CORRELATION RELATIONSHIPS FOR THE HYPOPLASTIC MODEL FOR FINE GRAINED SOILS

The paper is concerned with our ongoing research effort devoted to the development of reliable computational tools for the calibration of advanced constitutive models of soils. At present, such software is available for the hypoplastic model of clays applicable to soft soils. This software provides a stepping stone for the determination of potential links between individual model parameters and fundamental characteristics of soils. Identifying such links would allow for tuning the model without performing time consuming experiments, particularly in the case of an initial design. Some preliminary results are presented in the paper.

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A Numerical Investigation of Aeroacoustic Fan Blade Flutter

Volume 4: Turbo Expo 2003 ◽

10.1115/gt2003-38454 ◽

2003 ◽

Cited By ~ 1

Author(s):

X. Wu ◽

M. Vahdati ◽

A. I. Sayma ◽

M. Imregun

Keyword(s):

Research Effort ◽

Pressure Rise ◽

Acoustic Properties ◽

Pressure Amplitude ◽

Ongoing Research ◽

Blade Flutter ◽

Fan Blade ◽

Underlying Mechanisms ◽

Flutter Margin

This paper reports the results of an ongoing research effort to explain the underlying mechanisms for aeroacoustic fan blade flutter. Using a 3D integrated aeroelasticity method and a single passage blade model that included a representation of the intake duct, the pressure rise vs. mass flow characteristic of a fan assembly was obtained for the 60%–80% speed range. A novel feature was the use of a downstream variable-area nozzle, an approach that allowed the determination of the stall boundary with good accuracy. The flutter stability was predicted for the 2 nodal diameter assembly mode arising from the first blade flap mode. The flutter margin at 64% speed was predicted to drop sharply and the instability was found to be independent of stall effects. On the other hand, the flutter instability at 74% speed was found to be driven by flow separation. Further post-processing of the results at 64% speed indicated significant unsteady pressure amplitude build-up inside the intake at the flutter condition, thus highlighting the link between the acoustic properties of the intake duct and fan blade flutter.

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Formulation of a Basic Constitutive Model for Fine - Grained Soils Using the Hypoplastic Framework

Civil and Environmental Engineering ◽

10.2478/cee-2021-0047 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Filip Gago ◽

Alessandro Valletta ◽

Juraj Mužík

Keyword(s):

Constitutive Models ◽

Constitutive Modelling ◽

Tensor Function ◽

Elastoplastic Model ◽

Soil Response ◽

Hypoplastic Model ◽

Material Parameters ◽

Fine Grained ◽

Wide Range ◽

Geotechnical Problems

Abstract A hypoplastic approach to constitutive modelling was developed by Kolymbas 1996 considering a non-linear tensor function in the form of strain and stress rate. However, the implicit formulation of the hypoplastic model with indirect material parameters severely limits its applicability to real-world geotechnical problems. In many cases, the numerical analysis of geotechnical problems relies on simple elastoplastic constitutive models that cannot model a wide range of soil response aspects. One promising paradigm of constitutive modelling in geotechnics is hypoplasticity, but many of the hypoplastic models belong to advanced models. In the article, we present the simple hypoplastic model as an alternative to the widely used Mohr-Coulomb elastoplastic model.

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Automated Parameter Determination of Advanced Constitutive Models

Volume 2: Computer Technology ◽

10.1115/pvp2005-71634 ◽

2005 ◽

Cited By ~ 6

Author(s):

Syed M. Rahman ◽

Tasnim Hassan ◽

S. Ranji Ranjithan

Keyword(s):

Heuristic Search ◽

Constitutive Models ◽

Cyclic Stress ◽

Cyclic Plasticity ◽

Parameter Determination ◽

Model Parameters ◽

Optimization Approach ◽

Analysis And Design ◽

Chaboche Model

Parameter determination of advanced cyclic plasticity models which are developed for simulation of cyclic stress-strain and ratcheting responses is complex. This is mainly because of the large number of model parameters which are interdependent and three or more experimental responses are used in parameter determination. Hence the manual trial and error approach becomes quite tedious and time consuming for determining a reasonable set of parameters. Moreover, manual parameter determination for an advanced plasticity model requires in-depth knowledge of the model and experience with its parameter determination. These are few of the primary reasons for advanced cyclic plasticity models not being widely used for analysis and design of fatigue critical structures. These problems could be overcome through developing an automated parameter optimization system using heuristic search technique (e.g. genetic algorithm). This paper discusses the development of such an automatic parameter determination scheme for improved Chaboche model developed by Bari and Hassan [4]. A new stepped GA optimization approach which is found to be more efficient over the conventional GA approach in terms of fitness quality and optimization time is presented.

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Force-State Characterization of Struts Using Pinned Joints

Shock and Vibration ◽

10.1155/1997/281867 ◽

1997 ◽

Vol 4 (2) ◽

pp. 103-113 ◽

Cited By ~ 1

Author(s):

Brook D. Ferney ◽

Steven L. Folkman

Keyword(s):

Finite Element ◽

Finite Element Model ◽

Research Effort ◽

Element Model ◽

Mapping Technique ◽

Model Parameters ◽

The Finite Element Model ◽

Pinned Joints

As part of a research effort to study the microgravity dynamics of a truss with pinned joints, a single strut with a single clevis-tang pinned joint was characterized. Experimental data was collected using a force-state mapping technique. The strut was subjected to axial dynamic loads and the response of the strut was measured. The force-state map aids visualization of the strut dynamics. Finite element modeling of the response was explored. An example is presented that uses a method of manual determination of the finite element model parameters. The finite element model results correspond well with the measured strut response.

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Mechanical properties of young concrete - Part II: Determination of model parameters and test program proposals

Materials and Structures ◽

10.1617/13836 ◽

2003 ◽

Vol 36 (258) ◽

pp. 226-230 ◽

Cited By ~ 3

Author(s):

T. Kanstad

Keyword(s):

Mechanical Properties ◽

Model Parameters ◽

Test Program ◽

Young Concrete ◽

Concrete Part

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Fertilizers. Determination of bulk density (loose) of fine-grained fertilizers

10.3403/02237278u ◽

2015 ◽

Keyword(s):

Bulk Density ◽

Fine Grained

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Mining Camera Traces to Estimate Interactions Between Healthcare Workers and Patients

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.484 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s12-s12

Author(s):

D. M. Hasibul Hasan ◽

Philip Polgreen ◽

Alberto Segre ◽

Jacob Simmering ◽

Sriram Pemmaraju

Keyword(s):

Time Of Day ◽

Microsoft Kinect ◽

Critical Parameters ◽

Model Parameters ◽

Patient Room ◽

University Of Iowa ◽

Velocity Change ◽

Fine Grained ◽

Visit Frequency ◽

Contact Patterns

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.Funding: NoneDisclosures: None

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Novel Treatments and Technologies Applied to the Cure of Neuroblastoma

Children ◽

10.3390/children8060482 ◽

2021 ◽

Vol 8 (6) ◽

pp. 482

Author(s):

Irene Paraboschi ◽

Laura Privitera ◽

Gabriela Kramer-Marek ◽

John Anderson ◽

Stefano Giuliani

Keyword(s):

Research Effort ◽

Treatment Strategies ◽

Adverse Outcomes ◽

Treatment Protocols ◽

Ongoing Research ◽

Hematopoietic Stem ◽

Novel Treatments ◽

Therapeutic Modalities ◽

High Risk Disease ◽

Novel Treatment Strategies

Neuroblastoma (NB) is the most common extracranial solid tumour in childhood, accounting for approximately 15% of all cancer-related deaths in the paediatric population1. It is characterised by heterogeneous clinical behaviour in neonates and often adverse outcomes in toddlers. The overall survival of children with high-risk disease is around 40–50% despite the aggressive treatment protocols consisting of intensive chemotherapy, surgery, radiation therapy and hematopoietic stem cell transplantation2,3. There is an ongoing research effort to increase NB’s cellular and molecular biology knowledge to translate essential findings into novel treatment strategies. This review aims to address new therapeutic modalities emerging from preclinical studies offering a unique translational opportunity for NB treatment.

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Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽

10.3390/s21041280 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1280

Author(s):

Hyeonseok Lee ◽

Sungchan Kim

Keyword(s):

Neural Networks ◽

Model Prediction ◽

Saliency Map ◽

Model Parameters ◽

Learning Capability ◽

Fine Grained ◽

Network Layers ◽

Saliency Maps ◽

Novel Approach ◽

Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

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