene (CH3CCH) and allene (H2CCCH2) were combined with RRKM-ME calculations of temperature- and pressure-dependent rate constants using the automated EStokTP software suite and kinetic modeling to assess the reactivity of C7H5· with closed-shell unsaturated hydrocarbons. Experimentally, the reactions were attempted in a chemical microreactor heated to 998 ± 10 K by preparing fulvenallenyl radicals via pyrolysis of trichloromethylbenzene (C7H5Cl3) and seeding the radicals in methylacetylene or allene carrier gas, with product identification by means of photoionization mass spectrometry. The measured photoionization efficiency curve of m/z = 128 was assigned to a linear combination of the reference curves of two C10H8 isomers, azulene (minor) and naphthalene (major), presumably resulting from the C7H5· plus C3H4 reactions. However, the calculations demonstrated that these reactions are too slow, and kinetic modeling of processes in the reactor allowed us to conclude that the observation of naphthalene and azulene is due to the C7H5· plus C3H3· reaction, where propargyl is produced by direct hydrogen atom abstraction by chlorine (Cl) atoms from allene or methylacetylene and Cl stem from the pyrolysis of C7H5Cl3. Modeling results under the copyrolysis conditions of toluene and methylacetylene in high-temperature shock tube experiments confirmed the prevalence of the fulvenallenyl reaction with propargyl over its reactions with C3H4 even when the concentrations of allene and methylacetylene largely exceed that of propargyl. Overall, the reactions of fulvenallenyl with both allene and methylacetylene were found to be noncompetitive in the formation of naphthalene and azulene thus attesting the inefficiency of the fulvenallenyl radical reactions with the prototype closed-shell hydrocarbon species. In the meantime, the new reaction pathways revealed, including H-assisted isomerizations between C10H8 isomers and decomposition reactions of various C10H9 isomers, emerge as relevant and are recommended for inclusion in combustion kinetic models for naphthalene formation.

Calibration for in vivo monitoring of thyroid of exposed population at risk of intakes of radioiodine isotopes due to a nuclear or radiological emergency

This work presents an improvement of the calibration of a broad energy germanium detector (BEGe) using age dependent neck-thyroid phantoms for thyroid monitoring of exposed individuals (workers and members of the public), allowing the detection and quantification of incorporated radioiodine isotopes in a nuclear medicine frame or in case of accident of a nuclear reactor.A BEGe detector was calibrated using a family of age-dependent thyroid-neck phantoms that simulate thyroids of 1, 5, 10, 15 year-old children and (male and female) adults, based in ICRP Publication 8 (ICRP, 2002) and ANSI 13.44 Standard (ANSI, 2014).The calibration of the BEGe detector for all neck phantoms was performed with the same counting geometry, centered under the BE Ge detector and at the same distance of 15 cm. Using same counting parameters allows to compare efficiency curves of thyroid monitoring depending with age. The in vivo measurements with the BE Ge detector allow to identify X rays and gamma emissions between 10 and 1000 keV with a great resolution and to quantify activities of radioiodine isotopes in the thyroid of exposed population.Calibration curves have been obtained for different sizes of the thyroid, depending with age. This study of counting efficiency using the main energies of the most important radioiodine isotopes result in accurate activity calculations. Detection limits were obtained for each radioiodine isotope, using blank phantoms of each age.

Hybrid Energy Storage System Dispatch Optimization for Cost and Environmental Impact Analysis

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating energy storage systems (ESSs) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This article compares the performance of hybrid energy storage systems (HESSs) to a single battery, evaluating their energy supply cost and environmental impact through optimization problems. The optimization model is based on a MILP incorporating the energy and degradation terms. It generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load. Seven technologies are assessed, with an example applied to an industrial site combining a vanadium redox flow battery (VRFB) and lithium battery considering the demand of a local load (building). The results indicate that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.