General Geometry Research Articles

An Unstructured Mesh Reaction-Drift-Diffusion Master Equation with Reversible Reactions.

We develop a convergent reaction-drift-diffusion master equation (CRDDME) to facilitate the study of reaction processes in which spatial transport is influenced by drift due to one-body potential fields within general domain geometries. The generalized CRDDME is obtained through two steps. We first derive an unstructured grid jump process approximation for reversible diffusions, enabling the simulation of drift-diffusion processes where the drift arises due to a conservative field that biases particle motion. Leveraging the Edge-Averaged Finite Element method, our approach preserves detailed balance of drift-diffusion fluxes at equilibrium, and preserves an equilibrium Gibbs-Boltzmann distribution for particles undergoing drift-diffusion on the unstructured mesh. We next formulate a spatially-continuous volume reactivity particle-based reaction-drift-diffusion model for reversible reactions of the form . A finite volume discretization is used to generate jump process approximations to reaction terms in this model. The discretization is developed to ensure the combined reaction-drift-diffusion jump process approximation is consistent with detailed balance of reaction fluxes holding at equilibrium, along with supporting a discrete version of the continuous equilibrium state. The new CRDDME model represents a continuous-time discrete-space jump process approximation to the underlying volume reactivity model. We demonstrate the convergence and accuracy of the new CRDDME through a number of numerical examples, and illustrate its use on an idealized model for membrane protein receptor dynamics in T cell signaling.

General geometry realized by four-scalar model and application to [formula omitted] gravity

In this paper, we propose a model including four scalar fields coupled with general gravity theories, which is a generalization of the two-scalar model proposed in Phys. Rev. D 103 (2021) no.4, 044055, where it has been shown that any given spherically symmetric static/time-dependent spacetime can be realized by using the two-scalar model. We show that by using the four-scalar model, we can construct a model that realizes any given spacetime as a solution even if the spacetime does not have a spherical symmetry or any other symmetry. We also show that by imposing constraints on the scalar fields by using the Lagrange multiplier fields, the scalar fields become non-dynamical and they do not propagate. This tells that there does not appear any sound which is usually generated by the density fluctuation of the fluid. In this sense, the model with the constraints is a most general extension of the mimetic theory in JHEP 11 (2013), 135, where there appears an effective dark matter. The dark matter is non-dynamical and it does not collapse even under gravitational force. Our model can be regarded as a general extension of any kind of fluid besides dark matter. We may consider the case that the potential of the scalar fields vanishes and the model becomes a non-linear σ model. Then our formulation gives a mapping from the geometry of the spacetime to the geometry of the target space of the non-linear σ model via gravity theory although the physical meaning has not been clear. We also consider the application of the model to f(Q) gravity theory, which is based on a non-metricity tensor and Q is a scalar quantity constructed from the non-metricity tensor. When we consider the f(Q) gravity in the coincident gauge where the total affine connections vanish, when f(Q) is not a linear function of Q, spherically symmetric spacetime cannot be realized except in the case that Q is a constant. The situation does not change if we use the two-scalar model, as we show. If we use the four-scalar model in this paper, however, spherically symmetric spacetime can be realized in the framework of f(Q) gravity with the coincident gauge.

A porohyperelastic scheme targeted at High-Performance Computing frameworks for the simulation of the intervertebral disc

Background and Objective:The finite element method is widely used for studying the intervertebral disc at the organ level due to its ability to model complex geometries. An indispensable requirement for proper modelling of the intervertebral disc is a reliable porohyperelastic framework that captures the elaborate underlying mechanics. The increased complexity of such models requires significant computational power that is available within high-performance computing systems. The objective of this study is to present such a framework, validated both against literature and experiments, aiming to enable intervertebral disc research to benefit from state-of-the-art computational resources. Methods:In the context of this work, we implement a biphasic model that captures the mechanical response of the intricate, tissue-dependent models of the solid phase along with the hydrostatic pressure effects of the fluid phase. The tissue-dependent models involve the hyperelastic ground substance, fibrillar reinforcement, and osmotic swelling. The derived porohyperelastic, staggered scheme is implemented in Alya, a finite element code targeted at high-performance computing applications. The formulation is subsequently verified and validated by comparing the results of consolidation simulations with literature data for simulations and experiments using either generic or patient-specific geometries. Additionally, in-house experiments are replicated, evaluating the model’s ability to simulate alternating loading. Finally, the implementation’s circadian response is compared to previous implementation of similar material models in commercial software. Results:Results align well with experimental and literature findings in terms of disc height reduction (4% error), intradiscal pressure (14% error) and disc bulging. Validating the patient-specific geometry results in 4% and 7% deviation in measuring height loss. Simulations show excellent agreement with in-house experimental results, with less than 1% error regarding height reduction. Finally, the comparison to similar, published, earlier implementation in commercial software unveils excellent agreement of less than 1% error for the water content during circadian simulations. Simulation times are reported at 4 min per circadian cycle in the supercomputer Marenostrum V. Conclusions:This work presents a clear and validated formulation for simulating porohyperelastic materials based on assumptions that comply with the non-linear elasticity theory. The implementation in Alya enables intervertebral disc research to benefit from high-performance computing systems.

Numerical Methods for Topological Optimization of Wooden Structural Elements

Timber represents a building material that aligns with the environmental demands on the impact of the construction sector on climate change. The most common engineering solution for modern timber buildings with large spans is glued laminate timber (glulam). This project proposes a tool for a topological optimized geometry generator of structural elements made of glulam that can be used for building a database of topologically optimized glulam beams. In turn, this can be further used to train machine learning models that can embed the topologically optimized geometry and structural behavior information. Topological optimization tasks usually require a large number of iterations in order to reach the design goals. Therefore, embedding this information into machine learning models for structural elements belonging to the same topological groups will result in a faster design process since certain aspects regarding structural behavior such as strength and stiffness can be quickly estimated using Artificial Intelligence techniques. Topologically optimized geometry propositions could be obtained by employing generative machine learning model techniques which can propose geometries that are closer to the topologically optimized results using FEM and as such present a starting point for the design analysis in a reduced amount of time.

Design and validation of finite element models for the assessment of post-fixation distal femur fracture motion.

Fracture site motion is thought to play an important role in the healing of complex fractures of the distal femur via mechanotransduction. Measuring this motion in vivo is challenging, and this has led researchers to turn to finite element modeling approaches to gain insights into the mechanical environment at the fracture site. Developing a systematic understanding of the effect of different model choices for distal femur fractures may allow more accurate prediction of fracture site motion from these types of simulations. In this study, we aim to assess the effect of four different modeling choices and parameters. We looked at the effect of using bone specific density distributions vs generic values, employing landmark-based geometry generation, varying fracture alignment within clinically relevant ranges, and determining whether direct apposition of the fracture to the plate was achieved. For validation, five cadaveric femurs had fractures created and repaired with plated constructs, and these were then loaded and fracture site motion was directly measured. We found that using landmark based bone geometry and patient-specific bone density distributions had a minimal effect on the overall model predictions. Changing the alignment, particularly into varus and procurvatum could have a large (>50%) effect on predicted shear motion, as could direct apposition of the bone to the plate. These findings demonstrate that modeling choices can play an important role in simulating distal femur fracture mechanics, and it is particularly critical that patient customized models attempt to accurately represent alignment of the bone fragments and lateral plate apposition.

Effect of patient specificity on predicting knee cartilage degeneration in obese adults: Musculoskeletal finite-element modeling of data from the CAROT trial.

Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. Inthisstudy,we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL)and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve(AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

A leap forward in chemical process design: Introducing an automated framework for integrated AI and CFD simulations

Despite the numerous possibilities of integrating AI and CFD simulations for chemical process design, researchers often rely on manual techniques, resulting in suboptimal models and time-consuming processes. To address these challenges, we propose an automated framework that combines high-fidelity AI modeling with hyperparameter optimization, automated CFD simulations using OpenFOAM, and effortless post-processing for data extraction. This framework was tested on a reactor scale-up process involving 51 different configurations for butadiene synthesis, achieving 98.8 % accuracy in CFD validation and over 99 % accuracy in AI models. The automation pipeline streamlines geometry generation, meshing, simulation, data extraction, and AI-driven optimization, significantly reducing manual effort. Our framework is versatile, customizable for various types of process equipment design, and employs open-source software for ease of adoption and reproducibility. This approach not only enhances accuracy and efficiency but also opens up AI and CFD integration to a broader range of researchers.

The FLUKA Monte Carlo simulation of the magnetic spectrometer of the FOOT experiment

The FOOT experiment of INFN is devoted to the measurement of the nuclear fragmentation double differential cross sections useful for the improvement of calculation models adopted in hadrontherapy and radioprotection. A detailed Monte Carlo simulation of the FOOT magnetic spectrometer has been implemented in order to optimize the design and to guide data analysis. This task has been accomplished by means of the FLUKA Monte Carlo code. The input files of the FLUKA simulations are created from the software framework of the experiment, in order to have a consistent generation and description of geometry and materials in both simulation and data analysis. In addition, this ensures the possibility of processing both simulated and real data with the same data analysis procedures. Databases containing specific parameters describing the setup employed in each different data taking campaign are used. A customized event-by-event output of the Monte Carlo code has been developed. It can be read out by the general software framework of FOOT, enabling access to the generation history of all particles in the same event. This output structure therefore gives the possibility to perform a detailed analysis and study of all relevant processes, allowing the detailed tracking reconstruction of all individual particles. Examples of results are presented.

Thermal-hydraulic analysis of wetted airside plain fin flat tube heat exchangers and the role of vortex generators

This study addresses the demand for efficient and compact heat exchange solutions in industrial applications. Unlike previous studies that predominantly focused on round tube configurations, this research investigates the impact of vortex generators and geometrical parameters on plain-fin flat-tube designs. Using ANSYS Fluent, the study evaluated the effects of fin geometry, vortex generators, and flow conditions on thermal–hydraulic performance. The key findings revealed that vortex generators enhanced heat transfer by 5–48% by promoting fluid mixing and turbulence but also leading to an increment in friction losses reaching 35–62%, depending upon the number of vortex generators. Condensation rates increased downstream of vortex generators due to enhanced mixing with cooler surfaces. In terms of thermal–hydraulic performance, specific plain-fin configurations outperformed others, offering efficient heat transfer with relatively lower friction losses, even when compactness was considered. Some configurations were recommended for applications where heat exchanger size is less important, but high heat transfer is crucial. Lastly, it was discovered that the effect of vortex generators is more pronounced for lower to intermediate pumping powers per unit area. New empirical correlations were also derived to predict thermal–hydraulic behavior based on geometry and vortex generator placement. A comparison with the literature revealed significantly higher performance as compared to convex/concave vortex generators. Lastly, it was discovered that the effect of vortex generators is more pronounced for lower to intermediate pumping powers per unit area. Empirical correlations for “j” and “f” were correlated with all geometric parameters.

Benefits derived from WASP (a Grasshopper plugin) to Design for Disassembly Principles in the Built Environment

Abstract Design for Disassembly (DfD, henceforth) is a way to improve the effectiveness of the disassembly process in built environments, and it mainly contains ten principles. Examples of the principles include deconstruction techniques; selected materials; reachable connections; utilizing connections with fewer bolts, screws, and nails; the straightforwardness of the structure and design; and versatility. On the other hand, the WASP plugin for Grasshopper (on Rhinocheros©) is a toolkit for discrete computational design to simulate the aggregation and assembly possibilities in the building system. This paper explores the integration of the WASP plugin within the context of DfD through three criteria: the aggregation processes of part geometries, connection locations, and their orientation toward the building elements. Design with discrete elements, which can be defined as shape grammars for building structure, is the primary approach for the joining behavior and detaching behavior of elements discussed in this paper. Also, this type of component-based design can optimize the geometry and data-driven generation of the structure. The design process in this way can be disassembly sequence planning and knowledge-based design by applying the design constraints. To illustrate the integration, research is conducted through WASP simulation in designing a traditional Iranian timber pavilion-inspired structure with a 3×3m footprint in a roof structure. The fusion of WASP and DfD represents a promising avenue for advancing circular design principles and achieving more sustainable, resource-efficient buildings. It is concluded from this study that WASP makes the aggregation simulation and assembly possibilities of the vertical and horizontal components in a building more accessible.

Vacuum currents in curved tubes

We investigate the combined effects of spatial curvature and topology on the properties of the vacuum state for a charged scalar field localized on rotationally symmetric 2D curved tubes. For a general spatial geometry and for quasiperiodicity condition with a general phase, the representation of the Hadamard function is provided where the topological contribution is explicitly extracted. As an important local characteristic of the vacuum state the expectation value of the current density is studied. The vacuum current is a periodic function of the magnetic flux enclosed by the tube with the period of flux quantum. The general formula is specified for constant radius and conical tubes. As another application, we consider the Hadamard function and the vacuum current density for a scalar field on the Beltrami pseudosphere. Several representations are provided for the corresponding expectation value. For small values of the proper radius of the tube, compared with the curvature radius, the effect of spatial curvature on the vacuum current is weak and the leading term in the corresponding expansion coincides with the current density on a constant radius tube. The effect of curvature is essential for proper radii of the tube larger than the radius of spatial curvature. In this limit the falloff of the current density, as a function of the proper radius, follows a power law for both massless and massive fields. This behavior is in clear contrast to the one for a constant radius tube with exponential decay for massive fields. We also compare the vacuum currents on the Beltrami pseudosphere and on locally de Sitter and anti–de Sitter 2D tubes. Published by the American Physical Society 2024

Comparative analysis of allosteric rearrangements in FtsZ protein structure induced by benzamide and 4-hydroxycoumarine compounds

Aim. To reveal allosteric rearrangements of FtsZ molecules arising under the influence of benzamide compounds and 4-hydroxycoumarin derivatives. To discover the key molecular mechanisms predetermining the effect of the specified compounds on the cell division in bacteria. Methods. Comparative analysis of FtsZ protein structures and their complexes with ligands. Application of structural bioinformatics software for molecular visualization, measurement of interatomic distances and approximation of intramolecular shifts based on RMSD indicators. Results. Revealed conformational changes in FtsZ protein molecules, induced by allosteric effectors: 4-hydroxycoumarin – 4HC and benzamide – 9PC (PC-190723). Allosteric deformations and their consequences for intact FtsZ protein molecules, there GTPase domains, H7 helixes and C-terminal domains were studied. Conclusions. It was clarified that the binding of benzamides causes more significant shifts in the structure of the FtsZ protein monomer, its C-terminal domain, and H7 helix. At the same time, 4-hydroxycoumarins deform the structure of the GTPase domain almost twofold effectively. Both classes of compounds prosses allosteric action through unique mechanisms that are largely realized through deformations and displacements of the H7 helix. Despite the fact that these compounds demonstrate different allosteric mechanisms of action, their final effect can be summarized to destructions in GTP pocket, protofilament interfaces and the general geometry of molecule.

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

The climate targets set out in the “European Green Deal” call for the consideration and implementation of climate-friendly propulsion concepts and sustainable fuels in future aircraft configurations. This puts the use of very efficient propeller engines into the focus of aircraft design. However, these pose major challenges, especially to cabin acoustics, due to high, tonal sound pressure levels on the fuselage. The design of noise control measures requires the competence to predict the resulting interior noise as early as possible in the design process of the vehicle. This requires a high level of geometric and structural detail regarding the fuselage structure and cabin components. With increasing frequency range, the necessary structural details also increase, which have to be resolved in the simulation models due to the associated decreasing structural wavelength. Especially in the context of aircraft pre-design, there is usually not enough information available for a detailed vibro-acoustic modeling of the fuselage and cabin components, which allows a meaningful prediction of the vibrations and thus the cabin noise. Therefore, the knowledge-based tool Fuselage Geometry Assembler (FUGA) is developed for the targeted enrichment of preliminary design data with knowledge for detailed numerical analyses. This paper describes the knowledge-based geometry and model generation in FUGA, which can consider the necessary (increasing) level of detail for the vibro-acoustic prediction already in the preliminary design. For this purpose, aircraft data sets in the preliminary design data format Common Parametric Aircraft Configuration Schema (CPACS) form the modeling basis. Originating from the aircraft preliminary design, these initially describe the outer shell of the vehicle and are extended by detailed structural information that defines the geometric boundary conditions for component placement in cabin design. For the cabin components, the open-source geometry kernel Open Cascade Technology (OCCT) is used to provide geometries at the level of detail required for subsequent analyses. The geometry models are then discretized in open source (e.g. Gmsh) or commercial meshers and further used for numerical analysis. Finally, the prediction of cabin noise is demonstrated as a Proof of Concept using the example of a short-haul propeller-driven aircraft and the feasibility of the proposed method is indicated by investigating the sensitivity of resulting simulation models to the fuselage skin thickness.

Utilizing serverless framework for dynamic visualization and operations in geospatial applications

ABSTRACT While substantial efforts have been invested in the development of Discrete Global Grid Systems (DGGS) spatial operations and their potential applications in the geospatial domain, it has become evident that there is a demand for an efficient and scalable system to handle the visualization of large-scale DGGS data. This study demonstrated the potential of DGGS in conjunction with the serverless framework for dynamic visualization at various resolutions, which is based on data storage and effective querying using PostgreSQL integrated into Amazon Aurora Serverless. The use of Amazon Web Services (AWS) Lambda for on-the-fly generation of hexagon geometries significantly reduced the storage requirements and improved the speed of the visualization process. In addition, we implemented on-the-fly spatial operations including point binning, thresholding, aggregation, and neighborhood operations in the DGGS, highlighting the capabilities of DGGS in vector and raster processing. The proposed system has shown promising results in terms of efficiency, scalability, and adaptability, making it a viable solution for large-scale geospatial data processing and visualization. Case studies using flood risk data and terrain data further illustrate the system’s practical applicability in on-the-fly spatial operations and rapid visualization.

Adaptive Surface Normal Constraint for Geometric Estimation From Monocular Images.

We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness.

Generalization of the Synthetic Aperture Radar Azimuth Multi-Aperture Processing Scheme—MAPS

This paper analyzes the advantages and the drawbacks of using the Synthetic Aperture Radar (SAR) azimuth multichannel technique known as Multi-Aperture Processing Scheme (MAPS), in a set of relevant application cases that are far from the canonical ones. In the scientific literature on this topic, equally distributed azimuth channels with the quasi-monostatic deployment are assumed. With this research, we aim at extending the models from the current literature to (i) a generic bistatic acquisition geometry, (ii) a set of cases where the number of receiving tiles is not the same for each channel, or (iii) the tiles are shared between adjacent channels thus creating an overlapping configuration. The paper introduces the mathematical models for the listed non-conventional MAPS cases. Dealing with the bistatic MAPS, we first solve the problem by interpreting multichannel acquisition as a bank of Linear Time Invariant (LTI) filters. Then, a more physical approach, based on discrimination of the direction of arrivals (DoAs) is pursued. The effectiveness of the two methods and the advantages of the second approach on the first are proved by using a simplified 1D end-to-end simulation. Even limiting to the monostatic configuration, the azimuth antenna tiles have always been supposed equally partitioned among the RX channels. Overcoming this limit has two advantages: (i) more MAPS possible solutions in case few azimuth tiles are available, as in the ROSE-L mission; (ii) the number of channels can be designed independently of the number of tiles, also allowing asymmetric solutions, useful for a phase array antenna with an odd number of tiles such as in the SAOCOM-1 mission. Conversely, sharing one or more receiving tiles in different receiving channels makes the input noise partially correlated. The drawback is an increase in the noise level. A trade-off is determined for the different solutions obtained using simulations with real mission parameters. The theoretical performance and the end-to-end simulations are compared.

Analytical and computational studies predict negligible risk of cell death from eddy generation off flat surfaces in cell culture flow systems.

Cell-based therapies represent the current frontier of biomedical innovations, with the technologies required underpinning treatments as broad as CAR-T cell therapies, stem cell treatments, genetic therapies and mRNA manufacture. A key bottleneck in the manufacturing process for each of these lies in the expansion of cells within a bioreactor vessel, requiring by far the greatest share of time for what are often time-critical therapies. While various designs, culture feeding and mixing methods are employed in these bioreactors, a common concern among manufacturers and researchers lies in whether shear stresses generated by culture media flow will damage cells and inhibit expansion. This study develops an analytical tool to link macro-scale measures of flow to risk of cell death using relationships with eddy size and dissipation rates, from eddies generated off flat surfaces. This analytical tool was then employed using computational fluid dynamics (CFD) to replicate a range of generic bioreactor geometries and flow conditions. We found that no combination of flow condition or design parameter was predicted by the tool to cause cell death within eddies, indicating negligible risk of cell death due to eddy formation within cell culture systems. While this requires experimental validation, and does not apply when cells are expanded using microcarriers, this tool nonetheless provides reassurance and accessible prediction of bioreactor design parameters that could result in cell death. Finally, our findings show that bioreactor design can be tailored such that the shear stress stimulation of cells can be selectively altered through small changes in flow rate.

Plume and wall temperature impact on the subsonic aft-body flow of a generic space launcher geometry

Experimental and numerical simulation of launcher base flows are crucial for future launcher design. In experiments, exhaust plume simulation is often limited to cold or slightly heated gases. In numerical simulations, multi-species reactive flow is often neglected due to limited resources. The impact of these simplifications on the relevant flow features, compared to real flight scenarios, needs to be characterized in order to enhance the design process. Experimental and numerical investigations were carried out within the framework of the SFB/TRR 40 Collaborative Research Centre to study the impact of plume and wall temperature on the base flow of a generic small-scale launcher configuration. Wind tunnel tests were performed in the Hot Plume Testing Facility (HPTF) at DLR, Cologne, using subsonic ambient flow and pressurized air or hydrogen–oxygen combustion as exhaust gases. The tests were numerically rebuilt using the DLR TAU code employing a scale-resolved IDDES approach, including thermal coupling and detailed chemistry. The paper combines the experimental and numerical findings from the SFB/TRR 40 base flow studies and highlights the most prominent influences on the mean flow field, the pressure field, the dynamic wake flow motion, and the resulting aerodynamic forces on the nozzle. High-frequency pressure measurements, high-speed schlieren recordings, and time-resolved CFD results are evaluated using spectral and modal analysis of the one- and two-dimensional flow field data.

CFD modelling and analysis of a radial ORC turbine with backswept rotor blades

Abstract Global electricity demand has steadily risen over the past five decades due to increased industrialization and improved accessibility worldwide. Addressing this escalating demand requires expanding electricity production, achievable through adopting renewable energy sources or enhancing production efficiency. Organic Rankine Cycle (ORC) power systems offer a promising solution for efficient electricity generation by utilizing organic fluids with lower critical parameters. Unlike conventional steam power plants, ORC systems operate at lower temperatures, making them adaptable to various applications. The efficiency of ORC power systems heavily relies on the design and performance of their turbines. This study focuses on modelling and analyzing the ORC turbine using Computational Fluid Dynamics (CFD) techniques, specifically employing a radial-inflow design with backswept rotor blades. Geometric parameters were derived from a simplified zero-dimensional (0-D) turbine model developed in MATLAB. CFD modelling was conducted using ANSYS software, integrating the BladeGen module for turbine geometry generation and the TurboGrid toolbox for mesh creation. Comparative analysis with the 0-D design method validated the accuracy of the CFD turbine modelling and the achievement of desired operational parameters. This research underscores the potential of CFD techniques in optimizing ORC turbine design thus allowing to achieve efficient electricity generation, contributing to the advancement of sustainable energy technologies.

Microcrack characteristics of granite under circumferential strain-controlled uniaxial compression test using UDEC-GBM

Rockburst is one of the significant challenges for underground excavation stability. The post-peak behaviour of rock controls the way it fails, and the released energy is potentially involved in the rockburst. However, understanding of both microscopic and macroscopic behaviours of brittle rock in the post-peak stage is limited due to the lack of proper loading methods and quantitative microscopic analysis methods. This study proposes a circumferential strain-controlled loading algorithm in UDEC to control the failure process, which has been verified against previous literature. By employing the improved geometry generation method, the microcrack evolution is quantitatively assessed. Energy analysis reveals that up to 80% of the input energy is stored in the granite prior to complete failure, which could be released if the rock fails at this moment. Conversely, after the complete failure, intragranular cracks account for the majority of energy dissipation, indicating its importance in post-peak stability. A small portion of the peak stored strain energy needs to be extracted to achieve a controlled failure process. Furthermore, it is found that the significant increases in the formation of intergranular shear and low-angled microcracks potentially feature the complete failure of the specimen. The proposed model offers a complementary method to systematically investigate the post-peak behaviour of rock, including the evolution of microcracks and energy.