Uncertainty Of Efficiency Research Articles

Study on measurement uncertainty of energy conversion efficiency of tidal energy converters

Accurate assessment of the measurement uncertainty in the energy conversion efficiency of tidal energy converters is pivotal for a comprehensive analysis of experimental results. This paper presents a sophisticated measurement uncertainty assessment model which is based on energy conversion efficiency experiments of tidal energy converters. The model conducts a careful assessment of the input quantities using both Type A and Type B standard measurement uncertainties and provides an extended understanding of the experimental result uncertainties. The findings reveal that: (1) Even with human factors excluded, the experimental results show a dispersion ranging from 1.5 % to 4.2 % under the optimal experimental conditions; (2) Compared with the Type B standard measurement uncertainty evaluation method, the experimental results based on the Type A standard measurement uncertainty evaluation are more discrete, but the experimental results are more realistic; (3) Through the data analysis of the experimental results, the flow rate becomes a primary factor influencing the measurement uncertainty of experimental results. This paper provides a theoretical reference for scientific analysis of energy capture efficiency of tidal energy converters and for improving the credibility of experimental results.

Control of simulated ocean ecosystem indicators by biogeochemical observations

To protect marine ecosystems threatened by climate change and anthropic stressors, it is essential to operationally monitor ocean health indicators. These are metrics synthetizing multiple marine processes relevant to the users of operational services. Here we assess if selected ocean indicators simulated by operational models can be controlled (here meaning constrained effectively) by biogeochemical observations, by using a newly proposed methodological framework. The method consists in firstly screening the sensitivities of the indicators with respect to the initial conditions of the observable variables. These initial conditions are perturbed stochastically in Monte Carlo simulations of one-dimensional configurations of a multi-model ensemble. Then, the models are applied in three-dimensional ensemble assimilation experiments, where the reduction of the ensemble variance corroborates the controllability of the indicators by the observations. The method is applied for ten relevant ecosystem indicators (ranging from inorganic chemicals to plankton production), seven observation types (representing data from satellite and underwater platforms), and an ensemble of five biogeochemical models of different complexity, employed operationally by the European Copernicus Marine Service. We demonstrate that all the indicators are controlled by one or more types of observations. In particular, the indicators of phytoplankton phenology are controlled and improved by the merged observations from the surface ocean colour and chlorophyll profiles. Similar observations also control and reduce the uncertainty of the plankton community structure and production. However, the uncertainty of the trophic efficiency and POC increases when assimilating chlorophyll-a data, though observations were not available to assess whether that was due to a worsen model skill. We recommend that the assessment of controllability proposed here becomes a standard practice in designing operational monitoring, reanalysis and forecast systems, to ultimately provide the users of operational services with more precise estimates of ocean ecosystem indicators.

Measuring uncertainty of hydraulic generator efficiency by calorimetric method

Abstract Calorimetric method is generally performed on determination of hydraulic generator efficiency. Generator losses could be evaluated by measuring the heat taken by cooling medium or dissipated from a reference surface. To satisfy the requirements of measuring uncertainty in IEC 60034, special attention must be paid. An estimation method is proposed and performed on a hydraulic generator for calorimetric test. Estimation result shows that discharge of cooling water must be reduced to a rather low value to ensure the measuring uncertainty of calorimetry within 3%. Also, a lower cooling discharge can bring higher temperature rise then reduce the measuring uncertainty caused by temperature measurement. Furthermore, to reduce the measuring uncertainty of cooling discharge, flowmeters must be installed correctly.

Full characterization and calibration of a transfer standard monitor for atmospheric radon measurements

Abstract. In this work, a full characterization of the new user-friendly version of the Atmospheric Radon MONitor (ARMON), used to measure very low activity concentrations of the radioactive radon gas in the outdoor atmosphere, is carried out. The ARMON is based on the electrostatic collection of 218Po+ particles on a semiconductor detector surface. A main advantage of this instrument is that it offers high-resolution alpha-energy spectra, which will allow us to separate radon progeny (210Po, 218Po, and 214Po). The monitor feature may also allow measurements of thoron (220Rn) by collection of 216Po+, although the instrument is not calibrated for this gas. In the paper, the physical principle; the hardware configuration; and the software development of the automatic and remotely controlled ARMON, conceived and constructed within the MAR2EA and the traceRadon projects, are described. The monitor efficiency and its linearity over a wide span of radon concentration activities have been evaluated and tested here using theoretical and experimental approaches. Finally, a complete budget analysis of the total uncertainty of the monitor was also achieved. Results from the application of a simplified theoretical approach show a detection efficiency for 218Po+ of about 0.0075 (Bq m−3)−1 s−1. The experimental approach, consisting of exposing the ARMON at controlled radon concentrations between a few hundreds to a few thousands of becquerels per cubic metre (Bq m−3), gives a detection efficiency for 218Po+ of 0.0057 ± 0.0002 (Bq m−3) s−1. This last value and its independence from the radon levels were also confirmed thanks to a new calibration method which allows us, using low-emanation sources, to obtain controlled radon levels of a few tens of becquerels per cubic metre (Bq m−3). The total uncertainty of the ARMON detection efficiency obtained for hourly radon concentrations above 5 Bq m−3 was lower than 10 % (k= 1). The characteristic limits of the ARMON – being those dependent on the presence of thoron in the sampled air – were also calculated. A detection limit of 0.132 Bq m−3 was estimated in the absence of thoron. At a typical thoron concentration at atmospheric sites of 0.017 min−1, the detection limit was calculated to be 0.3 Bq m−3, but this can be reduced if using a delay volume, obtaining a decision threshold of 0.0045 Bq m−3. Current results may allow us to confirm that the ARMON is suitable to measure low-level radon activity concentrations (1–100 Bq m−3) and to be used as a transfer standard to calibrate secondary atmospheric radon monitors.

Uncertainty quantification of exchange efficiency in membrane-based air-to-air energy exchanger cores: Repetitive manufacturing and experiments

Air-to-air membrane-energy exchangers have been widely used in green buildings as passive energy-recovery technologies for energy conservation and carbon reduction. An air-to-air membrane-energy exchanger conserves building energy by forcing the indoor exhaust air and unconditioned fresh air to exchange heat and moisture. As a critical performance indicator, the energy-exchange efficiency is primarily determined by the core. To improve energy-saving performance, many studies have been conducted to analyze and enhance the efficiency of air-to-air membrane-energy exchanger cores by manufacturing, testing, modeling, and comparing different cores. However, few studies have investigated the uncertainty introduced by the manufacturing and testing processes (most related research only studied the uncertainty of the sensible efficiency), which means that some positive results of those controlled trials are possibly owing to the manufacturing tolerance or testing errors, rather than real technical progress. This study addresses this problem by quantifying the efficiency uncertainty caused by manufacturing and testing processes. Three cores were produced using the same manufacturing process and materials, followed by identical experimental tests on their sensible, latent, and enthalpy efficiencies. Comparison results suggest that even when all variables are controlled, the differential enthalpy efficiency of two “identical” cores still vary from 0 % to 2 %. Variance of repetitive testing results of a certain core can afford an enthalpy efficiency of 4 % owing to a non-ideal experimental procedure. When manufacturing tolerances and experimental errors are considered simultaneously, the differential sensible efficiency among multiple “identical” cores is within 0–5%, whereas latent efficiency data in winter shows a significant variation of 5–10 % for “identical” cores. The contributions and conclusions of this study are as follows. The efficiency uncertainty caused by manufacturing and testing processes is non-negligible, particularly in latent efficiency. This phenomenon could alert researchers and engineers in this field to conduct repetitive manufacturing and testing while trying to modify/test/compare air-to-air membrane-energy exchanger cores. Because this study is based on a limited number of cores, the quantitative results of this study should be considered a non-universal reference, and a systematic benchmark must be established in the future.

Risk-averse distributed optimization for integrated electricity-gas systems considering uncertainties of Wind-PV and power-to-gas

The uncertainties of wind power and photovoltaic (Wind-PV) systems pose a threat to the steady functioning of integrated electricity-gas systems and restrict their effective consumption. In this case, an optimal operation model considering the cost risk of Wind-PV uncertainties is proposed. Firstly, the cost risk of uncertainties in typical scenarios is measured by conditional value at risk (CVaR). Secondly, considering that the power system and the natural gas system as different stakeholders have barriers in information exchange, a distributed optimization operation model is established, and the uncertainty of the conversion efficiency of the coupling unit from Power-to-Gas (P2G) is considered. Then, the integrated electricity-gas systems are decoupled through consistency constraints, and the model is solved by an algorithm framework based on adaptive penalty ADMM. This work analyzed the impact of uncertainty and CVaR parameters on optimized operation, and discussed the performance of related distributed algorithms. The following conclusions were drawn: 1) the proposed model reduces costs by 1.72% and can measure the operation risk caused by uncertainty factors, which provide guidance for decision makers; 2) the total cost will increase by 3.9% when the uncertainty factor reaches the preset maximum value, which demonstrates that the uncertainty of conversion efficiency should be controlled to reduce the operation cost of P2G; 3) As the convergence accuracy gradually improves, the proposed method is easier to converge and converges faster.

A column generation approach for the quay crane allocation problem considering uncertain operation efficiency

This study constructs a mixed integer programming model for the integrated quay crane allocation problem to minimize vessels’ waiting time and delay time cost. Taking the uncertainty of quay crane operation efficiency into consideration, this study constructs a stochastic programming model and a distributional robust chance constraint (DRCC) model. In addition, column generation is applied to solve the three proposed models. Moreover, this paper uses real-world data from Shanghai Yangshan Port to verify the effectiveness of proposed model and algorithm. To prove the robustness of the DRCC model, this study conducts an uncertain parameters sensitivity analysis, and analyzes the performance of the models proposed above. The results show that the proposed DRCC model has better anti-interference ability. The conclusion drawn from this study has potential implications for resource allocation within port operations.

Uncertainty, Efficiency, and Stability of Mixed Traffic Flow: Stochastic Model-Based Analyses

This paper proposes a stochastic model for mixed traffic consisting of human-driven vehicles (HVs), connected automated vehicles (CAVs), and degraded connected automated vehicles (DCAVs). The model addresses the issue that most of the current literature ignores: the degradation of CAVs, and the heterogeneity and uncertainty of HVs, CAVs, and DCAVs. The source of uncertainty was the heterogeneous behavior of HVs, CAVs, and DCAVs, captured using vehicle-specific car-following relations, that is, parametric uncertainty. The proposed model allowed for the explicit investigation of the uncertainty, efficiency, and stability of mixed traffic under various CAV penetration rates, different positions of CAVs in the traffic stream, and the different degradation levels of CAVs. The numerical experiment results showed that a larger CAV penetration rate helped to reduce uncertainty and improve the efficiency and stability of traffic flow. Furthermore, we investigated the impact of different position combinations of CAVs in the mixed traffic stream on traffic performance under four scenarios: 1) CAVs randomly distributed in the traffic stream, 2) CAVs forming a platoon traveling in the front of the traffic stream, 3) CAVs forming a platoon traveling in the middle of the traffic stream, and 4) CAVs forming a platoon traveling in the rear of the traffic stream. The results demonstrated that Scenario 2 gave the best performance in reducing uncertainty and improving efficiency and stability under different CAV penetration rates, whereas Scenario 4 performed the worst. Moreover, increasing degradation levels of CAVs negatively affected the reduction of uncertainty and improvement of efficiency and stability.

Optimal energy procurement with long-term photovoltaic energy contracts considering generation uncertainty: A two-dimensional auction approach

The procurement auction scheme for long-term photovoltaic (PV) energy contracts is being implemented in various countries to ensure stable profits for potential PV generators. However, in most of these auction formats, there is a deficiency in that they consider only the contract price and capacity, neglecting to account for the uncertainty of generation efficiency. In this regard, this study proposes a procurement auction scheme for long-term photovoltaic (PV) energy contracts based on mechanism design theory. We developed a two-dimensional auction model in which PV generators bid their cost and capacity. The energy buyer then determines the winners and enters into contracts with them for a fixed period. We incorporated the capacity factor into the payoff functions of both the buyer and the sellers to reflect different generation efficiencies of generators. Following the revelation principle, we characterized the incentive-compatible, individually rational direct mechanism that maximizes the buyer’s expected payoff during the contract period. We also proposed a computation algorithm to implement the auction. Numerical analysis using data from the Korean PV auction market suggested that the proposed model demonstrates results similar to the uniform price auction in terms of the levelized cost of electricity and contract price, and these results are lower than those of the Vickrey auction. Furthermore, despite the fact that the proposed auction results in a slight increase in social costs (approximately 1% more than the Vickrey auction), it maximizes the expected procured electricity and the auctioneer’s payoff.

The encroachment strategy under eco-innovation collaboration with delayed realized efficiency information

PurposeEco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.Design/methodology/approachThis paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.FindingsThe analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.Originality/valueThe paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

A discriminative information-theoretical analysis of the regularity gradient in inflectional morphology

Over the last decades, several independent lines of research in morphology have questioned the hypothesis of a direct correspondence between sublexical units and their mental correlates. Word and paradigm models of morphology shifted the fundamental part-whole relation in an inflection system onto the relation between individual inflected word forms and inflectional paradigms. In turn, the use of artificial neural networks of densely interconnected parallel processing nodes for morphology learning marked a radical departure from a morpheme-based view of the mental lexicon. Lately, in computational models of Discriminative Learning, a network architecture has been combined with an uncertainty reducing mechanism that dispenses with the need for a one-to-one association between formal contrasts and meanings, leading to the dissolution of a discrete notion of the morpheme.The paper capitalises on these converging lines of development to offer a unifying information-theoretical, simulation-based analysis of the costs incurred in processing (ir)regularly inflected forms belonging to the verb systems of English, German, French, Spanish and Italian. Using Temporal Self-Organising Maps as a computational model of lexical storage and access, we show that a discriminative, recurrent neural network, based on Rescorla-Wagner’s equations, can replicate speakers’ exquisite sensitivity to widespread effects of word frequency, paradigm entropy and morphological (ir)regularity in lexical processing. The evidence suggests an explanatory hypothesis linking Word and paradigm morphology with principles of information theory and human perception of morphological structure. According to this hypothesis, the ways more or less regularly inflected words are structured in the mental lexicon are more related to a reduction in processing uncertainty and maximisation of predictive efficiency than to economy of storage.

Efficiency Optimization of Wireless Power Transfer System for Electric Vehicle Based on Improved Marine Predators Algorithm

Electric vehicle (EV) is the core part of future automobile technology, and safe and reliable wireless power transfer (WPT) technology is the key link to improve the intelligent driving technology of EV. In this paper, the uncertainty quantification method is proposed to guide the optimization design of WPT structure, so as to improve the efficiency of WPT. First this paper establishes a surrogate model of WPT efficiency based on the adaptive sparse polynomial chaos expansion, and the uncertainty of EVs WPT transmission efficiency is quantified, the computational efficiency is improved by about 8.4 times. Then the surrogate model is combined with the global sensitivity analysis method to quantify the impact of different variables in WPT on efficiency and screen out the variables with greater impact. Finally, this paper uses the improved marine predators algorithm to optimize the selected WPT system structure parameters. Considering the uncertainty, the average efficiency of the optimized WPT system is increased from 73.43% to 94.64%. Compared with other optimization methods, it proves that the method in this paper can optimize WPT more efficiently, and significantly improve the transmission efficiency.

Comprehensive study on uncertainties in 131I activity measurements in the thyroid gland

Activity measurements of 131I in the thyroid gland are influenced by a series of geometrical and individual anatomical factors, represented by the overlying tissue thickness (OTT), the thyroid volume, the neck-detector distance (NDD) and the detector displacement. The comprehensive evaluation of measurement uncertainty was performed by using the simulations of particle passage and the Monte Carlo method. Additionally, the measurement uncertainties in three typical 131I measurement scenarios (the measurement of nuclear accident emergency workers, nuclear medicine practitioners and patients treated with radioactive 131I) were also discussed. The results showed that the contribution of different sources to the overall measurement uncertainty varied across different scenarios. The uncertainty of counts will dominate measurement uncertainty with low activity and large NDD, while the uncertainty of efficiency will replace it under the rest conditions. Several strategies also emerged from the results, including correcting individual OTT, extending counting time appropriately and optimizing NDD. For instance, the correction of OTT would reduce the uncertainty from 25% to 7.7% (k = 1) in a presumptive measurement of nuclear accident. In summary, this study provides deeper insight into the measurement of 131I using a portable detector and has significant implications for the selection of optimal measurement parameters.

Robust optimization for designing air quality monitoring network in coal ports under uncertainty

Air quality monitoring network (AQMN) is an important management tool to mitigate the impact of air pollution from coal port operations on coastal atmospheric environment and public health. The location of monitoring stations in the AQMN is influenced by wind conditions and emission intensity of air pollutants, which is highly related to the efficiency of coal port operations. Therefore, this paper aims at solving the problem of how to design the AQMN with consideration of uncertain operational efficiency and wind conditions. This study proposes a robust optimization method to address the impact of uncertainty on the design of AQMN in coal ports. Firstly, the deterministic AQMN design problem is summarized as a maximum weighted covering location problem (MWCLP) and formulated as a gradual covering model with the cooperative covering strategy. Then, a linear approximation method is applied to the non-linear model to improve computational efficiency. Secondly, a method for constructing uncertain scenarios is proposed by combining sampling method, port emission inventory, and atmospheric diffusion simulation. After that, a robust optimization model is developed based on the concept of p-robustness to ensure that the relative regret value of each uncertain scenario is less than a specified threshold. Finally, we take a coal port in northern China as a case study. Results show that the average monitoring capacity of robust layout is 5.25% and 1.57% higher than that of conventional layout and deterministic layout. The robust layout ensures a relative regret value of no more than 3.41% for each scenario. The proposed method can provide port and environmental authorities with robust decision support of AQMN design under limited budget and uncertainty of operational efficiency and wind conditions.

Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins

Single-molecule Förster-resonance energy transfer (smFRET) experiments allow the study of biomolecular structure and dynamics in vitro and in vivo. We performed an international blind study involving 19 laboratories to assess the uncertainty of FRET experiments for proteins with respect to the measured FRET efficiency histograms, determination of distances, and the detection and quantification of structural dynamics. Using two protein systems with distinct conformational changes and dynamics, we obtained an uncertainty of the FRET efficiency ≤0.06, corresponding to an interdye distance precision of ≤2 Å and accuracy of ≤5 Å. We further discuss the limits for detecting fluctuations in this distance range and how to identify dye perturbations. Our work demonstrates the ability of smFRET experiments to simultaneously measure distances and avoid the averaging of conformational dynamics for realistic protein systems, highlighting its importance in the expanding toolbox of integrative structural biology.

Prediction of Process Parameters for Ultra-Precision Optical Processing Based on Dual-Stacked LSTM

High-precision and large-aperture optical components have important applications in modern optics and optoelectronics. However, the traditional continuous polishing technology of optical components relies heavily on the processing experience of the processing personnel. The surface shape of the pitch lap is judged by frequent offline measurement of the surface shape of the processing workpiece, and then the processing personnel judges how to adjust the next process parameters through their own experience, which leads to uncertainty of processing time and low processing efficiency. In this paper, based on the historical processing data, including the surface parameters of workpieces and process parameters before and after each processing, a machine learning-based prediction method of process parameters is proposed. At first, taking the surface shape of the pitch lap as the hidden parameter of the model, a time-series mathematical model of the forward and reverse processing processes is constructed. Theoretical and experimental results show that the prediction method in this paper can effectively reduce the processing time and improve the stability of the workpiece quality.

Low-carbon economic dispatch of integrated energy system considering the uncertainty of energy efficiency

The integrated energy system (IES) is recognized as an effective measure to promote energy efficiency and environmental protection. However, the uncertain efficiency of energy devices under variant working conditions threatens the operation of the IES. In this paper, a low-carbon economic dispatch method for the IES considering the uncertainty of energy efficiency is developed. Specifically, a dynamic energy hub (DEH) is established by integrating an efficiency correction technique into the traditional energy hub (EH). The deep neural network (DNN) method with excellent accuracy in nonlinear mapping is utilized to correct energy efficiency affected by the load level, temperature and pressure. Based on the DEH, a low-carbon economic dispatch model is formulated to minimize operational costs. Case studies verify the effectiveness of the proposed method, which can enhance the accuracy of dispatch schemes and simultaneously, promote the low-carbon economic dispatch of the IES.

RETRACTED: Repeatability in measurements and control settings of a small francis turbine test rig

A reduced scaled-down hydraulic turbine test setup has been designed. The test setup is made to investigate the mitigation of pressure pulsations like the rotating vortex rope (RVR) and its formation and problems related to prototype operation. The setup arrangements and instrumentations follow mostly the international electro-technical commission (IEC) standards. This paper presents the runner hub modification, calibration procedure, repeatability and systematic uncertainty estimation, and measurement methods as per the guidelines given in IEC 60193 and IEC 60041. The calibration and regression curves of all devices are presented. A LABVIEW program for open-loop operation at a constant head and closed-loop operation at constant discharge is also presented. Finally, the systematic and random uncertainty of hydraulic efficiency is discussed. • Design and development of small turbine test rig. • Design modification of runner cone profile. • Working of control feedback system using Partial Integral Differential (PID) controller. • Calibration of different instruments used in the small turbine test rig. • Random and systematic uncertainty of the small turbine test rig.

Maximum efficiency tracking design of wireless power transmission system based on machine learning

For the typical magnetically coupled wireless power transmission system, power consumption is often caused by the impedance effect of the primary and secondary sides, the loss and heat generation of the actual semiconductor devices and the change of the coupling coefficient caused by distance. The uncertainty of frequency tracking efficiency caused by high enough coil transmission with low load receiving efficiency and load impedance changes makes it difficult to stabilize the output of the optimal efficiency point of the system. In order to solve this problem, a wireless power transmission system is proposed based on the traditional wireless power transmission system a wireless power transmission system is proposed to predict and stabilize the variable impedance, so as to stably track the efficiency of the system in the resonant frequency range. It is not affected by load jitter, actual circuit parameters and environmental disturbance. The test results show that the transmission efficiency of tracking frequency and predicting impedance can be realized at a certain distance, and the difference between the prediction error and the actual efficiency is about 1%–3%.

Neutron tagging following atmospheric neutrino events in a water Cherenkov detector

We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimated to be 26%, with a mis-tag rate of 0.016 per neutrino event. The uncertainty of the tagging efficiency is estimated to be 9.0%. Measurement of the tagging efficiency with data from an Americium-Beryllium calibration agrees with this value within 10%. The tagging procedure was performed on 3,244.4 days of SK-IV atmospheric neutrino data, identifying 18,091 neutrons in 26,473 neutrino events. The fitted neutron capture lifetime was measured as 218±9 μs.