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Thermoacoustic technology emerges as a sustainable and low-carbon method for energy conversion, leveraging environmentally friendly working mediums and independence from electricity. This study presents the development of a multimode heat-driven thermoacoustic system designed to utilize medium/low-grade heat sources for room-temperature cooling and heating. We constructed both a simulation model and an experimental prototype for a single-unit direct-coupled thermoacoustic system, exploring its performance in heating-only, cooling-only, and hybrid heating and cooling modes. Internal characteristic analysis including an examination of internal exergy loss and a distribution analysis of key parameters was first conducted in the hybrid cooling and heating mode. The results indicated a positive-focused traveling-wave-dominant acoustic field within the thermoacoustic core unit, enhancing energy conversion efficiency. The output system performance was subsequently tested under different working conditions in the heating-only and cooling-only modes. A maximum output heating power of 2.3 kW and a maximum COPh of 1.41 were observed in the heating-only mode. Meanwhile, a cooling power of 748 W and a COPc of 0.4 were obtained in the typical cooling condition at 7 °C when operating in cooling-only mode. These findings underscore the promising potential of thermoacoustic systems for efficiently utilizing medium/low-grade heat sources for cooling and/or heating applications in the future.

Engineered cementitious composites (ECCs) are special types of fibre-reinforced cementitious composites (FRCC) with higher strain capacity which can be achieved with low fibre volume as low as 2% and total elimination of coarse aggregates. Due to the outstanding performance of ECCs, they are suitable for various construction and repair applications. However, in order for ECCs to achieve their properties; a high amount of binder which is primarily composed of Portland cement (PC) is used alongside a special type of ultrafine silica sand (USS) which is different from the conventional natural fine aggregates. The production of PC is known to be detrimental to the environment due to its high carbon dioxide emissions coupled with the high consumption of natural resources. Thus, the high use of PC content in ECCs posed a sustainability threat. Similarly, the USS used in ECCs are not readily available everywhere and are expensive. The processing of the USS coupled with its transportation over long distances would also increase the cost and embodied carbon of ECCs. Hence, in order to promote more development and applications of ECCs for various applications; this dissertation aims to provide innovative ways to improve the sustainability of ECCs and their performances. This dissertation offers four solutions to improve the sustainability of ECCs which are (i) use of unconventional industrial by-products as partial replacement of PC (ii) total replacement of PC in ECCs with alternative sustainable binders (iii) replacement of USS in ECCs with recycled materials and (iv) the use of supplementary cementitious materials to replace a high volume of PC. The findings from this study revealed sustainable ECCs with acceptable mechanical and durability performance can be achieved with the use of alternative binders or replacement of the conventional USS used in ECC mixtures. The sustainability and cost assessment of the ECCs indicated that the incorporation of industrial by-products such as blast furnace slag (BFS) especially at higher content is beneficial to reducing the negative environmental impact and economic burden associated with ECCs compared to the conventional ECC. The sustainability index and cost index of the ECCs further showed that the use of BFS is more beneficial when the sustainability and cost of the ECCs are compared with the corresponding performance. Similarly, the use of recycled materials as an alternative to USS was found to result in a significant reduction in the embodied carbon and cost of ECCs. The use of recycled materials such as expanded glass (EG) as aggregates in ECCs was also found to improve the thermal insulation properties of ECCs making such ECC suitable for the production of building envelope elements.

This paper proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.

An important limitation of polymer electrolyte fuel cell technology is the low mechanical strength and dimensional instability with changes of water content of proton exchange membranes (PEMs). A range of different approaches to more stable PEMs based on Nafion have been studied of which composite materials of Nafion with mechanically stronger polymers is one of the most promising directions. If successful, they will lead to thinner composite PEMs with enhanced fuel cell performance, life span, and cost-effectiveness. Developed in this thesis are electrospinning conditions for the fabrication of electrospun mats for potential application in PEMs. Polysulfone (PSU), poly vinylidene fluoride-co-hexafluoropropylene (PVDF-HFP), and polyvinylidene fluoride (PVDF) were tested as mechanically stronger but inert (minimal contribution to proton transport) polymers that can tolerate the fuel cell condition. PVDF-HFP generated defect free electrospun mats over a wide range of spinning conditions, while PSU required very specific conditions and no successful conditions were found for PVDF mostly due to over-wetting. These mats might function as mechanical support and could be tested as PEMs when filled with Nafion, but the complete filling of electrospun mats with Nafion has been proven difficult. Instead, the electrospinning of Nafion was tested to explore options of electrospinning mixed mats of two different polymers and co-electrospinning of core-sheath fibers. Two commercial Nafion solutions D520 and D2020 with 5 wt% and 20 wt% content of Nafion were electrospun together with polyethylene oxide of two different molecular weights as a carrier polymer. Mats of sufficient quality for PEM tests were obtained with solutions based on 20 wt% content of Nafion, a low flow rate of 0.2 mL/h, and the lower molecular weight polyethylene oxide as the carrier. Finally, coaxial electrospinning conditions for the formation of core-sheath fibers were developed for Nafion as sheath material and PVDF-HFP or PSU as the core material. Defect-free, core-sheath fibers were generated when the concentration of both solutions was high (20 wt%), the Nafion flow rate was about 0.2 mL/h for the sheath, and the core flow rate was below the flow rate of the sheath (0.1-0.15 mL/h for PVDF-HFP and 0.15 mL/h for PSU). Mats of these core-sheath fibers should provide good mechanical strength combined with much better compatibility with Nafion. A straightforward pore filling with Nafion solutions is expected and their investigation as PEMs in fuel cells is planned as future work.

The accurate training of a wind power forecasting (WPF) model for a newly built wind farm is difficult because of limited historical data. This study established a multitask learning architecture wherein the WPF in different wind farms represents an independent task. Subsequently, a novel short-term WPF model based on a multitask learning architecture was proposed. In this model, a multitask Gaussian process is used to capture the intertask conjunction, which contributed to the training of each task. The proposed methodological framework employs dependencies from other tasks wherein older wind farms contain substantial historical data to enhance the performance of tasks in which there is a newly built wind farm. Several numerical experiments were conducted using datasets from seven independent wind farms in Australia. The results show that the proposed scheme not only obtains improved point forecasting results but also produces better probabilistic forecasting results, thus demonstrating the superiority of the proposed method.

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Abstract The aim of this study is to evaluate the incremental innovation performance of nuclear energy projects. Within this context, a novel model is generated which consists of two different stages, and large nuclear reactors are taken into consideration. Firstly, the Pythagorean fuzzy DEMATEL is used to weight the phases of technology S-Curve for nuclear energy projects. Moreover, the second stage includes the ranking two-generation technology S-curve with integer patterns for nuclear energy projects. In this framework, the best combinations are selected for innovation life cycle pattern with the integer code series. The findings demonstrate that the nuclear energy companies need to consider the two-generation technology S-Curve because continuous technological developments are occurring for nuclear power generation. It is also determined that aging in the first generation is the most significant period of two-generation technology S-Curve for nuclear energy projects. In this process, critical decisions should be made regarding future technological investments. In addition, the growth phase in the second generation is also important for the effectiveness of the nuclear energy technology. Conducting effective evaluations in these processes will contribute to increasing the efficiency of companies.

The aim of this study is to define strategies to increase green nuclear energy investments. Within this context, a model is suggested that includes two different stages. Firstly, the consensus-based fuzzy preferences are determined for these investments. For this purpose, four different TRIZ-based strategies are identified, which have an influence on the effectiveness of these projects. Furthermore, in the second stage, the significance weights of these strategies are calculated by considering the spherical fuzzy DEMATEL approach. The findings indicate that dynamicity is the most appropriate TRIZ strategy for the effectiveness of green nuclear energy investments. In addition, the prior action is another significant strategy that should also be considered in these projects. It is obvious that sufficient technological infrastructure is very important for the performance of green nuclear power plants and there is a rapid development of this technology. Because of this situation, these nuclear power plants should be designed effectively so that they can be adoptable to any changes in the technology. In this scope, qualified personnel should be employed, and communication between departments should be effective and of high quality. With the help of these issues, technological changes can be adopted more easily.