Research Articles (Mechanical and Aeronautical Engineering)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1705

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Wind effects on Tundra vegetation : insights from natural windbreaks in the Sub-Antarctic
Marneweck, Nicola; De Kock, Leandri; Schoombie, Janine; Greve, Michelle; Le Roux, Peter Christiaan (Wiley, 2026-08-17)
QUESTIONS : Wind is an understudied component of climate, with relatively few studies quantifying the effect of wind exposure on natural vegetation. Wind can, however, alter leaf microclimate, reduce soil water availability and physically damage plants, thereby influencing the growth of individual plants, with its effects potentially scaling up to affect plant community characteristics. The paucity of research assessing the relationship between wind conditions and plant communities' taxonomic and functional composition is a key gap in our understanding of environment–vegetation relationships in windy environments. LOCATION : Sub-Antarctic Marion Island. METHODS : We test whether vegetation characteristics, including vascular plant species richness, composition, cover and functional traits, differ between the windward and sheltered sides of natural windbreaks. RESULTS : Wind exposure did not strongly affect vascular plant richness, cover or composition, with larger differences in these responses occurring between sampling sites than between windward and sheltered microsites. Wind most strongly affected maximum plant height (both for individual species and for the community), with significantly shorter-statured plants observed in wind-exposed microsites. In contrast, most species' leaf traits were unaffected by wind exposure, with only 21% of species–trait combinations differing significantly between microsites. In those cases, plants experiencing greater wind exposure had lower leaf area, specific leaf area or leaf dry matter content, with changes in leaf thickness varying among species. CONCLUSIONS : Wind exposure did not strongly influence vascular plant community characteristics on Marion Island, apart from restricting vegetation height. Wind did, however, influence the occurrence and traits of some individual species, generally enhancing species avoidance or resistance to wind where its effects were significant. Broadly, these results indicate that wind can significantly influence plant size and some aspects of morphology, thereby reinforcing the need to explicitly consider wind as a potentially important climate variable, especially in habitats where wind is a prominent environmental factor and in areas where wind speeds are predicted to change.
Exploring the role of Whatsapp in facilitating communication and support in academic settings
Basitere, Moses; Ivala, Eunice N.; Mogashana, Disaapele (Taylor and Francis, 2025-05-30)
WhatsApp is extensively employed in academic settings for collaboration and information exchange; however, its potential for simultaneous academic and psychosocial support remains underexplored. This article draws from a study which investigated students’ experiences with WhatsApp as an informal learning and psychosocial support tool in chemical engineering undergraduate studies. Small WhatsApp groups, facilitated by the life coach in the first year, provided psychosocial support which was deemed necessary for helping students transition from high school to university. Over four years, students continued participating in course WhatsApp groups facilitating student–student engagement, communication with lecturers, and support from a life coach if needed. Qualitative data from 10 semi-structured interviews revealed the development of a supportive community of practice which enabled collaboration, communication, and empathy among students, lecturers, and the life coach on life and academic matters. However, some students experienced negative consequences, suggesting a need for further exploration of WhatsApp’s impact. This article advocates for continued research on WhatsApp’s role in informal learning, self-directed learning, and psychosocial support, particularly in large class settings.
Evaluating follower wing performance in formation flight using the power balance method
Raubenheimer, Ryan; Smith, Lelanie; Sanders, Drewan S. (American Institute of Aeronautics and Astronautics, 2026)
Formation flight is used to reduce the drag of the follower and increase range and endurance. However, quantifying how much of the potential energy in the leader’s wake is available to, and used by, the follower is poorly understood. This paper explores how the power balance method (PBM) may be applied in the context of formation flight to evaluate not only classical measures of performance, such as the spanwise efficiency factor, but also to quantify what proportion of the potential energy in the leader’s wake is recovered as drag savings. A finite NACA0012 wing in subsonic flow is modeled downstream of an identical leader wing. The spanwise efficiency factor is compared to the PBM at the far field in predicting induced drag savings: the former predicts induced drag savings of 37.0–61.0%, while the latter predicts 41.5–63.0%. The PBM shows that, for the inviscid case, approximately 73.3% of the wake potential energy was recovered. This demonstrates that the power balance method agrees with traditional momentum-based analyses of performance and can also be used to gauge the efficiency of extracting potential energy from the flowfield. Future work is recommended to separate induced drag from profile drag in the follower’s far field in the viscous regime.
Aerodynamic limits of gliding flight in grey-headed albatrosses under variable wind conditions
Schoombie, Janine; Craig, K.J. (Kenneth); Smith, Lelanie (Company of Biologists, 2026-08-04)
Adult grey-headed albatrosses breeding on Marion Island experience highly variable near-ground wind vectors that can result in crash landings, some of which are fatal. This study quantifies the combinations of airspeed and wind direction that can lead to loss of lift or the generation of downforce sufficient to cause such crashes. Using a previously developed three-dimensional grey-headed albatross body geometry, we conducted numerical simulations of this rigid geometry across a wide range of flight conditions defined by airspeed, angle of attack, and sideslip angle. Lift and aerodynamic efficiency (lift-to-drag ratio) are then evaluated to identify conditions under which insufficient lift is produced. Simulations show that for airspeeds below 10 m·s⁻¹, the generated lift is lower than the average weight of an adult grey-headed albatross, with peak aerodynamic efficiency occurring at an angle of attack of approximately 5°. While the geometry generates lift effectively under either strong crosswinds or downdrafts alone, their combination can produce substantial downforce. Given that albatrosses preferentially exploit crosswinds at the meso-scale, transient gusts combining crosswind and downdraft components may force birds into the ground, particularly during low-altitude nest departure, increasing the likelihood of fatal crash landings.
Experimental characterization and predictive modelling of Al2O3/MWCNT hybrid nanofluid thermophysical properties using ANN, ANFIS, FCM and hybrid techniques
Momin, Modaser Hamid Morahed; Atofarati, Emmanuel O.; Oladipo, Stephen; Adogbeji, Victor O.; Ajuka, Luke; Giwa, Solomon O.; Sharifpur, Mohsen; Meyer, Josua P. (Springer, 2026-06-26)
Please read abstract in the article.
I am because we are’ : unfolding role identity and tensions of South African middle leaders in engineering as change agents
Jiang, Dan; Smith, Lelanie; Hattingh, Teresa; Guerra, Aida; Du, Xiangyun (Routledge, 2026)
This paper explores how role identity enables or constrains academic middle leaders in engineering as change agents within South African higher education. Framed by complexity theory, the study draws on the Dynamic Systems Model of Role Identity (DSMRI), which conceptualizes role identity as a complex, dynamic system shaped by the interaction of internal and external factors. Data were collected from eleven academic engineering middle leaders during a pedagogical development programme in Denmark. Using multiple qualitative sources, the study examines how participants’ role identities are constructed through their self-perceptions, epistemological beliefs, goals, emotions, and perceived action possibilities, across student, teacher, curriculum, institutional, and national-levels. Misalignments across these domains give rise to role tensions, including resistance to change, limited authority, resource constraints, and broader socio-economic challenges. The findings contribute a context-sensitive reading of DSMRI by incorporating a collective, relational lens that foregrounds community, mutual accountability, and shared purpose. This shifts the emphasis from individual agency to collaborative leadership and interconnected identity formation. The study concludes that enabling sustained educational change requires strategic support for middle leadership, investment in professional learning communities, and long-term institutional commitment to building capacity for change across multiple system levels.
WIce-FOAM 1.0 : coupled dynamic and thermodynamic modelling of heterogeneous sea ice and waves using OpenFOAM-v2306
Marquart, Rutger; Alberello, Alberto; Bogaers, Alfred Edward Jules; De Santi, Francesca; Vichi, Marcello (Copernicus Publications, 2025-12-15)
We present WIce-FOAM 1.0, a numerical model built on OpenFOAM that couples the dynamics and thermodynamics of heterogeneous sea ice to analyse waves' response in marginal ice zone regions composed of consolidated ice floes and interstitial grease ice. The model represents prototypical conditions on the 5 km scale, where each 10 m grid cell classified as ice floe or grease ice may contain both ice types, but are predominantly occupied by one. Our model aims to study the mean shear viscosity of heterogeneous sea ice to bridge the gap with larger-scale ocean-sea ice models in which sub-grid details and wave effects are neglected. We tested the model in the Southern Ocean using a realistic sea-ice field from a SAR satellite image and complemented our analysis by idealised simulations. The thermodynamic model was coupled online to optimize the stiffness of the process scales and to explicitly account for the distinct characteristics of different ice types. We first investigated the dynamic response of sea ice to one-way wave forcing across a range of wave periods and directions. The results show that the domain-averaged sea-ice viscosity is scale invariant from approximately 800 m to 5 km and is primarily governed by the relative proportion of ice floes to grease ice, with less sensitivity to wave periods and directions. While the wave direction affects the local strain rate and viscosity, and the presence and orientation of narrow connections between the larger ice floes significantly influence the mean viscosity, these effects do not break the observed scale invariance. Finally, we demonstrate that, despite the different time scales, the mean viscosity responds nonlinearly to the inclusion of thermodynamic sea-ice growth. This model represents a first step towards a mechanistic understanding and description of heterogeneous sea ice, which is common in the Antarctic and is increasing in the warming Arctic. It can be used to design field experiments and to derive parametrisations of waves-in-ice response for large-scale sea-ice models.
Flow characteristics and thermal features of inline butterfly wings-shaped protruded surface in a rectangular wind channel
Shote, Adeola Suhud; Aasa, Samson Abiodun; Olorunlana, M.A.; Adelaja, A.D. (Elsevier, 2026-04)
As devices are getting smaller and powerful, advanced thermal solutions are needed to manage heat in limited and confined spaces. In this research, the performance of cooling techniques for progressively smaller and more powerful devices are explored. Flow, heat transmission (heat augmentation and evacuation), and pressure drop characteristics on a butterfly-shaped turbulence generator endwall in a channel passage are studied numerically. The impact of convective heat transfer on a new geometric protrusion surface is simulated in contrast to a baseline (smooth) surface. The new protrusion consists of two oval forms that are 90° apart from one another. The study also compared the baseline (smooth) and the new protrusion surfaces' capacities for performance and heat transfer at various channel heights of 5 mm, 7 mm, and 10 mm. The geometrical surfaces of the baseline and protrusion are simulated at five different Reynolds numbers (Re) of 600, 1000, 5000, 7000, and 15,000. The findings demonstrate that, in comparison to baseline smooth surfaces, the protruded surface's Nusselt number corresponds to a larger value of heat transmission. The Nusselt number increased with over 20% at Re 1000. When compared to the baseline surface in terms of pressure drop and frictional drag force, the presence of protruded configurations on the plate surface greatly enhances heat transmission performance. In terms of pressure drop and frictional drag force, the protruded surface significantly outperforms the baseline smooth surface. The results will be useful for heat enhancement applications in compact devices.
Comparison of gelation techniques for UO2 and UCO fuel kernels
Boyes, W.A.; Boyes, D.; Slabber, Johan F.M.; Braehler, G.; Froschauer, K.; Reiser, C. (Elsevier, 2025-11)
Most HTGR reactors being designed and developed all over the world will utilise High Assay Low Enriched Uranium (HALEU) fuel cycles (≤20 wt% U-235) either in a UO2 or UCO TRISO fuel form. There are two manufacturing routes to produce the fuel kernels: the External Gelation (German) process, which was adopted by the Chinese and applied to UO2 fuel forms, and the Internal Gelation (American) process mostly applied to UCO fuel forms. China has played a pivotal role in advancing the industrial-scale external gelation process, particularly in the field of nuclear fuel fabrication for high-temperature gas-cooled reactors. A key milestone is the commercial production line at China National Nuclear Corporation (CNNC) Northern Nuclear Fuel, which can produce up to 300,000 spherical fuel elements annually, utilising the external gelation process for kernel fabrication. This facility reflects not only China’s commitment to nuclear innovation but also its success in scaling up the external gelation process to meet commercial production demands. Significant advancements have been made in optimizing sol–gel chemistry, improving microsphere sphericity and uniformity, and enhancing process automation and quality control, which have collectively increased efficiency and reliability. Furthermore, the 2023 commissioning of China’s HTR-PM (High Temperature Gas-Cooled Reactor − Pebble-bed Module) demonstration reactor represents a landmark achievement, as it is the world’s first commercial reactor to utilize TRISO-coated fuel spheres produced via external gelation. A comparable scale implementation of the internal gelation process is still not available. Both the external gelation and internal gelation kernel fabrication methods produce high-quality fuel kernels, but a comparison was required between the two different processes for pilot and industrial scale operations that considers the ease of manufacture, operational ranges, process parameters, wastes, complexity of the chemistry, and proven technologies. This paper discusses the differences between the external and internal kernel fuel manufacturing processes and assesses which process is best used for pilot and commercial plant applications to produce either fuel form. The detailed manufacturing processes for both routes is discussed, as well as the complex chemistry for making high-quality UO2 or UCO fuel kernels. Each process will be compared in terms of the chemistry and operating parameters. The optimum High Temperature Gas Reactor (HTGR) kernel manufacturing process for industrial scale application to produce UO2 or UCO is the external gelation process, since the external gelation process is more forgiving in terms of corrections in the solution chemistry, the external gelation process has simpler chemistry and larger operating ranges. For pilot-scale kernel manufacture, the levels of complexity can be handled using either of the two production methods.
Tri-hybrid nanofluids for thermal applications: stability, magneto-hydrodynamics, and machine learning prediction
Adogbeji, Victor Omoefe; Atofarati, Emmanuel O.; Govinder, Kuvendran; Sharifpur, Mohsen; Meyer, Josua P. (Springer, 2025-08-11)
This study presents a comprehensive experimental and analytical investigation into the thermophysical and magneto-hydrodynamic (MHD) properties of //MWCNT/DIW tri-hybrid nanofluids (THNFs) across varying nanoparticle ratios with Sample A (15 wt.%, 80 wt.%, 5 wt.% MWCNT), Sample B (20 wt.%, 70 wt.%, 10 wt.% MWCNT), Sample C (20 wt.%, 60 wt.%, 20 wt.% MWCNT), Sample D (25 wt.%, 50 wt.%, 25 wt.% MWCNT), and Sample E (33.33 wt.% of each material). The effects of temperature (10–50 °C) on viscosity, thermal conductivity (TC), electrical conductivity (EC), stability, and sedimentation were analysed for advanced thermal management applications. Results indicate that the hybridization ratios markedly influence THNF properties. Higher content enhances stability by reducing particle agglomeration, while increased and MWCNT fractions elevate EC, however, excessive MWCNT raises viscosity, potentially impacting pumping efficiency. Notably, Sample E offers an optimal balance of TC, stability, and viscosity at lower concentrations. pH measurements reveal an acidic trend that decreases with rising temperature and volume fraction, potentially leading to corrosion in metallic systems. Strategies such as surfactant addition and surface functionalization are proposed to mitigate these effects. Moreover, machine learning models (Gradient Boosting, Random Forest, LightGBM) identified temperature as the dominant factor influencing TC and viscosity, while nanoparticle volume fraction primarily affected pH and EC, achieving high predictive accuracy (R2 > 0.96, MSE < 0.000025).
Assessment and characterization of the impact of pulmonary pathology on flow-induced acoustics using computational fluid dynamics
Makhanya, Khanyisani Mhlangano; Bhamjee, Muaaz; Martinson, Neil; Connell, Simon (Elsevier, 2026-07)
Please read abstract in the article. HIGHLIGHTS • Novel CFD approach to analysis of cough acoustics for pulmonary disease assessment • Studied Healthy lungs, pneumonia, bronchiectasis and cavitary tuberculosis • Method validated against 22 patient recordings with distinct clinical diagnoses • Model correlations with recordings advance understanding of respiratory acoustics • Potential to generate synthetic data to train AI model for telemedicine diagnoses
The climate opportunities and risks of contrail avoidance
Smith, Jessie R.; Grobler, Carla; Hodgson, Paul J.; Mukhopadhaya, Jayant; Shapiro, Marc L.; Mirolo, Matteo; Stettler, Marc E.J.; Eastham, Sebastian D.; Barrett, Steven R.H. (Nature Research, 2026-03)
Navigational contrail avoidance presents an opportunity for rapid reduction in aviation-attributable warming. Here, we use the Aviation Climate and Air Quality Impacts model to evaluate the global temperature changes associated with contrail avoidance towards 2050. If no avoidance is adopted, aviation is projected to contribute 0.040 K of CO2 warming and 0.054 K of contrail warming by 2050. The combined warming from aviation CO2 and contrails is 19% of the difference between current temperatures and the +2 °C limit above pre-Industrial levels, i.e. 19% of our remaining temperature budget. An avoidance strategy phased in over 2035-2045 may recover 9% of this budget, but a 10-year delay may reduce this to 2%. The warming due to additional CO2 emitted during avoidance is two orders of magnitude lower than the expected contrail warming reduction. For every year of delay, the world will be on average 0.003 K hotter in 2050. The most significant climate risk associated with contrail avoidance is therefore inaction.
Fuel and graphite temperatures in a micro nuclear reactor during a DLOFC
Du Toit, Charl; Boyes, Wayne; Slabber, Johan F.M. (Taylor and Francis, 2026)
The Advanced Micro Reactor (AMR), a high-temperature gas-cooled prismatic block reactor, is being designed to produce 10 MW of thermal power. The active core consists of an inner graphite reflector, fuel graphite block assemblies arranged in three rings, and an outer graphite reflector (OR). The system code Flownex SE has been used to set up an axisymmetric network model of the reactor to study the thermal-hydraulic behavior of the reactor under steady-state conditions and during a depressurized loss-of–forced cooling (DLOFC) event. The helium coolant enters the reactor at 320°C, flows up in the risers in the OR to the upper plenum, down through the core, and exists the lower plenum at 750°C. In the risers, the coolant is preheated to 324.8°C. Under steady-state conditions, the top of the core is on average 403°C cooler than the bottom of the core, and the maximum fuel and graphite temperatures are 1032.4°C and 809.0°C, respectively. During the DLOFC, the reactor endeavors to heat up the upper part of the core, cool down the lower part of the core, and set up the required temperature gradient in the radial direction to remove the decay heat and the excess heat accumulated in the solids. When the DLOFC starts and the reactor is scrammed, the fuel temperatures drop steeply, along with the drop in power, until they are in equilibrium with the corresponding graphite temperatures, and then follow the graphite temperatures as the decay heat decreases and the graphite heats up or cools down. This paper focuses among other things on the accumulation and release of heat by the fuel and solids during the DLOFC. The thermal behavior of the inner ring of the fuel block assemblies receives special attention.
Using of artificial neural networks (ANNs) to predict the rheological behavior of magnesium oxide-water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates
Li, Yicheng; Kalbasi, Rasool; Karimipour, Arash; Sharifpur, Mohsen; Meyer, Josua P. (Wiley, 2026-04)
Laboratory studies are usually time-consuming and costly; hence, soft computing methodology can be an attractive alternative for predicting results. In this study, the viscosity of MgO-water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates has been predicted by artificial neural networks (ANNs) and surface methods. In the ANN method, an algorithm is proposed to select the best neuron number for the hidden layer. In the fitting method, a surface is proposed for each volume fraction of nanoparticles, and finally, the results of the ANN and surface fitting method have been compared. It can be observed that increasing the volume fraction from 0.07% to 1.25% at temperatures of 25°C, 30°C, 40°C, 50°C, and 60°C resulted in about two-fold increase in viscosity. Also, the best network has 24 neurons in the hidden layer. It can be seen that for a network with 24 neurons in the hidden layer has the best overall correlation, and this coefficient is 0.999035. The mean absolute value of errors in the ANN and fitting method are 0.0118 and 0.0206, respectively.
Towards scientific machine learning for granular material simulations : challenges and opportunities
Fransen, Marc; Furst, Andreas; Tunuguntla, Deepak; Wilke, Daniel Nicolas; Alkin, Benedikt; Barreto, Daniel; Brandstetter, Johannes; Cabrera, Miguel Angel; Fan, Xinyan; Guo, Mengwu; Kieskamp, Bram; Kumar, Krishna; Morrissey, John; Nuttall, Jonathan; Ooi, Jin; Orozco, Luisa; Papanicolopulos, Stefanos-Aldo; Qu, Tongming; Schott, Dingena; Shuku, Takayuki; Sun, Waiching; Weinhart, Thomas; Ye, Dongwei; Cheng, Hongyang (Springer, 2026-01)
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advances in neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.
Intra-island variation in wind patterns on sub-Antarctic Marion Island
Schoombie, Janine; Craig, K.J. (Kenneth); Goddard, Kyle Andrew; Hedding, D.W. (David William); Nel, W.; Le Roux, Peter Christiaan (University of Pretoria, 2025-10)
Sub-Antarctic Marion Island provides a critical habitat for pelagic species, yet its terrestrial ecosystem faces increasing threats from climate change. Despite being situated in one of the windiest regions globally, the impact of changing wind patterns at the intra-island scale remains poorly understood. Existing datasets lack the spatial resolution necessary to capture fine-scale wind dynamics across the island. This study aimed to address this gap by presenting high-resolution wind speed and direction data to investigate the effects of wind on terrestrial systems. We present two complementary datasets: (1) wind measurements collected from 17 stations distributed across the island between May 2018 and March 2021, and (2) computational fluid dynamics (CFD) simulations providing wind vectors and associated properties at a 30 × 30 m resolution for heights up to 200 m above ground level. The data reveal significant differences in wind speed and direction across different geographical sectors of Marion Island. Notably, anemometers situated in the south recorded more frequent gale-force winds, while the western stations experienced calmer conditions. By using the observed wind direction frequencies, a weighted average vector plot was generated from the CFD simulations, providing an island-scale representation of spatial wind patterns across the island. These datasets offer valuable insights into variations in wind patterns, including upstream and downstream effects, and serve as a crucial resource for studying wind-driven processes affecting the landscape and ecosystem, such as seed dispersal.
Development of a predictive, risk-based model to assess the effects of maintenance decisions on vertical mine shaft structures
Wannenburg, Johann; Ngcobo, Glory Nomvula; Heyns, P.S. (Philippus Stephanus) (Elsevier, 2026-05)
PURPOSE : The study addresses the challenge of effective long-term maintenance of the structures of vertical mine shafts. These structures face significant degradation over time due to corrosion, the impact of falling objects, and exposure to harsh environments with high humidity, chemical contamination, and poor ventilation. Current maintenance practices often prioritise short-term needs, neglecting the long-term consequences for structural integrity and operational sustainability. To bridge this gap, the research introduces a novel predictive risk-based maintenance decision-making model. DESIGN/METHODOLOGY/APPROACH : The model incorporates finite element analysis and Monte Carlo simulations to evaluate the failure modes caused by corrosion, fatigue and falling objects while accounting for uncertainties in degradation rates and impact probabilities. The analysis calculates the energy of falling objects and estimates corrosion rates based on environmental conditions, enabling accurate predictions of the remaining useful life (RUL) of critical steel components. This is combined with an Integrated Structural Inspection and Maintenance Management (iSIMM) system, which combines structural inspection data with Computerised Maintenance Management Systems (CMMS). ORIGINALITY/VALUE : This model enables informed decision-making, enhancing safety, reliability, and cost-efficiency in mining operations. The research’s novelty lies in the integration of predictive and risk-based maintenance strategies, offering new insights into managing mine shaft structural integrity whilst integrating quantitative FEA-derived damage models (for impact) with stochastic, inspection-driven lifecycle simulation as a key methodological that enables the transition from qualitative inspection to predictive, risk-informed planning. FINDINGS : The model is used in a case study of a South African gold mine and demonstrates the practical application, showcasing its ability to optimise maintenance planning, reduce life cycle costs, and extend the lifespan of mine shafts, and to quantify the cost-risk trade-off between different multi-year maintenance strategies, a decision-support feature often missing in practice.
Multi-dish configurations for single-shaft and parallel-flow solar-dish Brayton cycles
Cockcroft, C.C. Le Roux; Le Roux, Willem Gabriel (Elsevier, 2025-12)
Concentrating solar power combined with parallel-flow Brayton cycles can form a viable solution to generating cleaner and more sustainable energy. Parallel-flow Brayton cycles are not influenced as greatly as traditional single-shaft cycles when solar and/or recuperation components are added to the cycle. To further improve the thermal efficiency of parallel-flow cycles, the solar heat input to the cycles can be increased through introducing a second solar receiver in the setup (a multi-dish setup). This analytical study addresses the viability of incorporating multi-dish configurations in parallel-flow and single-shaft Brayton cycles. The study is based on using off-the-shelf automotive turbochargers to develop a solarised micro gas turbine for power generation. It is determined that a multi-dish cycle setup adds performance improvement to the thermal efficiency results in both single-shaft and parallel-flow recuperated solar cycles. When the best-performing multi-dish recuperated low-temperature turbine (LTT) cycle is considered, the thermal efficiency is 69 % greater than in the best-performing single-dish recuperated LTT cycle. The multi-dish recuperated single-shaft cycle, however, obtains 3.2 % less thermal efficiency than the single-dish recuperated single-shaft cycle. The multi-dish single-shaft power output is greatly restricted by high solar receiver surface temperatures, which is not the case in the multi-dish recuperated LTT cycle. Therefore, more solar heat can be captured in the parallel-flow multi-dish LTT cycle.
CFD modelling of convective falling films for enhanced algae cultivation : fluid mechanics influence of mass transfer
Lancaster, Gerald; Bock, Bradley D.; Craig, K.J. (Kenneth) (EDP Sciences, 2026-01-14)
Falling film photobioreactors are able to increase the mass transfer rates per volume of liquid through the gas-liquid interface compared to other photobioreactor types. At low Reynolds numbers the flow mass transfer is still diffusion limited. This work aims to resolve and improve the convective mixing within thin falling films numerically to optimize the rates of CO2 absorption into a thin falling water film. Flat plate models are compared and it is found that slower films with a Reynolds number of 28 outperform faster flowing films by up to 2.5 times.
Enhancing the hydrothermal and economic efficiency of parabolic solar collectors with innovative semi-corrugated absorber tubes, shell form cone turbulators, and nanofluid
Samad, Sarminah; Saeidlou, Salman; Khan, M. Nadeem; Alamry, Ali; Al-Harbi, Laila M.; Sharifpur, Mohsen; Ghoushchi, S.P. (Elsevier, 2025-11)
This study proposes a performance-enhancing design for parabolic trough solar collectors by integrating a novel semi-corrugated absorber tube with an innovative shell-form cone turbulator, operating with CuO–water nanofluid. Numerical simulations were conducted across a Reynolds number range of 4500–10,930 to evaluate the effects of corrugation radius (0.5–1.5 mm), nanofluid volume fraction (1–3 %), and turbulator geometry. Three turbulator designs—full (FSFCT), semi (SSFCT), and hollow (HSFCT) shell-form cone turbulators—were analyzed to identify optimal configurations. Performance was assessed from both hydrothermal and economic perspectives using the performance evaluation criterion (PEC), levelized cost of energy (LCOE), and payback time. Results indicate that the configuration combining a semi-corrugated tube with a 1.5 mm radius, 3 % CuO nanofluid, and the FSFCT achieved a 369 % increase in Nusselt number, an LCOE of 0.546 $/kWh, and a payback time of 3.6 years, confirming its economic superiority. From a thermal-hydraulic perspective, the highest PEC value of 2.77 was obtained using the HSFCT under the same conditions.

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