The application of supercritical carbon dioxide (sCO2) mixtures in power generation cycles could improve power block efficiency for concentrated solar power applications. Mixing CO2 with titanium tetrachloride (TiCl4), hexafluoro-benzene (C6F6), sulphur dioxide (SO2) or others increases the critical temperature of the working fluid, allowing it to condense at ambient temperatures in dry solar field locations. Therefore, transcritical power cycles, which have lower compression work and higher thermal efficiency compared to supercritical cycles, become feasible. This paper presents the flow path design of a utility-scale axial turbine operating with an 80-20% molar mix of CO2 and sulphur dioxide (SO2). A preliminary turbine design has been developed for the selected mixture using an in-house mean-line design code considering both mechanical and rotor dynamic criteria. Furthermore, 3D blades have been generated and blade shape optimisation has been carried out for the first and last turbine stages of the multi-stage design. It has been found that increasing the number of stages from 4 to 14 stages increase the total-to-total efficiency by 6.3%. The final turbine design has a total-to-total efficiency of 92.9% as predicted by the 3D numerical results with maximum stress less than 260 MPa and a mass flow rate within 1% of the intended cycle mass-flow rate. Optimum aerodynamic performance was achieved with a 14- stages design where the hub radius and the flow path length are 310 mm and 1800 mm respectively.

The utilisation of certain blends based on supercritical CO2 (sCO2), namely CO2/TiCl4, CO2/C6F6 and CO2/SO2, have been found to be promising for enhancing the performance of power cycles for Concentrated Solar Power (CSP) applications; allowing for up to a 6% enhancement in cycle efficiency with respect to a simple recuperated CO2 cycle, depending upon the nature of the used blend and the cycle configuration of choice. This paper presents an investigation of the impact of adopting these sCO2-based blends on the flow path design for a multi-stage axial turbine whilst accounting for aerodynamic, mechanical and rotordynamic considerations. This includes assessing the sensitivity of the turbine design to selected working fluid and imposed optimal cycle conditions. Ultimately, this study aims to provide the first indication that a high-efficiency turbine can be achieved for a large-scale axial turbine operating with these non-conventional working fluids and producing power in excess of 120 MW. To achieve this aim, mean-line aerodynamic design is integrated with mechanical and rotordynamic constraints, specified based on industrial experience, to ensure technically feasible solutions with maximum aerodynamic efficiency. Different turbine flow path designs have been produced for three sCO2 blends under different cycle boundary conditions. Specifically, flow paths have been obtained for optimal cycle configurations at five different molar fractions and two different turbine inlet pressure and temperature levels of 250 & 350 bar and 550 & 700◦C respectively. A total-to-total turbine efficiency in excess of 92% was achieved, which is considered promising for the future of CO2 plants. The highest efficiencies are achieved for designs with a large number of stages, corresponding to reduced hub diameters due to the need for a fixed synchronous rotational speed. The large number of stages is contrary to existing sCO2 turbine designs, but it is found that an increase from 4 to 14 stages can increase the efficiency by around 5%. Ultimately, based on the preliminary cost analysis results, the designs with a large number of stages showed to be financially feasible compared to the designs with a small number of stages.

In this paper, a modified loss breakdown approach is introduced for axial turbines operating with supercritical carbon dioxide (sCO2) mixtures using computational fluid dynamics (CFD) results. Loss breakdown analysis has been previously developed using two approaches, however each approach has its own uncertainties. The first approach neglects the effects of the cross-interaction between the different loss sources, while the second approach ignores the potential changes to the boundary layer thicknesses and the loss source domains. Although the second methodology accounts for the interactions between the different loss sources, it may produce less accurate predictions for compact machines like sCO2 turbines where the boundary layer may dominate the flow passage. The proposed methodology aims to obtain the turbine loss breakdown using a single CFD model where all sources of aerodynamic loss coexist, while considering variable loss regions defined based on the velocity and entropy distribution results. A steady state, single-stage, single-passage, 3D numerical model is set up to simulate the turbine and verify the loss audit methodology. The results are verified against the published loss audit methodologies for a 130 MW axial turbine operating with CO2/C6F6 blend, designed using an in-house mean line design code. The results show a good agreement between the proposed approach and the multiple-model approaches from the literature. However, the existing approaches appear to overestimate endwall losses by 13-16% and underestimate the profile losses by 11-31% compared to the proposed approach. Compared to mean line loss models, large differences in loss sources are observed from the CFD results, especially for the stator and rotor endwall losses which are found to be 3.2 and 1.6 times the CFD values, respectively. This helps to indicate limitations in existing mean line loss models.

A detailed loss assessment of an axial turbine stage operating with a supercritical carbon dioxide (sCO2) based mixture, namely titanium tetrachloride (CO2-TiCl4 85-15%), is presented. To assess aerodynamic losses, computational fluid dynamics (CFD) simulations are conducted using a geometry generated using mean-line design equations which is part of the work delivered to the SCARABEUS project [1]. The CFD simulations are 3D steady state and employ a number of turbulence models to investigate various aerodynamic loss mechanisms. Two categories of turbulence models are used: Eddy Viscosity and Reynold's Stress models (RSM). The Eddy Viscosity models are the k-?, k-? RNG, k-?, k-? SST and k-? Generalized while the RSM models are BSL, LRR, w-RSM and k-? EARSM. The comparison between different turbulence models showed minor deviations in mass-flow rate, power output and blade loading while significant deviations appear in the loss coefficients and the degree of reaction. It is noted that the k-? model gives the highest loss coefficients and the lowest isentropic efficiencies while most of the RSM models indicate higher efficiencies and lower loss coefficients. At off-design conditions a sensitivity study revealed that the k-? RNG model records the sharpest drop in the isentropic efficiency of 8.24% at low mass flowrate reaching 30% off-design. The efficiency sensitivity is found to be less for the other tested models getting 3.1% drop in efficiency for the LRR RSM model.

Thermal-power cycles operating with supercritical carbon dioxide (sCO2) could have a significant role in future power generation systems with applications including fossil fuel, nuclear power, concentrated-solar power, and waste-heat recovery. The use of sCO2 as a working fluid offers potential benefits including high thermal efficiencies using heat-source temperatures ranging between approximately 350∘C and 800∘C, a simple and compact physical footprint, and good operational flexibility, which could realise lower levelised costs of electricity compared to existing technologies. However, there remain technical challenges to overcome that relate to the design and operation of the turbomachinery components and heat exchangers, material selection considering the high operating temperatures and pressures, in addition to characterising the behaviour of supercritical CO2. Moreover, the sensitivity of the cycle to the ambient conditions, alongside the variable nature of heat availability in target applications, introduce challenges related to the optimal operation and control. The aim of this paper is to provide a review of the current state-of-the-art of sCO2 power generation systems, with a focus on technical and operational issues. Following an overview of the historical background and thermodynamic aspects, emphasis is placed on discussing the current research and development status in the areas of turbomachinery, heat exchangers, materials and control system design, with priority given to experimental prototypes. Developments and current challenges within the key application areas are summarised and future research trends are identified.

Supercritical CO2 (sCO2) power cycles have gained prominence for their expected excellent performance and compactness. Among their benefits, they may potentially reduce the cost of Concentrated Solar Power (CSP) plants. Because the critical temperature of CO2 is close to ambient temperatures in areas with good solar irradiation, dry cooling may penalise the efficiency of sCO2 power cycles in CSP plants. Recent research has investigated doping CO2 with different materials to increase its critical temperature, enhance its thermodynamic cycle performance, and adapt it to dry cooling in arid climates. This paper investigates the use of CO2/TiCl4, CO2/NOD (an unnamed Non-Organic Dopant), and CO2/C6F6 mixtures as working fluids in a transcritical Rankine cycle implemented in a 100 MWe power plant. Specific focus is given to the effect of dopant type and fraction on optimal cycle operating conditions and on key parameters that influence the expansion process. Thermodynamic modelling of a simple recuperated cycle is employed to identify the optimal turbine pressure ratio and recuperator effectiveness that achieve the highest cycle efficiency for each assumed dopant molar fraction. A turbine design model is then used to define the turbine geometry based on optimal cycle conditions. It was found that doping CO2 with any of the three dopants (TiCl4, NOD, or C6F6) increases the cycle's thermal efficiency. The greatest increase in efficiency is achieved with TiCl4 (up to 49.5%). The specific work, on the other hand, decreases with TiCl4 and C6F6, but increases with NOD. Moreover, unlike the other two dopants, NOD does not alleviate recuperator irreversibility. In terms of turbine design sensitivity, the addition of any of the three dopants increases the pressure ratio, temperature ratio, and expansion ratios across the turbine. The fluid's density at turbine inlet increases with all dopants as well. Conversely, the speed of sound at turbine inlet decreases with all dopants, yet higher Mach numbers are expected in CO2/C6F6 turbines.

Previous investigations suggest the power output from waste-heat recovery organic Rankine cycle (ORC) systems could be enhanced by up to 30% by operating with two-phase expansion, which could reduce cost and aid in the more widespread deployment of ORC technology. However, there are limited expander technologies suitable for such operation. The aim of this research is to investigate a novel ORC system that operates with wet-to-dry expansion permitting the use of a radial-inflow turbine. Specifically, through the exploitation of molecularly complex fluids the wet-to-dry expansion could be achieved across a single turbine stage, whilst the two-phase region is confined to the stator vane. Thermodynamic system optimisation is completed for potential fluids in which the degree of reaction is used to differentiate between the stator and rotor expansion processes. The results reveal that siloxanes are optimal fluids, and that for heat-source temperatures of 150, 200 and 250 °C the two-phase systems could generate up to 28%, 14% and 3% more power than single-phase systems, respectively. Following this, existing mean-line turbine methods are extended to two-phase turbines under the assumption of a two-phase homogeneous fluid under thermal equilibrium, which is supported with numerical simulations of a two-phase stator vane. The mean-line turbine optimisation for the 200 °C heat source is then conducted, with the optimal system corresponding to a turbine inlet vapour quality of 0.1 and degree of reaction of 0.4, with lower reaction leading to lower turbine efficiencies. More generally, feasible rotor geometries can be obtained, and conditions with the rotor are expected to remain subsonic. Whilst stator outlet Mach numbers range between 1.5 and 2.1, and stator throat widths below 1 mm are required, the CFD simulations indicate that wet-to-dry expansion can be successfully realised within the stator. In summary, these results provide the first positive demonstration that a 30% improvement in power output could be achieved with a two-phase ORC system operating with molecularly complex working fluids and a radial-inflow turbine.

Within this study, the blade shape of a large-scale axial turbine operating with sCO2 blended with dopants is optimized using an integrated aerodynamic-structural three-dimensional (3D) numerical model, whereby the optimization aims at maximizing the aerodynamic efficiency whilst meeting a set of stress constraints to ensure safe operation. Specifically, three candidate mixtures are considered, namely, CO2 blended with titanium tetrachloride (TiCl4), hexafluorobenzene (C6F6), or sulfur dioxide (SO2), where the selected blends and boundary conditions are defined by the EU project, SCARABEUS. A single passage axial turbine numerical model is setup and applied to the first stage of a large-scale multistage axial turbine design. The aerodynamic performance is simulated using a 3D steady-state viscous computational fluid dynamic (CFD) model while the blade stress distribution is obtained from a static structural finite element analysis simulation (FEA). A genetic algorithm is used to optimize parameters defining the blade angle and thickness distributions along the chord line while a surrogate model is used to provide fast and reliable model predictions during optimization using a genetic aggregation response surface. The uncertainty of the surrogate model, represented by the difference between the surrogate model results and the CFD/FEA model results, is evaluated using a set of verification points and is found to be less than 0.3% for aerodynamic efficiency and 1% for both the mass-flow rate and the maximum equivalent stresses. The comparison between the final optimized blade cross section has shown some common trends in optimizing the blade design by decreasing the stator and rotor trailing edge thickness, increasing the stator thickness near the trailing edge, and decreasing the rotor thickness near the trailing edge and decreasing the rotor outlet angle. Further investigations of the loss breakdown of the optimized and reference blade designs are presented to highlight the role of the optimization process in reducing aerodynamic losses. It has been noted that the performance improvement achieved through shape optimization is mainly due to decreasing the endwall losses with both the stator and rotor passages.

The successful and economic conversion of waste heat into electricity requires new, innovative, power cycles to be developed. The proposed wet-to-dry cycle, in which the working fluid transitions across the saturation dome during expansion, has been shown to offer significant thermodynamic benefits. However, the feasibility of achieving wet-to-dry expansion when non-equilibrium effects, which are expected during a high-speed two-phase expansion, are taken into account has not been previously established. This paper first introduces a simple method to assess the thermodynamic potential of a fluid for the wet-to-dry cycle, which confirms that the siloxanes MM and MDM are excellent contenders and under ideal conditions can achieve second law efficiencies approaching 90% whilst operating with heat-source temperatures around 200 ∘C. The second part of this paper presents a one-dimensional nozzle design tool that accounts for thermal and mechanical non-equilibrium effects, which has been verified against previous studies and non-equilibrium computational-fluid dynamic simulations of the two-phase expansion of the refrigerant R1233zd(E). The model is then applied to the wet-to-dry expansion of MM under operating conditions directly relevant for the wet-to-dry cycle. The results firstly indicate the importance of accounting for non-equilibrium effects when designing nozzles for wet-to-dry expansion and the importance of having a realistic model that accounts for the break-up of droplets during the expansion. More importantly, the results reveal that for eight of the twelve cases considered it is still possible to achieve the full vapourisation of the working fluid within the nozzle when non-equilibrium effects are considered. This confirms the potential of the wet-to-dry cycle to enhance the performance of waste-heat recovery systems, which, in turn, necessitates further investigation, including experimental investigation, to further explore the concept.

Loss models are used to evaluate the aerodynamic performance of axial turbines at the preliminary design stage. The commonly used loss models were derived for air and steam turbines and have not been sufficiently investigated for turbines working with non-conventional working fluids, relevant to new power systems, such as organic fluids and supercritical CO2 (sCO2). Thus, the aim of this study is to explore the deviation between the performance predictions of different loss models, namely Dunham and Came, Kacker and Okapuu, Craig and Cox and Aungier, for non-conventional working fluids where turbines may differ in design and operation than conventional air or steam turbines. Additionally, this paper aims to investigate the effect of the turbine scale on the trends in the performance predictions of these models. Three different case-studies are defined for air, organic Rankine cycle (ORC) and sCO2 turbines and each one is evaluated at two different scales. It is found that the selected loss models resulted in varying loss predictions; particularly for predicting the losses due to the clearance gap for all small scale designs. Furthermore, large variations were found in predicting the effect of the flow regime on the turbine performance for all models.

Two-phase expansion has previously been considered as a means to improve the performance of ORC systems for waste-heat recovery applications. However, ORC turbomachinery has almost exclusively been designed for operation with either superheated or saturated vapour, whilst during experimental testing turbine operation close the saturation region is generally avoided. However, for high temperature ORC systems, characterised by high molecular weight fluids, high expansion ratios and supersonic flows, it is postulated that a degree of wetness at the turbine inlet could be accommodated. This raises the question of whether an existing ORC turbine could be operated under two-phase inlet conditions, and whether two-phase expansion could be realised with existing ORC turbomachinery. This work presents an investigation of the performance of a supersonic stator vane, initially designed for superheated expansion, under two-phase inlet conditions. The stator geometry evaluated is installed on the ORC test rig at Lappeenranta University of Technology and was designed for the expansion of MDM from turbine inlet conditions of 265 °C and 8 bar to a Mach number of 2.4. A one-dimensional isentropic analysis is first conducted under the assumption of homogeneous flow to predict the variation in pressure and velocity if the existing area distribution is mapped onto two-phase inlet conditions with the same inlet pressure. The results indicate that for inlet vapour qualities above 0.65 the two-phase region is confined to upstream of the throat. This investigation is followed by a two-dimensional CFD simulation of the stator vane with inlet vapour qualities of 0.85, 0.65 and 0.45. As an initial assumption the two-phase mixture is modelled as a homogeneous binary mixture, and non-equilibrium effects are neglected. The CFD results are consistent with the one-dimensional predictions, indicating that for vapour qualities of 0.85 and 0.65 the two-phase region is confined to upstream of the throat, whilst for a vapour quality of 0.45 the transition to superheated vapour occurs in very close proximity to the throat. In all cases, there is no significant shift in the velocity triangles at stator outlet, indicating similar rotor performance could be expected. Finally, the validity of the equilibrium assumption is discussed.

Within this study, the blade shape of a large-scale axial turbine operating with sCO2 blended with dopants is optimised using an integrated aerodynamic-structural 3D numerical model, whereby the optimisation aims at maximising the aerodynamic efficiency whilst meeting a set of stress constraints to ensure safe operation. Specifically, three candidate mixtures are considered, namely CO2 blended with titaniumtetrachloride (TiCl4), hexafluorobenzene (C6F6) or sulfur dioxide (SO2), where the selected blends and boundary conditions are defined by the EU project, SCARABEUS. A single passage axial turbine numerical model is setup and applied to the first stage of a large-scale multi-stage axial turbine design. The aerodynamic performance is simulated using a 3D steady-state viscous computational fluid dynamic (CFD) model while the blade stress distribution is obtained from a static structural finite element analysis (FEA). A genetic algorithm is used to optimise parameters defining the blade angle and thickness distributions along the chord line while a surrogate model is used to provide fast and reliable model predictions during optimisation using genetic aggregation response surface. The uncertainty of the surrogate model represented by the difference between the surrogate model results and the CFD/FEA model results is evaluated using a set of verification points and found to be less than 0.3% for aerodynamic efficiency and 1% for both the mass flow rate and the maximum equivalent stresses. The comparison between the final optimised blade cross-sections have shown some common trends in optimising the blade design by decreasing stator and rotor trailing edge thickness, increasing stator thickness near the trailing edge, decreasing rotor thickness near the trailing edge and decreasing the rotor outlet angle. Further investigations of the loss breakdown of the optimised and reference blade designs are presented to highlight the role of the optimisation process in reducing aerodynamic losses. It has been noted that the performance improvement achieved through shape optimisation is mainly due to decreasing the endwall losses of both stator and rotor blades.

Artificial Intelligence (AI) and Blockchain Technology (BCT) are considered two of the most trending and disruptive technologies. BCT, although commonly associated with cryptocurrencies, has shown a tremendous impact among many other distributed applications domains. BCT characteristics, such as the distribution of data storage among independent nodes and the use of consensus algorithms offering immutability and transparency, remove the need for a central authority making BCT a trustful technology. However, currently decision-makers and stakeholders lack the confidence to overcome the perception of risk and uncertainty related to AI technology. This lack of trust is crucial in accepting the deployment of AI Technology in wider application domains, such as the recruitment process. Further, current research literature does not adequately investigate the role of trust and how it can be implemented as an integral part of a AI recruitment based application. Therefore, the aim of this paper is to investigate how emerging BCT and AI technologies can improve decision making and stakeholder trust in a job recruitment system that is traditionally focused on human expert decision making. In this paper we propose the design of a new solution for trusting AI in recruitment applications through the use of Blockchain Smart Contracts (TAIRA-BSC). TAIRA-BSC integrates Blockchain Smart Contracts (BSC) with the Data Lake (DL), Machine Learning (ML) and AI technologies in our AI Recruitment Model (AIRM) architecture. TAIRA-DSC improves transparency and interoperability in the recruitment process while protecting sensitive job candidate data and ensures data integrity delivery and traceability in the recruiting process through a verifiable decentralised ledger, i.e. the blockchain and associated smart contracts. The research work presented in this paper also provides an architectural proof of concept demonstrating a novel approach to implementing an AI based job recruitment application integrating BCT to provide trust and traceability in the recruitment process for both decision makers (e.g. job recruitment agency) and other stakeholders (e.g. job seekers). The paper presents a state-of-the-art on discussion on integrating AI with BCT focusing on how BCT can be used to bridge trust concerns with AI systems. We also present a conceptual architecture (TAIRA-BSC) developed to serve as a foundation for future studies focused on enhancing trust in AI applications through the integration of BCT.

Data security has become crucial to most enterprise and government applications due to the increasing amount of data generated, collected, and analyzed. Many algorithms have been developed to secure data storage and transmission. However, most existing solutions require multi-round functions to prevent differential and linear attacks. This results in longer execution times and greater memory consumption, which are not suitable for large datasets or delay-sensitive systems. To address these issues, this work proposes a novel algorithm that uses, on one hand, the reflection property of a balanced binary search tree data structure to minimize the overhead, and on the other hand, a dynamic offset to achieve a high security level. The performance and security of the proposed algorithm were compared to Advanced Encryption Standard and Data Encryption Standard symmetric encryption algorithms. The proposed algorithm achieved the lowest running time with comparable memory usage and satisfied the avalanche effect criterion with 50.1%. Furthermore, the randomness of the dynamic offset passed a series of National Institute of Standards and Technology (NIST) statistical tests.

Cloud computing has become essential for IT resources that can be delivered as a service over the Internet. Many e-government services that are used worldwide provide communities with relatively complex applications and services. Governments are still facing many challenges in their implementation of e-government services in general, including Saudi Arabia, such as poor IT infrastructure, lack of finance, and insufficient data security. This research paper investigates the challenges of e-government cloud service models in developing countries. This paper finds that governments in developing countries are influenced by how the top management deals with the attention to the adoption of cloud computing. Further, organisational readiness levels of technologies, such as IT infrastructure, internet availability and social trust of the adoption of new technology as cloud computing, still present limitations for e-government cloud services adoption. Based on the findings of the critical review, this paper identifies the issues and challenges affecting the adoption of cloud computing in e- government such as IT infrastructure, internet availability, and trust adopted new technologies thereby highlighting benefits of cloud computing-based e-government services. Furthermore, we propose recommendations for developing IT systems focused on trust when adopting cloud computing in e-government services (CCEGov).

Mobile augmented reality has become an influential tool for digital content representation in terms of enhancing users’ experience and improving the adaptability and
usability of augmented reality applications. In our research, we have developed a service oriented mobile AR architecture for multiple applications, such as a museum interactive or web app. Our solution enhances closed platform mobile AR applications to create more flexible mobile AR clients that efficiently support content acquisition and utilization of third party digital media contents on a real scene. Our example web service framework on a mobile AR client exploits specific museum (e.g. Victoria and Albert Museum) or third party APIs (e.g. Google Maps) to aggregate data from participating web service providers. A typical media API content request is sent to a content provider to obtain a targeted cultural object’s associated media contents such
as 3D models, images, text, videos and metadata. Acquired
contents are then visualized in both VR and AR environments and consumed by mobile users. Other examples of supporting modules include photogrammetry based 3D reconstructions based on available commercial or open source web services and personalization that allow a user to request rich media, e.g. 3D models, and associated metadata, of a targeted cultural object for exploiting in a ‘saved museum exhibition’.

This paper describes the design of our service-oriented architecture to support mobile multiple object tracking augmented reality applications applied to education and learning scenarios. The architecture is composed of a mobile multiple object tracking augmented reality client, a web service framework, and dynamic content providers. Tracking of multiple real objects and retrieval of associated multiple media contents allows more complex augmented reality learning scenarios to be constructed that could improve students’ knowledge and learning strategies on a mobile platform. It also allows students to create their own augmented reality learning environments and select preferences from acquired digital contents based on multiple object real scenes. Mobile users are able to request contextual digital contents from web service providers to augment these multiple objects in the real world. The digital contents are generally dynamically acquired digital media, e.g. 3D models, images, textual descriptive data, metadata, multimedia or even social media data. New digital contents for augmenting the real world are acquired through a service-oriented approach by accessing any appropriate web services to deliver that content to the augmented learning environment.