The analysis of the cladding mechanical performance is essential for predicting rod failure during the heat-up phase of a loss-of-coolant accident (LOCA). Previous modelling and simulation exercises ...have shown that there is room for improvement in the accuracy of fuel performance simulations under such conditions. In this work, TUmech, a simplified standalone version of the mechanical model in TRANSURANUS, has been developed and coupled with FRAPTRAN-2.0. After highlighting the most significant differences between both approaches, particularly in their mechanical treatment and material property correlations, simulations have been performed for IFA-650.2 (fresh fuel) and IFA-650.10 (high burnup fuel) of the Halden LOCA test series. The results obtained from both FRAPTRAN versions have been compared with experimental data and benchmarked against TRANSURANUS estimates, focusing on rod inner gas pressure, post-test cladding outer diameter, and burst time. In both scenarios, FRAPTRAN-TUmech has proven to enhance the accuracy in predicting these three variables compared to the FRAPTRAN default version. However, further validation is required before drawing definitive conclusions based on the encouraging results.
•TUmech has been developed for predicting the cladding mechanical response under LOCA conditions.•TUmech has been coupled with FRAPTRAN-2.0 as an alternative mechanical model.•The FRAPTRAN-TUmech tool has been validated with two Halden LOCA tests.•The obtained results have been benchmarked against FRAPTRAN-2.0 and TRANSURANUS.•FRAPTRAN-TUmech enhances the accuracy of the predictions compared to FRAPTRAN-2.0 and TRANSURANUS.
This paper aims to present a quantitative investigation of a case study based on a live industrial refrigeration system exhibiting complex and dynamic behaviour such as randomness, concurrency, and ...time dependency. The study employs a state-space stochastic Markovian process to model the interactions among the key functional elements of the system. Furthermore, the scientific approach pursued in this study would help improve the availability of the considered plant by establishing a trade-off between investment, economy and quality. Based on the analysis of results, a framework for Decision Support Priorities (DSP) is proposed, emphasizing the criticality of various functional units. This framework could also help set and prioritize maintenance, spare parts, and human resource requirements accordingly.
•QN-ACTR-SA is a model designed to simulate driver Situation Awareness (SA).•Utilizes QN-ACTR framework and SEEV model to simulate drivers SA.•Interacts with simulators for realistic ...predictions.•Validated against empirical data from drivers in complex and easy driving conditions.
The goal of this research is to computationally model and simulate the situation awareness (SA) of drivers. A computational model in a cognitive architecture was developed that can interact with a driving simulator to infer quantitative predictions of drivers’ SA. The model uses the Queueing Network Adaptive Control of Thought-Rational (QN-ACTR) framework as a foundation and integrates a dynamic visual sampling model (SEEV) to create QN-ACTR-SA, which simulates attention allocation patterns of human drivers at SA Level 1 (i.e., perception of critical elements). QN-ACTR-SA also incorporates a driver model that can interact with a driving simulator. A validation study was conducted to determine whether Level 1 SA results produced with the QN-ACTR-SA model correspond to empirical data collected from human drivers (14 participants) for the same tasks. Both QN-ACTR-SA and human participants were probed for SA using two approaches: within-task queries using the Situation Awareness Global Assessment Technique (SAGAT) and post-experiment questions. A comparative assessment demonstrated that QN-ACTR-SA could reasonably simulate drivers’ Level 1 SA for two driving conditions: easy (with few vehicles and signboards) and complex (with dense traffic and signboards). QN-ACTR-SA fit for human SAGAT scores (possible range 0–100) resulted in a mean absolute percentage error (MAPE) of 5.0% and the root means square error (RMSE) of 3.5. Model fit for post-experiment human SA results was MAPE of 6.7% and RMSE of 6.1. Limitations of QN-ACTR-SA as a predictive model and areas of future research are discussed.
Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance ...of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online delivery. There is an opportunity to compare the experiences of students in the MOOC environment in this period, particularly in terms of the difference of engagement, semantics and sentiment/stress behaviours in 2019 and 2020. In this study, we use a dataset from AdelaideX between this time period to identify the most significant features that impact student outcomes. Where previous machine learning approaches used singular features such as student interaction or sentiment in discussion forum posts, we incorporate three feature categories of engagement, semantics and sentiment/stress in an ensemble model is based on voting and stacked methods to determining the relationship between them and academic performance. From our results, we discover that sentiment/stress played little part in academic performance and was relatively unchanged in online courses in this dataset between 2019 and 2020. We present two individual student cases to further contextualise our findings.
•New understanding of MOOC students' engagement and emotional behaviour.•Extraction and analysis of various features of student behavioural data.•Case studies for engagement, semantics and sentiment/stress of MOOC students.
A model that can be used to predict the performance of Membrane Capacitive Deionization (MCDI), including current efficiency, flowrate of the product water, water recovery and energy consumption is ...established in this work. The model was developed using a Response Surface Model approach that considers the influence of influent conductivity, flowrate passing by the electrodes and applied current on MCDI system performance rather than relying on electrochemical principles or the description of the intrinsic properties of the carbon electrodes and ion exchange membranes. In the scenarios tested here, flowrate contributes the most towards MCDI performance. The appropriateness of using NaCl for an MCDI performance model is confirmed. The model can be applied to extensive applications with two examples detailed here including a small town potable application of 100 m3/day with influent water of 2000 μS/cm and an industrial wastewater application of 1000 m3/day with influent water of 4000 μS/cm. A costing model is developed and applied to the two scenarios with the optimised levelized cost of water (LCOW) found to be US$0.74/m3 for the small town and US$1.08/m3 to US$1.41/m3 for the industrial wastewater (product water quality dependent). By maximising the production rate, the LCOW can be minimised. The model presented here can be applied to a wide range of applications or used as a framework for efficient performance modelling of other MCDI configurations.
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•A framework for a simplified MCDI performance model developed.•MCDI performance model based on influent conductivity, flowrate and applied current.•Levelized cost of water (LCOW) of US$0.74/m3 for MCDI treated potable supply.•LCOW can be minimised by maximising MCDI production rate.
Abstract
Considering local population dynamics and dispersal is crucial to project species' range adaptations in changing environments. Dynamic models including these processes are highly computer ...intensive, with consequent restrictions on spatial extent and/or resolution.
We present CATS, an open‐source, extensible modelling framework for simulating spatially and temporarily explicit population dynamics of plants. It can be used in conjunction with species distribution models, or via direct parametrisation of vital rates and allows for fine‐grained control over the demographic and dispersal processes' models.
The performance and flexibility of CATS is exemplified (i) by modelling the range shift of four plant species under three future climate scenarios across Europe at a spatial resolution of 100 m., and (ii) by exploring consequences of demographic compensation for range expansion on artificial landscapes.
The presented software attempts to leverage the availability of computational resources and lower the barrier of entry for large‐extent, fine‐resolution simulations of plant range shifts in changing environments.
We investigated the relationship of the time-dependent behaviour of muscle oxygen saturation SmO 2 (t), phosphagen energy supply W PCr (t) and blood lactate accumulation ΔBLC(t) during a 60-s all-out ...cycling sprint and tested SmO 2 (t) for correlations with the end of the fatigue-free state t Ff , maximal pedalling rate PR max and maximal blood lactate accumulation rate v̇La max . Nine male elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) on a cycle ergometer. Crank force and cadence were monitored continuously to determine PR max and t Ff based on force-velocity profiles. SmO 2 of the vastus lateralis muscle and respiratory gases were measured until the 30th minute after exercise. W PCr was calculated based on the fast component of the post-exercise oxygen uptake for each sprint. Before and for 30 minutes after each sprint, capillary blood samples were taken to determine the associated ΔBLC. Temporal changes of SmO 2 , W PCr and ΔBLC were analysed via non-linear regression analysis. v̇La max was calculated based on ΔBLC(t) as the highest blood lactate accumulation rate. All models showed excellent quality (R 2 > 0.95). The time constant of SmO 2 (t) τSmO 2 = 2.93±0.65 s was correlated with the time constant of WPCr(t) τ PCr = 3.23±0.67 s (r = 0.790, p < 0.012), v̇La ma x = 0.95±0.18 mmol·l −1 ·s −1 (r = 0.768, p < 0.017) and PR max = 299.51±14.70 rpm (r = -0.670, p < 0.049). t Ff was correlated with τ SmO2 (r = 0.885, p < 0.001). Our results show a time-dependent reflection of SmO 2 kinetics and phosphagen energy contribution during a 60-s maximal cycling sprint. A high v̇La max results in a reduction, a high PR max in an increase of the desaturation rate. The half-life of SmO 2 desaturation indicates the end of the fatigue-free state.
The accuracy of photovoltaic (PV) performance forecasts is essential for improving grid penetration, fault detection, and financing of new installations. Failing to account for the spectral influence ...on PV performance can lead to weekly errors of up to 14% even for relatively stable technologies such as polycrystalline silicon. There exist a range models, known as spectral correction functions (SCFs), to account for the spectral influence on PV performance forecasts. These SCFs use different methods to characterise both the shift in PV performance due to the spectrum, and the solar spectrum itself. This review analyses the merits and limitations of seven commonly used spectral characterisation indices — five proxy variables (air mass, clearness index, precipitable water, aerosol, diffuse solar radiation ratio) and two variables extracted from the spectral distribution (average photon energy, depth of a water absorption band). The same analytical approach is adopted to review a further four indices (mismatch factor and its variants, (weighted) useful fraction, normalised short-circuit current) that are commonly used to characterise the variation in PV performance due to the solar spectrum. A review of ten SCFs that are based on these indices is undertaken to analyse the current state of the art of spectral correction modelling. The results of the review show that whereas some proxy-variable methods offer a simple and convenient way to account for the spectral influence in PV performance forecasts, they are surpassed in terms of accuracy by SCFs based on parameters derived directly from the spectrum, such as the average photon energy and the depth of spectral absorption bands. A decision-making framework is proposed to guide PV performance modellers in their choice of spectral correction model. The framework considers system specifications, climate, data availability, etc. The results of this work may be applied in, for example, software packages for PV performance forecasting to enable more accurate case-specific power forecasts. In future work, a standardised comparison of all SCFs and their respective indices is necessary to quantify the differences between a wider range of models than is currently available in the literature and substantiate the proposed framework.
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•Seven spectral characterisation and five PV spectral mismatch indices are compared.•Direct indices are more accurate than proxies but require non-trivial measurements.•10 spectral correction models are reviewed in terms of accuracy, usability, and scope.•A framework is proposed to inform model selection according to use case scenario.•A worldwide, standardised, empirical model comparison is found to be required.
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•A cogeneration plant based on a gas turbine and a heat recovery boiler was analysed.•51 constrains and 74 variables were identified, of which 44 were unmeasured.•Gross error ...detection identified three process variables with incorrect measurements.•Error is reduced when the model is based on an ANN instead of polynomial models.•The advantage of training the ANN-based model with reconciled data is demonstrated.
This contribution represents a practical application of predictive thermal modelling of an existing cogeneration plant. The analysed cogeneration plant consists of a gas turbine coupled to a heat recovery steam generator, which produces two streams of superheated steam (65 bar and 10 bar) and a thermal oil stream at 350 °C. The proposed model was based on an artificial neural network and was trained using real operational data. However, although data acquisition systems currently used in power generation plants allow for the recording of multiple measurements using small sampling intervals, this does not guarantee a satisfactory analysis of operational data. Therefore, the potential of artificial neural networks can result in incorrect or imprecise results if the calibration of the network is performed with inconsistent or highly uncertain datasets. The novelty of this work consisted on the application of data reconciliation to the real dataset before the model training, in order to minimize the typical uncertainty associated with plant instrumentation measurements. The results obtained demonstrated the advantage of training the network with reconciled data and that modelling error is reduced for all analysed outputs when the model is based on artificial neural networks instead of polynomial models.
Introduction: This study presented a novel approach to predict future front crawl swimming world records (WRs) by employing a methodology that integrated performance data from both running and front ...crawl swimming. Methods: By extracting the top one running and swimming performances from 1995 to 2023 and applying a model that correlates physiological characteristics such as maximum aerobic power, anaerobic capacity, the decrement in maximum power with prolonged effort, and performance speed and duration, it was possible to project the potential record-breaking performances in 2024 across various swimming distances for both male and female athletes. Furthermore, this approach was expected to be less susceptible to the influence of the full-body swimsuit era, which may have disrupted the typical trajectory of swimming performance progression. Results: The average relative error between the top one and estimated speeds in front crawl swimming (50–1,500 m, from 1995 to 2023, and for male and female) was 0.56% ± 0.17%. For male, WR in longer distances have been predicted with new WR in the 400 and 800 m. A more ambitious prediction was noted among female, with twice as many WR as among male illustrated by new WR in the 50, 200, 400 and 800 m. Discussion: This study illustrated that the utilization of a prediction model based on physiological parameters yielded plausible time estimates. Additionally, the research accentuated the ongoing trajectory of surpassing existing WR into 2024, illustrating the competitive zeal fueled by an emerging framework of exceptional swimmers.
