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Summary Extraheavy-oil (XHO) reservoirs in South America represent some of the largest hydrocarbon accumulations (>500 billion bbl) in the world. Primary production (PP) that uses long horizontal wells is a commercially proved technology for XHO reservoirs. The expected ultimate recovery with primary production is generally less than 12% of original oil in place (OOIP), and thermal enhanced oil recovery (EOR) is critical for increasing recovery to 30–60% OOIP. Economic and environmentally viable thermal development of these reservoirs will require the use of horizontal steam injectors. Our results reveal that continuous steam injection (CSI) with a horizontal injector placed vertically above a horizontal producer (CSI-HIHP) is a very effective method for XHO reservoirs, with high peak-oil rate and significantly high recovery. This study, the first of its kind for an XHO reservoir, outlines an integrated work flow to evaluate the production potential of a large XHO greenfield with PP followed by thermal exploitation. The work flow, based on a probabilistic framework [involving design of experiment (DOE), proxy methods, and Monte Carlo simulations], evaluates reservoir performance for the whole life cycle of the field under a range of uncertainties, and quantifies the impact of key parameters affecting the reservoir performance. XHO reservoirs usually have significantly higher pressures than typical conventional heavy-oil reservoirs, where CSI has been applied commercially. Therefore, pressure in these reservoirs must be reduced before CSI can begin. Cyclic steam stimulation (CSS) after the initial stage of PP can be used to accelerate pressure reduction in the reservoir, while providing additional recovery. Our results demonstrate that geological features such as shale baffles have a significant impact on delaying pressure reduction during PP and CSS. Under a broad range of conditions investigated in this study, PP for 1 year followed by CSS for 4 years has been found to be successful in reducing pressure to the target pressure for CSI. High pressure drop in the horizontal steam injector can cause pressure near the toe region of the injector to be lower than the producer pressure. This results in poor steam injection and poor steam-chest development in that region, thus greatly reducing the efficiency of the thermal-recovery process. We quantify pressure drop in a horizontal steam injector and its impact on the thermal performance and suggest a novel well configuration that uses two injectors for every long producer during CSI. The proposed configuration with a sequential development plan can significantly improve economics of the projects. A novel probabilistic work flow for a full-field (FF) development plan (PP, CSS, and CSI) of XHO reservoirs provides robust production forecast during the entire life cycle. The work flow developed and the insights obtained would be very valuable in preparing effective exploitation plans and optimal facility design, a key economic variable in large projects of developing giant XHO reservoirs.

This paper evaluates the performance of a recently patented rotationally asymmetrical dielectric totally internally reflective concentrator (RADTIRC) under diffuse radiation. The RADTIRC has a geometrical concentration gain of 4.969 and two half acceptance angles of ± 30° and ± 40° along the x-axis and z-axis respectively. Simulation and experimental work have been carried out to determine the optical concentration gain under diffuse radiation. It was found that the RADTIRC has an optical concentration gain of 1.94 under diffuse irradiance. The experimental results for the single concentrator showed an optoelectronic gain of 2.13, giving a difference of 9.8% due to factors such as the presence of direct radiation during experiments, the increase in diffuse radiation due to the reflection from surrounded buildings as well as from the ground reflection.

The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the system, the facilities, and their personnel. This paper presents an innovative and reliable methodology for developing such applications, ensuring safety and efficiency throughout the process. It involves a staged transition from simulation to reality, wherein various components are validated at each stage. This iterative approach facilitates error identification and resolution, enabling subsequent real flights to be conducted with enhanced safety after validating the remainder of the system. Furthermore, this article showcases two use cases: wind turbine inspection and photovoltaic plant inspection. By implementing the suggested methodology, these applications were successfully developed in an efficient and secure manner.

Abstract The article introduces the process of deep energy retrofit carried out on a residential building in the UK, using a ‘TCosy’ approach in which the existing building is completely surrounded by a new thermal envelope. It reports on the entire process, from establishing the characteristics of the existing building, carrying out design simulations, documenting the off- site manufacture and on-site installation, and carrying out instrumental monitoring, occupant studies and performance evaluation. Multi-objective optimisation is used throughout the process, for establishing the characteristics of the building before the retrofit, conducting the design simulations, and evaluating the success of the completed retrofit. Building physics parameters before and after retrofit are evaluated in an innovative way through simulation of dynamic heating tests with calibrated models, and the method can be used as quality control measure in future retrofit programmes. New insights are provided into retrofit economics in the context of occupants’ health and wellbeing improvements. The wide scope of the lessons learnt can be instrumental in the creation of continuing professional development programmes, university courses, and public education that raises awareness and demand. These lessons can also be valuable for development of new funding schemes that address the outstanding challenges and the need for updating technical reference material, informing policy and building regulations.

Abstract Subcooled flow boiling is frequently encountered in hot assemblies in nuclear power plant. Considerable efforts have been made to improve the understanding of boiling process and prediction of critical heat flux (CHF). In this study, the effects of non-uniform heat input on heat transfer characteristics are further investigated, so that more realistic descriptions of practical systems can be achieved vis-a-vis usual heat input assumptions. A CFD model has been built to predict subcooled flow boiling process in a vertical round pipe. The capability of the model is validated against experimental results in the publication. A variety of non-uniform heat input profiles are tested using this validated CFD model. Results show that a regular triangle-shape heat input profile will cause stronger vapour generation in the middle of the heating region. Also, the risk of reaching CHF may be increased with the position of maximum heat flux moving downstream.

The decarbonisation of heating in the United Kingdom is likely to entail both the mass adoption of heat pumps and widespread development of district heating infrastructure. Estimation of the spatially disaggregated heat demand is needed for both electrical distribution network with electrified heating and for the development of district heating. The temporal variation of heat demand is important when considering the operation of district heating, thermal energy storage and electrical grid storage. The difference between the national and urban heat demands profiles will vary due to the type and occupancy of buildings leading to temporal variations which have not been widely surveyed. This paper develops a high-resolution spatiotemporal heat load model for Great Britain (GB: England, Scotland a Wales) by identifying the appropriate datasets, archetype segmentation and characterisation for the domestic and nondomestic building stock. This is applied to a thermal model and calibrated on the local scale using gas consumption statistics. The annual GB heat demand was in close agreement with other estimates and the peak demand was 219 GWth. The urban heat demand was found to have a lower peak to trough ratio than the average national demand profile. This will have important implications for the uptake of heating technologies and design of district heating.

Abstract The determination of extremely low probability of the reactor pressure vessel and the primary main coolant pipe fracture is one of the prerequisites for further operation of the first generation NPPs in Czechoslovakia. The project for this task includes experimental and analytical work on the reactor pressure vessel and on the main piping components. Review of the project and preliminary results are presented. The results show extremely low probability of failure. Future work is focussed on annealing studies and tests for the pressure vessel and the application of the Leak-Before-Break technology on primary piping circuit.

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.