Cross-code comparison of the edge codes SOLPS-ITER, SOLEDGE2D and UEDGE in modelling a low-power scenario in the DTT

As reactor-level nuclear fusion experiments are approaching, a solution to the power exhaust issue in future fusion reactors is still missing. The maximum steady-state heat load that can be exhausted by the present technology is around 10 MW/m2. Different promising strategies aiming at successfully managing the power exhaust in reactor-relevant conditions such that the limit is not exceeded are under investigation, and will be tested in the Divertor Tokamak Test (DTT) experiment. Meanwhile, the design of tokamaks beyond the DTT, e.g. EU-DEMO/ARC, is progressing at a high pace. A strategy to work around the present lack of reactor-relevant data consists of exploiting modelling to reduce the uncertainty in the extrapolation in the design phase. Different simulation tools, with their own capabilities and limitations, can be employed for this purpose. In this work, we compare SOLPS-ITER, SOLEDGE2D and UEDGE, three state-of-the-art edge codes heavily used in power exhaust studies, in modelling the same DTT low-power, pure-deuterium, narrow heat-flux-width scenario. This simplified, although still reactor-relevant, testbed eases the cross-comparison and the interpretation of the code predictions, to identify areas where results differ and develop understanding of the underlying causes. Under the conditions investigated, the codes show encouraging agreement in terms of key parameters at both targets, including peak parallel heat flux (1-45%), ion temperature (2-19%), and inner target plasma density (1-23%) when run with similar input. However, strong disagreement is observed for the remaining quantities, from 30% at outer mid-plane up to a factor 4-5 at the targets. The results primarily reflect limitations of the codes: the SOLPS-ITER plasma mesh not reaching the first wall, SOLEDGE2D not including ion-neutral temperature equilibration, and UEDGE enforcing a common ion-neutral temperature. Potential improvements that could help enhance the accuracy of the code models for future applications are also discussed.

How Can We Adapt Thermal Comfort for Disabled Patients? A Case Study of French Healthcare Buildings in Summer

This paper investigates adaptive thermal comfort during summer in medical residences that are located in the French city of Troyes and managed by the Association of Parents of Disabled Children (APEI). Thermal comfort in these buildings is evaluated using subjective measurements and objective physical parameters. The thermal sensations of respondents were determined by questionnaires, while thermal comfort was estimated using the predicted mean vote (PMV) model. Indoor environmental parameters (relative humidity, mean radiant temperature, air temperature, and air velocity) were measured using a thermal environment sensor during the summer period in July and August 2018. A good correlation was found between operative temperature, mean radiant temperature, and PMV. The neutral temperature was determined by linear regression analysis of the operative temperature and Fanger’s PMV model. The obtained neutral temperature is 23.7 °C. Based on the datasets and questionnaires, the adaptive coefficient α representing patients’ capacity to adapt to heat was found to be 1.261. A strong correlation was also observed between the sequential thermal index n(t) and the adaptive temperature. Finally, a new empirical model of adaptive temperature was developed using the data collected from a longitudinal survey in four residential buildings of APEI in summer, and the obtained adaptive temperature is 25.0 °C with upper and lower limits of 24.7 °C and 25.4 °C.

An adaptive thermal comfort model for naturally ventilated classrooms of technical institutions in Madurai

Purpose Occupants dwelling in hot climatic regions of India for a longer term are tolerable to high temperature levels than predicted by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards. The purpose of this study is to evaluate the thermal sensations (TS) and neutral temperature of the occupants in naturally ventilated (NV) and air-conditioned (AC) classrooms of two technical institutions located in the same premises in the suburbs of Madurai. The main focus of this study is to understand the occupants’ behaviour in response to the thermal conditions of the educational buildings particularly in the warm and humid climatic zone of Madurai. Design/methodology/approach This research collected data through field studies. The data included 383 survey questionnaires from NV classrooms and 285 from AC classrooms, as well as on-site measurements of interior and exterior weather conditions. The TS results show that the students preferred well-designed NV classrooms than AC classrooms. A new adaptive comfort equation derived from this study can be applied to NV classrooms in warm and humid climates where mean outdoor temperature exceeds 40°C. Findings The neutral temperature derived for NV classrooms in Madurai ranged from 29°C to 34°C. Thus, the occupants in the NV classrooms of the higher learning educational institutions in the warm and humid climatic region of Madurai can adapt well to higher indoor temperature levels than predicted by ASHRAE comfort levels with minimum adjustments. Research limitations/implications The study was limited to only occupants in two premier higher learning technical educational institutions located in Madurai region within 5–10 km within the city limits to understand the implications of microclimate with respect to the urban context. Thus, further research is required to examine the tendency under local conditions in other regions beyond those applied to this study. Social implications The findings of this study showed that occupants in higher learning educational intuitions in Madurai prefer NV classrooms than AC classrooms. Therefore, with rising demands of energy use for mechanical ventilation and the associated high cost for running AC buildings, architects should prioritize the design of energy efficient buildings through the optimal use of passive design strategies for ventilation and thermal comfort. This study gives a base data for architects to understand the adaptive limitations of occupants and design NV buildings that can promote natural ventilation and provide better thermal environments that can help increase the productivity of students. Originality/value This paper was an attempt to develop the adaptive comfort model for NV classrooms in Madurai regions. There has been no attempt to identify the adaptive comfort levels of occupants in higher learning technical educational institutions located in warm and humid climatic region of India.

Rail Neutral Temperature Estimation Using Field Data, Numerical Models, and Machine Learning

Effective Rail Neutral Temperature (RNT) management is needed for continuous welded rail (CWR). RNT is the temperature at which the longitudinal stress of a rail is zero. Due to the lack of expansion joints, CWR develops internal tensile or compressive stresses when the rail temperature is below or above, respectively, the RNT. Mismanagement of RNT can lead to rail fracture or buckling when thermal stresses exceed the limits of rail steel. In this work, we propose an effective RNT estimation method structured around four hypotheses. The work leverages field-collected vibration test data, high-fidelity numerical models, and machine learning techniques. First, a contactless non-destructive and non-disruptive sensing technology was developed to collect real-world rail vibrational data. Second, the team established an instrumented field test site at a revenue-service line in the state of Illinois and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. Third, numerical models were developed to understand and predict rail vibration behavior under the influence of temperature and longitudinal load. Excellent agreement between model and experimental results were obtained using an optimization approach. Finally, a supervised machine learning algorithm was developed to estimate RNT using the field-collected rail vibration data. Sensitivity studies and error analyses were included in this work. The system performance with field data indicates that the proposed framework can support reasonable RNT estimation accuracy when measurement or model noise is low.

Modelling of an Existing Neutral Temperature District Heating Network: Detailed and Approximate Approaches

This paper deals with the modelling of an existing neutral-temperature district heating network, meaning that the distribution temperature is close to the ambient temperature, with decentralised heat pumps. The considered case is located in Ospitaletto, Italy. Heat sources are given by industrial waste heat at about 25 °C and aquifer wells at about 15 °C. Two models are used to analyse the network: a detailed model able to calculate local values of operating parameters and an approximate model focused on energy balances aggregating all users with a lumped demand. Both models include the behaviour of heat pumps, a feature not available in other district heating modelling tools. An entire year of operation is considered, with an hourly time resolution. Load profiles are provided as inputs, while the main outputs consist of energy balances and primary energy consumptions. The corresponding results are compared, showing a reasonable agreement, where the approximate model underestimates the overall electricity consumptions by about 15% with respect to the detailed model. On the other hand, the different information levels and execution times (the detailed model requires about 30 min to solve the considered network for a full year with hourly time steps, while the approximate model is almost immediate) make the two models suitable for different purposes, like the simulation of control solutions for the detailed one and scenario analysis for the other.