Publications

Design is a dynamic, decision-driven process, often guided by intuition and experience. It can be susceptible to cognitive biases, systematic deviations in information processing and decision making, which have been recognized as influential factors affecting expert judgment in multiple domains. Although some studies in the design field have investigated and proposed methods to address specific biases, such as the confirmation bias, there is currently no comprehensive approach in the literature to make designers aware of the various biases that may manifest during the design process. The main contribution of this article is to provide designers with a broad overview of the biases that may be involved within the three principal areas of design cognition: problem formulation, concept generation and concept evaluation. It also proposes a novel and workable methodology to facilitate designers' recognition and mitigation of biases through metacognition, while favoring the implementation of more specific correction strategies.

Supercritical water oxidation (SCWO) with hydrothermal flames is well established for the treatment of aqueous organic waste as it not only overcomes the limitations of simple SCWO, such as precipitation of salts, but also exhibits many advantages over other waste treatment processes. Seeking these advantages, we propose to perform SCWO using hydrothermal flames in microfluidic reactors ) for aerospace applications to be used in deep space/ISS missions. The novelty and highlight of this work are successful demonstration of realizing microreactors (channel width 200 ), which can withstand pressure of 250 bar with temperature °C, thereby presenting the feasibility to realize this technology. We present the first evidence of SCWO/hydrothermal in a flow microreactor of sapphire, which is captured through optical visualization. This is followed by a numerical investigation to understand the underlying physics leading to the formation of hydrothermal flame and thus differentiate it from a simple SCWO reaction. The simulations are performed in a 2D domain in a co-flow configuration with equal inlet velocity of fuel and oxidizer at two different inlet temperatures (350 °C and 365 °C), just below the critical temperature of water using ethanol and oxygen, the former acting not only as a model organic matter but also fuel for the formation of hydrothermal flames. It is observed that due to microscale size of the system, hydrothermal flames are formed at low inlet velocities (< 30 mm/s), while reaction at higher ones are characterized as simple SCWO reaction. This upper limit of inlet velocity was found to increase with inlet temperature. Finally, some key characteristics of hydrothermal flames - ignition delay time, flame structure, shape, and local propagation speed are analyzed.

Most of biopharmaceuticals, in their liquid form, are prone to instabilities during storage. In order to improve their stability, lyophilization is the most commonly used drying technique in the pharmaceutical industry. In addition, certain applications of biopharmaceutical products can be considered by oral administration and tablets are the most frequent solid pharmaceutical dosage form used for oral route. Thus, the tableting properties of freeze-dried products used as cryo and lyoprotectant could be a key element for future pharmaceutical developments and applications. In this study, we investigated the properties that might play a particular role in the specific compaction behavior of freeze-dried excipients. The tableting properties of freeze-dried trehalose, lactose and mannitol were investigated and compared to other forms of these excipients (spray-dried, commercial crystalline and commercial crystalline milled powders). The obtained results showed a specific behavior in terms of compressibility, tabletability and brittleness for the amorphous powders obtained after freeze-drying. The comparison with the other powders showed that this specific tableting behavior is linked to both the specific texture and the physical state (amorphization) of these freeze-dried powders.

A new model predicting the whole-body Dynamic Mean thermal sensation Vote (DMV) is described. The model is useful for evaluating transient thermal conditions but is limited to uniform ones. It is based on physiological signals (mean skin temperature and its rate of change, mean skin wittedness, and core body temperature) simulated by using Gagge's two-node thermophysiological model. It is derived from empirical data obtained through experiments conducted under 160 steady-state thermal exposures at rest, 60 transient thermal conditions at rest, and 24 static thermal conditions during exercise. An independent validation is performed against 13 transient thermal conditions during exercise. The model shows good agreement (RMSE less than 0.5) with experimental observations within the range of air temperatures between 15 and 37 °C and when activity levels are below 3 met. It performs better than the widely used Fanger's PMV model, especially when far from thermal neutrality, for step-change thermal transients, and under exercise conditions. Furthermore, the model's simplicity and low computational cost are important advantages over more complex and computationally expensive thermal sensation models based on multi-segment and multi-node thermophysiological models.

After the Notre-Dame de Paris (NDP) Cathedral fire, a structural analysis was undertaken to provide decision support for architects in charge of diagnosis and repair operations. Due to the potential impact of the results on the renovation project, calculations on the vaults and walls of the monument were compared to increase their reliability. This article presents the 3 modelling strategies implemented: 2 discrete block-to-block 3D approaches (DEM and FEM) and 1 continuum 3D approach (FEM). The assumptions common to the 3 approaches are presented. They mainly concern the geometrical model, the thermal actions and a diagnosis methodology called the working point method, previously used by the authors on other Gothic vaults. Comparisons of the results with each other and with on-site deflection measurements should lead to validation of the calculation strategies. The analysis will highlight the strengths and weaknesses of the computational approaches and propose research perspectives. Future developments concern the determination of models of homogenized thermomechanical behaviour of masonry and the development of a new hybrid calculation tool taking advantage of the continuum and discontinuum approaches detailed in this article.

Machine learning has seen increasing implementation as a predictive tool in the chemical and physical sciences in recent years. It offers a route to accelerate the process of scientific discovery through a computational data-driven approach. Whilst machine learning is well established in other fields, such as pharmaceutical research, it is still in its infancy in supercritical fluids research, but will likely accelerate dramatically in coming years. In this review, we present a basic introduction to machine learning and discuss its current uses by supercritical fluids researchers. In particular, we focus on the most common machine learning applications; including: (1) The estimation of the thermodynamic properties of supercritical fluids. (2) The estimation of solubilities, miscibilities, and extraction yields. (3) Chemical reaction optimization. (4) Materials synthesis optimization. (5) Supercritical power systems. (6) Fluid dynamics simulations of supercritical fluids. (7) Molecular simulation of supercritical fluids and (8) Geosequestration of CO2 using supercritical fluids.

This work deals with the topology optimisation of structures made of multiple material phases. The proposed approach is based on non-uniform rational basis spline (NURBS) hyper-surfaces to represent the geometric descriptor related to each material phase composing the continuum, and an improved multiphase material interpolation (MMI) scheme to penalise the stiffness tensor of the structure. In this context, the problem is formulated in the most general case by considering inhomogeneous Neumann–Dirichlet boundary conditions and by highlighting the differences between two different problem formulations. The first one uses the work of applied forces and displacements as cost function and the resulting optimisation problem is not self-adjoint. The second one considers the generalised compliance (related to the total potential energy), and the resulting optimisation problem is self-adjoint. Moreover, the improved MMI scheme proposed here does not require the introduction of artificial filtering techniques to smooth the boundary of the topological descriptors of the material phases composing the structure. The effectiveness of the method is proven on both 2D and 3D problems. Specifically, an extensive campaign of numerical analyses is conducted to investigate the influence of the type of geometric descriptor, of the integer parameters involved in the definition of the NURBS entity, of the type of cost function, of the type of lightness requirement, of the number and type of material phases, of the applied boundary conditions on the optimised topology.

Fatigue specimens of a Ti-6Al-4V alloy containing internal artificial defects with controlled and reproducible size and shape have been produced. These defects systematically led to the initiation of a fatigue crack which propagation has been monitored in situ by synchrotron X-ray tomography during R=0.1 uniaxial fatigue tests at 20 Hz. The crack growth curves of the internal cracks have been obtained for 6 samples. Ex situ fatigue tests have been performed on samples submitted to a supplementary heat treatment or containing a defect put into contact with air. The results obtained tend to support the fact that internal fatigue cracks grow from the notch in a vacuum environment. On the fracture surfaces of samples containing an artificial defect not connected to air, two regions have been observed. They correspond to the Rough Area and the Fish Eye regions observed for internal cracks initiated from natural defects. The transition between those two regions takes place when the plastic radius size is equivalent to the grain size.

This paper presents an original multi-level optimisation method for the design of composite structures integrating a global–local approach based on higher-order theories to assess the responses of the structure at each scale. The method offers a good balance between accuracy and computational costs. Unlike multi-level strategies available in the literature, in the proposed approach there is a strong interaction between the steps of the optimisation process. The proposed method is articulated in two nested optimisation loops (outer and inner). The outer loop focuses on the macroscopic scale where the polar formalism is used to describe the laminate behaviour. The resolution of the outer loop is performed through a special metaheuristic algorithm. However, since requirements on local structural responses are evaluated on the most critical region of the structure (modelled through a higher-order theory) at the ply-level, for each solution of the outer loop, a nested genetic optimisation (inner loop) is performed to find the stack matching the values of the geometric variables and of the polar parameters corresponding to the current solution of the outer loop. During the inner loop, the optimised stacking sequences are searched in the domain of general quasi-trivial solutions, without introducing simplifying hypotheses. The new methodology is applied to the least-weight design of a simplified wing-box structure by considering requirements of both mechanical nature (first buckling load, first-ply failure, and delamination) and technological nature (blending between adjacent laminates).

This work deals with an experimental/numerical validation of the optimised topologies found through a special density-based topology optimisation (TO) method wherein the topological descriptor, i.e., the pseudo-density field, is represented through a non-uniform rational basis spline (NURBS) hyper-surface. The framework is that of multi-scale TO methods to design architected cellular materials (ACMs). Specifically, in the most general case, the topological variables are defined at the scale of the representative volume element (RVE) of the ACM and at the macroscopic scale of the structure. The transition among scales is performed via a numerical homogenisation scheme based on the strain energy of elements. The proposed formulation exploits the properties of NURBS entities to determine the relationships occurring among the topological variables defined at different scales to correctly state the optimisation problem and to satisfy the hypotheses at the basis of the homogenisation method. Three design cases are considered: in the first one, TO is performed only at the macroscopic scale; in the second one, TO is performed only at the RVE scale; in the last one, TO is performed simultaneously at both scales. Multiple design requirements related to lightness, scale separation condition (to ensure the validity of the results of the homogenisation method) and minimum printable size are included in the problem formulation. Particularly, the last two requirements are implicitly satisfied by controlling the integer parameters of the NURBS entity (describing the pseudo-density field at each scale) without introducing explicit optimisation constraints. The multi-scale TO strategy is applied to a structure made of ACM subject to three-point bending test-like boundary conditions: for each design case, the optimised topology is manufactured through stereo-lithography and a comparison between experimental and numerical results (obtained through non-linear analysis conducted a posteriori on the optimised topology) is performed to assess the effectiveness of the approach.