13,612 research outputs found
Learning macroscopic internal variables and history dependence from microscopic models
This paper concerns the study of history dependent phenomena in heterogeneous
materials in a two-scale setting where the material is specified at a fine
microscopic scale of heterogeneities that is much smaller than the coarse
macroscopic scale of application. We specifically study a polycrystalline
medium where each grain is governed by crystal plasticity while the solid is
subjected to macroscopic dynamic loads. The theory of homogenization allows us
to solve the macroscale problem directly with a constitutive relation that is
defined implicitly by the solution of the microscale problem. However, the
homogenization leads to a highly complex history dependence at the macroscale,
one that can be quite different from that at the microscale. In this paper, we
examine the use of machine-learning, and especially deep neural networks, to
harness data generated by repeatedly solving the finer scale model to: (i) gain
insights into the history dependence and the macroscopic internal variables
that govern the overall response; and (ii) to create a computationally
efficient surrogate of its solution operator, that can directly be used at the
coarser scale with no further modeling. We do so by introducing a recurrent
neural operator (RNO), and show that: (i) the architecture and the learned
internal variables can provide insight into the physics of the macroscopic
problem; and (ii) that the RNO can provide multiscale, specifically FE2,
accuracy at a cost comparable to a conventional empirical constitutive
relation
A computational framework for pharmaco-mechanical interactions in arterial walls using parallel monolithic domain decomposition methods
A computational framework is presented to numerically simulate the effects of
antihypertensive drugs, in particular calcium channel blockers, on the
mechanical response of arterial walls. A stretch-dependent smooth muscle model
by Uhlmann and Balzani is modified to describe the interaction of
pharmacological drugs and the inhibition of smooth muscle activation. The
coupled deformation-diffusion problem is then solved using the finite element
software FEDDLib and overlapping Schwarz preconditioners from the Trilinos
package FROSch. These preconditioners include highly scalable parallel GDSW
(generalized Dryja-Smith-Widlund) and RDSW (reduced GDSW) preconditioners.
Simulation results show the expected increase in the lumen diameter of an
idealized artery due to the drug-induced reduction of smooth muscle
contraction, as well as a decrease in the rate of arterial contraction in the
presence of calcium channel blockers. Strong and weak parallel scalability of
the resulting computational implementation are also analyzed
An investigation of entorhinal spatial representations in self-localisation behaviours
Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space.
Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour.
Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure.
Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Machine learning and mixed reality for smart aviation: applications and challenges
The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency
Resilience and food security in a food systems context
This open access book compiles a series of chapters written by internationally recognized experts known for their in-depth but critical views on questions of resilience and food security. The book assesses rigorously and critically the contribution of the concept of resilience in advancing our understanding and ability to design and implement development interventions in relation to food security and humanitarian crises. For this, the book departs from the narrow beaten tracks of agriculture and trade, which have influenced the mainstream debate on food security for nearly 60 years, and adopts instead a wider, more holistic perspective, framed around food systems. The foundation for this new approach is the recognition that in the current post-globalization era, the food and nutritional security of the world’s population no longer depends just on the performance of agriculture and policies on trade, but rather on the capacity of the entire (food) system to produce, process, transport and distribute safe, affordable and nutritious food for all, in ways that remain environmentally sustainable. In that context, adopting a food system perspective provides a more appropriate frame as it incites to broaden the conventional thinking and to acknowledge the systemic nature of the different processes and actors involved. This book is written for a large audience, from academics to policymakers, students to practitioners
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
Effect of Electromigration on Onset of Morphological Instability of a Nanowire
Solid cylindrical nanowires are vulnerable to a Rayleigh-Plateau-type
morphological instability. The instability results in a wire breakup, followed
by formation of a chain array of spherical nanoparticles. In this paper, a base
model of a morphological instability of a nanowire on a substrate in the
applied electric field directed along a nanowire axis is considered. Exact
analytical solution is obtained for 90 degrees contact angle and, assuming
axisymmetric perturbations, for a free-standing wire. The latter solution
extends the 1965 result by Nichols and Mullins without electromigration effect
(F.A. Nichols and W.W. Mullins, Trans. Metall. Soc. AIME 233, 1840-1848
(1965)). For general contact angles the neutral stability is determined
numerically. It is shown that a stronger applied electric field (a stronger
current) results in a larger instability growth rate and a decrease of the most
dangerous unstable wavelength; in experiment, the latter is expected to yield
more dense chain array of nanoparticles. Also it is noted that a wire
crystallographic orientation on a substrate has larger impact on stability in a
stronger electric field and that a simple switching of the polarity of
electrical contacts, i.e. the reversal of the direction of the applied electric
field, may suppress the instability development and thus a wire breakup would
be prevented. A critical value of the electric field that is required for such
wire stabilization is obtained
KYT2022 Finnish Research Programme on Nuclear Waste Management 2019–2022 : Final Report
KYT2022 (Finnish Research Programme on Nuclear Waste Management 2019–2022), organised by the Ministry of Economic Affairs and Employment, was a national research programme with the objective to ensure that the authorities have sufficient levels of nuclear expertise and preparedness that are needed for safety of nuclear waste management.
The starting point for public research programs on nuclear safety is that they create the conditions for maintaining the knowledge required for the continued safe and economic use of nuclear energy, developing new know-how and participating in international collaboration.
The content of the KYT2022 research programme was composed of nationally important research topics, which are the safety, feasibility and acceptability of nuclear waste management.
KYT2022 research programme also functioned as a discussion and information-sharing forum for the authorities, those responsible for nuclear waste management and the research organizations, which helped to make use of the limited research resources. The programme aimed to develop national research infrastructure, ensure the continuing availability of expertise, produce high-level scientific research and increase general knowledge of nuclear waste management
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