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This study uses computer-aided design to improve the ecological and environmental sustainability of early-stage landscape designs. Urban expansion on open land and natural habitats has led to a decline in biodiversity and increased climate change impacts, affecting urban inhabitants' quality of life and well-being. While sustainability indicators have been employed to assess the performance of buildings and neighbourhoods, landscape designs' ecological and environmental sustainability has received comparatively less attention, particularly in early-design stages where applying sustainability approaches is impactful.

The authors propose a computation framework for evaluating key landscape sustainability indicators and providing real-time feedback to designers. The method integrates spatial indicators with widely recognized sustainability rating system credits. A specialized tool was developed for measuring biomass optimization, precipitation management and urban heat mitigation, and a proof-of-concept experiment tested the tool's effectiveness on three Mediterranean neighbourhood-level designs.

The results show a clear connection between the applied design strategy to the indicator behaviour. This connection enhances the ability to establish sustainability benchmarks for different types of landscape developments using parametric design.

The study allows non-expert designers to measure and embed landscape sustainability early in the design stages, thus lowering the entry level for incorporating biodiversity enhancement and climate mitigation approaches.

This study expands the parametric vocabulary for measuring landscape sustainability by introducing spatial ecosystem services and architectural sustainability indicators on a unified platform, enabling the integration of critical climate and biodiversity-loss solutions earlier in the development process.

To make wind energy (one of the alternative-energy production technologies) economical, wind-turbines (convertors of wind energy into electrical energy) are required to operate, with only regular maintenance, for at least 20 years. However, some key wind-turbine components (especially the gear-box) often require significant repair or replacement after only three to five years in service. This causes an increase in both the wind-energy cost and the cost of ownership of the wind turbine. The paper aims to discuss these issues.

To overcome this problem, root causes of the gear-box premature failure are currently being investigated using mainly laboratory and field-test experimental approaches. As demonstrated in many industrial sectors (e.g. automotive, aerospace, etc.) advanced computational engineering methods and tools cannot only complement these experimental approaches but also provide additional insight into the problem at hand (and do so with a substantially shorter turn-around time). The present work demonstrates the use of a multi-length-scale computational approach which couples large-scale wind/rotor interactions with a gear-box dynamic response, enabling accurate determination of kinematics and kinetics within the gear-box bearings (the components most often responsible for the gear-box premature failure) and ultimately the structural response (including damage and failure) of the roller-bearing components (e.g. inner raceways).

It has been demonstrated that through the application of this approach, one can predict the expected life of the most failure-prone horizontal axis wind turbine gear-box bearing elements.

To the authors’ knowledge, the present work is the first multi-length-scale study of bearing failure in wind-turbines.

The purpose of this paper is to introduce and analyze a new blast-wave impact-mitigation concept using advanced computational methods and tools. The concept involves the use of a protective structure consisting of bimolecular reactants displaying a number of critical characteristics, including: a high level of thermodynamic stability under ambient conditions (to ensure a long shelf-life of the protective structure); the capability to undergo fast/large-yield chemical reactions under blast-impact induced shock-loading conditions; large negative activation and reaction volumes to provide effective attenuation of the pressure-dominated shockwave stress field through the volumetric-energy storing effects; and a large activation energy for efficient energy dissipation. The case of a particular bimolecular chemical reaction involving polyvinyl pyridine and cyclohexyl chloride as reactants and polyvinyl pyridinium ionic salt as the reaction product is analyzed.

Direct simulations of single planar shockwave propagations through the reactive mixture are carried out, and the structure of the shock front examined, as a function of the occurrence of the chemical reaction. To properly capture the shockwave-induced initiation of the chemical reactions during an impact event, all the calculations carried out in the present work involved the use of all-atom molecular-level equilibrium and non-equilibrium reactive molecular-dynamics simulations. In other words, atomic bonding is not pre-assigned, but is rather determined dynamically and adaptively using the concepts of the bond order and atomic valence.

The results obtained clearly reveal that when the chemical reactions are allowed to take place at the shock front and in the shockwave, the resulting shock front undergoes a considerable level of dispersion. Consequently, the (conserved) linear momentum is transferred (during the interaction of the protective-structure borne shockwaves with the protected structure) to the protected structure over a longer time period, while the peak loading experienced by the protected structure is substantially reduced.

To the authors’ knowledge, the present work is the first attempt to simulate shock-induced chemical reactions at the molecular level, for purposes of blast-mitigation.

In the present work, a new blast-/ballistic-impact mitigation concept is introduced and its efficacy analyzed using advanced computational methods and tools. The concept involves the use of a zeolite protective layer separated by air from the structure being protected and in contact with a water layer in front. The paper aims to discuss these issues.

To properly capture the attendant nano-fluidics phenomena, all the calculations carried out in the present work involved the use of all-atom molecular-level equilibrium and non-equilibrium molecular-dynamics simulations.

Under high-rate loading, water molecules (treated as a nano-fluidic material) are forced to infiltrate zeolite nanopores wherein, due to complex interactions between the hydrophobic nanopore walls and the hydrogen bonds of the water molecules, water undergoes an ordering-type phase transition and acquires high density, while a significant portion of the kinetic energy of the water molecules is converted to potential energy. Concomitantly, a considerable portion of this kinetic energy is dissipated in the form of heat. As a result of these energy conversion/dissipation processes, the (conserved) linear momentum is transferred to the target structure over a longer time period, while the peak loading experienced by the structure is substantially reduced.

To the authors’ knowledge, the present work constitutes the first reported attempt to utilize pure SiO₂ hydrophobic zeolites in blast-/ballistic-impact protection applications.

The initial stiffness method has been extensively adopted for elasto‐plastic finite element analysis. The main problem associated with the initial stiffness method, however, is its slow convergence, even when it is used in conjunction with acceleration techniques. The Newton‐Raphson method has a rapid convergence rate, but its implementation resorts to non‐symmetric linear solvers, and hence the memory requirement may be high. The purpose of this paper is to develop more advanced solution techniques which may overcome the above problems associated with the initial stiffness method and the Newton‐Raphson method.

In this work, the accelerated symmetric stiffness matrix methods, which cover the accelerated initial stiffness methods as special cases, are proposed for non‐associated plasticity. Within the computational framework for the accelerated symmetric stiffness matrix techniques, some symmetric stiffness matrix candidates are investigated and evaluated.

Numerical results indicate that for the accelerated symmetric stiffness methods, the elasto‐plastic constitutive matrix, which is constructed by mapping the yield surface of the equivalent material to the plastic potential surface, appears to be appealing. Even when combined with the Krylov iterative solver using a loose convergence criterion, they may still provide good nonlinear convergence rates.

Compared to the work by Sloan et al., the novelty of this study is that a symmetric stiffness matrix is proposed to be used in conjunction with acceleration schemes and it is shown to be more appealing; it is assembled from the elasto‐plastic constitutive matrix by mapping the yield surface of the equivalent material to the plastic potential surface. The advantage of combining the proposed accelerated symmetric stiffness techniques with the Krylov subspace iterative methods for large‐scale applications is also emphasized.

Accurate documentation of damaged or destroyed historical buildings to protect cultural heritage has been on the agenda of architecture for many years. In that sense, this study uses machine learning (ML) to predict missing/damaged parts of historical buildings within the scope of early ottoman tombs.

This study uses conditional generative adversarial networks (cGANs), a subset of ML to predict missing/damaged parts of historical buildings within the scope of early Ottoman tombs. This paper discusses that using GAN as a ML framework is an efficient method for estimating missing/damaged parts of historical buildings. The study uses the plan drawings of nearly 200 historical buildings, which were prepared one by one as a data set for the ML process.

The study contributes to the field by (1) generating a mixed methodological framework, (2) validating the effectiveness of the proposed framework in the restitution of historical buildings and (3) assessing the contextual dependency of the generated data. The paper provides insights into how ML can be used in the conservation of architectural heritage. It suggests that using a comprehensive data set in the process can be highly effective in getting successful results. The findings of the research will be a reference for new studies on the conservation of cultural heritage with ML and will make a significant contribution to the literature.

A reliable outcome has been obtained concerning the interpretation of documented data and the generation of missing data at the macro level. The framework is remarkably effective when it comes to the identification and re-generation of missing architectural components like walls, domes, windows, doors, etc. on a macro level without details. On the other hand, the proposed methodological framework is not ready for advanced steps of restitution since every case of architectural heritage is very detailed and unique. Therefore, the proposed framework for re-generation of missing components of heritage buildings is limited by the basic geometrical form which means the architectural details of the mentioned components including ornaments, materials, identification of construction layers, etc. are not covered.

The generic literature as to ML models used in architecture mostly constitutes design exploration and floor plan/urban layout generation. More specific studies in the conservation of architectural heritage by using ML mostly focus on architectural component recognition over 3D point cloud data (1) or superficial damage detection of heritage buildings (2). However, we propose a mixed methodological framework for the interpretation of documented architectural data and the regeneration of missing parts of historical buildings. In addition, the methodology and the results of this paper constitute a guide for further research on ML and consequently contribute to architects in the early phases of restitution.

The purpose of this paper is to determine dependencies between a rotor-blade shape and a rotor performance as well as to search for optimal shapes of blades dedicated for helicopter main and tail rotors.

The research is conducted based on computational methodology, using the parametric-design approach. The developed parametric model takes into account several typical blade-shape parameters. The rotor aerodynamic characteristics are evaluated using the unsteady Reynolds-averaged Navier–Stokes solver. Flow effects caused by rotating blades are modelled based on both simplified approach and truly 3D simulations.

The computational studies have shown that the helicopter-rotor performance may be significantly improved even through relatively simple aerodynamic redesigning of its blades. The research results confirm high potential of the developed methodology of rotor-blade optimisation. Developed families of helicopter-rotor-blade airfoils are competitive compared to the best airfoils cited in literature. The finally designed rotors, compared to the baselines, for the same driving power, are characterised by 5 and 32% higher thrust, in case of main and tail rotor, respectively.

The developed and implemented methodology of parametric design and optimisation of helicopter-rotor blades may be used in future studies on performance improvement of rotorcraft rotors. Some of presented results concern the redesigning of main and tail rotors of existing helicopters. These results may be used directly in modernisation processes of these helicopters.

The presented study is original in relation to the developed methodology of optimisation of helicopter-rotor blades, families of modern helicopter airfoils and innovative solutions in rotor-blade-design area.

Polyurea is an elastomeric two-phase co-polymer consisting of nanometer-sized discrete hard (i.e. high glass transition temperature) domains distributed randomly within a soft (i.e. low glass transition temperature) matrix. A number of experimental investigations reported in the open literature clearly demonstrated that the use of polyurea external coatings and/or internal linings can significantly increase blast survivability and ballistic penetration resistance of target structures, such as vehicles, buildings and field/laboratory test-plates. When designing blast/ballistic-threat survivable polyurea-coated structures, advanced computational methods and tools are being increasingly utilized. A critical aspect of this computational approach is the availability of physically based, high-fidelity polyurea material models. The paper aims to discuss these issues.

In the present work, an attempt is made to develop a material model for polyurea which will include the effects of soft-matrix chain-segment molecular weight and the extent and morphology of hard-domain nano-segregation. Since these aspects of polyurea microstructure can be controlled through the selection of polyurea chemistry and synthesis conditions, and the present material model enables the prediction of polyurea blast-mitigation capacity and ballistic resistance, the model offers the potential for the “material-by-design” approach.

The model is validated by comparing its predictions with the corresponding experimental data.

The work clearly demonstrated that, in order to maximize shock-mitigation effects offered by polyurea, chemistry and processing/synthesis route of this material should be optimized.

Wind energy is one of the most promising and the fastest growing alternative-energy production technologies, which have been developed in response to stricter environmental regulations, the depletion of fossil-fuel reserves, and the world's ever-growing energy needs. This form of alternative energy is projected to provide 20 percent of the US energy needs by 2030. For economic reasons, wind turbines (articulated structures that convert wind energy into electrical energy) are expected to operate, with only regular maintenance, for at least 20 years. However, some key wind turbine components (especially the gearbox) tend to wear down, malfunction and fail in a significantly shorter time, often three to five years after installation, causing an increase in the wind-energy cost and in the cost of ownership of the wind turbine. Clearly, to overcome this problem, a significant increase in long-term gearbox reliability needs to be achieved.

While purely empirical efforts aimed at identifying shortcomings in the current design of the gearboxes are of critical importance, the present work demonstrates that the use of advanced computational engineering analyses, like the finite-element stress analysis and a post-processing fatigue-life assessment analysis, can also be highly beneficial.

The results obtained in the present work clearly revealed how a variety of normal operating and extreme wind-loading conditions can influence the service-life of a wind-turbine gearbox in the case when the service-life is controlled by the gear-tooth bending-fatigue.

The present work attempts to make a contribution to the resolution of an important problem related to premature-failure and inferior reliability of wind-turbine gearboxes.