Low-Level Turbulence Forecasts From Fine-Scale Models

2014 ◽  
Author(s):  
Jeffrey E. Passner
Keyword(s):  
Fine Scale ◽  
Low Level ◽  
Scale Models

Fine-Scale Fire Spread in Pine Straw

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Daryn Sagel ◽  
Kevin Speer ◽  
Scott Pokswinski ◽  
Bryan Quaife
Keyword(s):  
Fire Spread ◽  
Small Scale ◽  
Natural Setting ◽  
Fine Scale ◽  
Prescribed Burn ◽  
Fire Dynamics ◽  
Rate Of Spread ◽  
Scale Models ◽  
Fire Front ◽  
Visible Images

Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.

Survival analysis at multiple scales for the modeling of track geometry deterioration

2017 ◽  
Vol 232 (3) ◽  
pp. 842-850 ◽  
Author(s):  
Negin Alemazkoor ◽  
Conrad J Ruppert ◽  
Hadi Meidani
Keyword(s):  
Survival Analysis ◽  
Multiple Scales ◽  
Fine Scale ◽  
Coarse Scale ◽  
Track Geometry ◽  
Time Period ◽  
Scale Models ◽  
Optimal Maintenance ◽  
Maintenance Decisions

Defects in track geometry have a notable impact on the safety of rail transportation. In order to make the optimal maintenance decisions to ensure the safety and efficiency of railroads, it is necessary to analyze the track geometry defects and develop reliable defect deterioration models. In general, standard deterioration models are typically developed for a segment of track. As a result, these coarse-scale deterioration models may fail to predict whether the isolated defects in a segment will exceed the safety limits after a given time period or not. In this paper, survival analysis is used to model the probability of exceeding the safety limits of the isolated defects. These fine-scale models are then used to calculate the probability of whether each segment of the track will require maintenance after a given time period. The model validation results show that the prediction quality of the coarse-scale segment-based models can be improved by exploiting information from the fine-scale defect-based deterioration models.

scholarly journals Spatiotemporal Variations in Mercury Bioaccumulation at Fine and Broad Scales for Two Freshwater Sport Fishes

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1625 ◽  
Author(s):  
Shyam Thomas ◽  
Stephanie Melles ◽  
Satyendra Bhavsar
Keyword(s):  
Climate Change ◽  
Environmental Factors ◽  
Northern Pike ◽  
Space Time ◽  
Fine Scale ◽  
Spatiotemporal Variations ◽  
Scale Models ◽  
Change Context ◽  
Mercury Bioaccumulation ◽  
Broad Scale

Bioaccumulation of mercury in sport fish is a complex process that varies in space and time. Both large-scale climatic as well as fine-scale environmental factors are drivers of these space-time variations. In this study, we avail a long-running monitoring program from Ontario, Canada to better understand spatiotemporal variations in fish mercury bioaccumulation at two distinct scales. Focusing on two common large-bodied sport fishes (Walleye and Northern Pike), the data were analyzed at fine- and broad-scales, where fine-scale implies variations in bioaccumulation at waterbody- and year-level and broad-scale captures variations across 3 latitudinal zones (~5° each) and eight time periods (~5-year each). A series of linear mixed-effects models (LMEMs) were employed to capture the spatial, temporal and spatiotemporal variations in mercury bioaccumulation. Fine-scale models were overall better fit than broad-scale models suggesting environmental factors operating at the waterbody-level and annual climatic conditions matter most. Moreover, for both scales, the space time interaction explained most of the variation. The random slopes from the best-fitting broad-scale model were used to define a bioaccumulation index that captures trends within a climate change context. The broad-scale trends suggests of multiple and potentially conflicting climate-driven mechanisms. Interestingly, broad-scale temporal trends showed contrasting bioaccumulation patterns—increasing in Northern Pike and decreasing in Walleye, thus suggesting species-specific ecological differences also matter. Overall, by taking a scale-specific approach, the study highlights the overwhelming influence of fine-scale variations and their interactions on mercury bioaccumulation; while at broad-scale the mercury bioaccumulation trends are summarized within a climate change context.

Testing plausible upper-mantle compositions using fine-scale models of the 410-km discontinuity

1999 ◽  
Vol 26 (11) ◽  
pp. 1641-1644 ◽  
Author(s):  
James B. Gaherty ◽  
Yanbin Wang ◽  
Thomas H. Jordan ◽  
Donald J. Weidner
Keyword(s):  
Upper Mantle ◽  
Fine Scale ◽  
Scale Models

scholarly journals Modeling the past, present, and future distributions of endangered white abalone (Haliotis sorenseni) to inform recovery efforts in California

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259716
Author(s):  
Jordan DiNardo ◽  
Kevin L. Stierhoff ◽  
Brice X. Semmens
Keyword(s):  
Population Monitoring ◽  
Independent Data ◽  
Fine Scale ◽  
Coarse Scale ◽  
California Department ◽  
Climate Conditions ◽  
Point Conception ◽  
Scale Models ◽  
In Captivity ◽  
National Marine Fisheries Service

White abalone (Haliotis sorenseni) was once commonly found in coastal waters of the Southern California Bight (SCB) and south to Punta Abreojos, Baja California, Mexico. During the 1970s, white abalone supported a commercial fishery, which reduced the population and resulted in the closure of the fishery in 1996. When population levels continued to decline, National Marine Fisheries Service (NMFS) listed the species as endangered under the Endangered Species Act. The California Department of Fish and Wildlife and NMFS began surveying the wild populations, propagating specimens in captivity, and protecting its seabed habitat. We modeled coarse-scale (17 x 17 km) historical (using fishery-dependent data [1955–1996]) and contemporary (using fishery-independent data [1996–2017]) distributions of white abalone throughout its historical domain using random forests and maximum entropy (MaxEnt), respectively, and its fine-scale (10 x 10 m) contemporary distribution (fishery-independent data) using MaxEnt. We also investigated potential outplanting habitat farther north under two scenarios of future climate conditions. The coarse-scale models identified potential regions to focus outplanting efforts within SCB while fine-scale models can inform population monitoring and outplanting activities in these particular areas. These models predict that areas north of Point Conception may become candidate outplant sites as seawater temperatures continue to rise in the future due to climate change. Collectively, these results provide guidance on the design and potential locations for experimental outplanting at such locations to ultimately improve methods and success of recovery efforts.

scholarly journals Spatiotemporal Variations in Mercury Bioaccumulation at Fine and Broad Scales for Two Freshwater Sport Fishes

2021 ◽  
Author(s):  
Shyam M. Thomas ◽  
Stephanie J. Melles ◽  
Satyendra P. Bhavsar
Keyword(s):  
Climate Change ◽  
Environmental Factors ◽  
Northern Pike ◽  
Space Time ◽  
Fine Scale ◽  
Spatiotemporal Variations ◽  
Scale Models ◽  
Change Context ◽  
Mercury Bioaccumulation ◽  
Broad Scale

Bioaccumulation of mercury in sport fish is a complex process that varies in space and time. Both large-scale climatic as well as fine-scale environmental factors are drivers of these space-time variations. In this study, we avail a long-running monitoring program from Ontario, Canada to better understand spatiotemporal variations in fish mercury bioaccumulation at two distinct scales. Focusing on two common large-bodied sport fishes (Walleye and Northern Pike), the data were analyzed at fine- and broad-scales, where fine-scale implies variations in bioaccumulation at waterbody- and year-level and broad-scale captures variations across 3 latitudinal zones (~5° each) and eight time periods (~5-year each). A series of linear mixed-effects models (LMEMs) were employed to capture the spatial, temporal and spatiotemporal variations in mercury bioaccumulation. Fine-scale models were overall better fit than broad-scale models suggesting environmental factors operating at the waterbody-level and annual climatic conditions matter most. Moreover, for both scales, the space time interaction explained most of the variation. The random slopes from the best-fitting broad-scale model were used to define a bioaccumulation index that captures trends within a climate change context. The broad-scale trends suggests of multiple and potentially conflicting climate-driven mechanisms. Interestingly, broad-scale temporal trends showed contrasting bioaccumulation patterns—increasing in Northern Pike and decreasing in Walleye, thus suggesting species-specific ecological differences also matter. Overall, by taking a scale-specific approach, the study highlights the overwhelming influence of fine-scale variations and their interactions on mercury bioaccumulation; while at broad-scale the mercury bioaccumulation trends are summarized within a climate change context.

scholarly journals Spatiotemporal Variations in Mercury Bioaccumulation at Fine and Broad Scales for Two Freshwater Sport Fishes

2021 ◽  
Author(s):  
Shyam M. Thomas ◽  
Stephanie J. Melles ◽  
Satyendra P. Bhavsar
Keyword(s):  
Climate Change ◽  
Environmental Factors ◽  
Northern Pike ◽  
Space Time ◽  
Fine Scale ◽  
Spatiotemporal Variations ◽  
Scale Models ◽  
Change Context ◽  
Mercury Bioaccumulation ◽  
Broad Scale

Bioaccumulation of mercury in sport fish is a complex process that varies in space and time. Both large-scale climatic as well as fine-scale environmental factors are drivers of these space-time variations. In this study, we avail a long-running monitoring program from Ontario, Canada to better understand spatiotemporal variations in fish mercury bioaccumulation at two distinct scales. Focusing on two common large-bodied sport fishes (Walleye and Northern Pike), the data were analyzed at fine- and broad-scales, where fine-scale implies variations in bioaccumulation at waterbody- and year-level and broad-scale captures variations across 3 latitudinal zones (~5° each) and eight time periods (~5-year each). A series of linear mixed-effects models (LMEMs) were employed to capture the spatial, temporal and spatiotemporal variations in mercury bioaccumulation. Fine-scale models were overall better fit than broad-scale models suggesting environmental factors operating at the waterbody-level and annual climatic conditions matter most. Moreover, for both scales, the space time interaction explained most of the variation. The random slopes from the best-fitting broad-scale model were used to define a bioaccumulation index that captures trends within a climate change context. The broad-scale trends suggests of multiple and potentially conflicting climate-driven mechanisms. Interestingly, broad-scale temporal trends showed contrasting bioaccumulation patterns—increasing in Northern Pike and decreasing in Walleye, thus suggesting species-specific ecological differences also matter. Overall, by taking a scale-specific approach, the study highlights the overwhelming influence of fine-scale variations and their interactions on mercury bioaccumulation; while at broad-scale the mercury bioaccumulation trends are summarized within a climate change context.

Substituting of a Thermodynamic Simulation with a Metamodel in the Scope of Multiscale Modelling

2016 ◽  
Vol 879 ◽  
pp. 1207-1212 ◽  
Author(s):  
Piotr Macioł ◽  
Danuta Szeliga ◽  
Łukasz Sztangret
Keyword(s):  
Thermodynamic Simulation ◽  
Multiscale Simulation ◽  
Scale Model ◽  
Precipitation Kinetic ◽  
Fine Scale ◽  
Coarse Scale ◽  
Computational Point ◽  
Computing Power ◽  
Thermodynamic Computations ◽  
Scale Models

A typical multiscale simulation consists of numerous fine scale models, usually one for each computational point of a coarse scale model. One of possible ways of limiting computing power requirements is replacing fine scale models with some simplified and speeded up ersatz ones. In this paper, the authors attempt to develop a metamodel, replacing direct thermodynamic computations of precipitation kinetic with an advanced approximating model. MatCalc simulator has been used for thermodynamic modelling of precipitation kinetic. Typical heat treatment of P91 steel grade was examined. Selected variables were chosen to be modelled with approximating models. Several attempts with various approximation variants (interpolation algorithms and Artificial Neural Networks) have been investigated and its comparison is included in the paper.

Downscaling seismic tomography models

2021 ◽  
Author(s):  
Navid Hedjazian ◽  
Thomas Bodin ◽  
Yann Capdeville
Keyword(s):  
Seismic Imaging ◽  
Waveform Inversion ◽  
Effective Medium ◽  
Scale Model ◽  
Multiscale Approach ◽  
Full Waveform Inversion ◽  
Fine Scale ◽  
Full Waveform ◽  
Macro Scale ◽  
Scale Models

<p>Seismic imaging techniques such as elastic full waveform inversion (FWI) have their spatial resolution limited by the maximum frequency present in the observed waveforms. Scales smaller than a fraction of the minimum wavelength cannot be resolved, only a smoothed version of the true underlying medium can be recovered. Application of FWI to media containing small and strong heterogeneities therefore remains problematic. This smooth tomographic image is related to the effective elastic properties, which can be exposed with the homogenization theory of wave propagation. We study how this theory can be used in the FWI context. The seismic imaging problem is broken down in a two-stage multiscale approach. In the first step, called homogenized full waveform inversion (HFWI), observed waveforms are inverted for a macro-scale, fully anisotropic effective medium, smooth at the scale of the shortest wavelength present in the wavefield. The solution being an effective medium, it is difficult to directly interpret it. It requires a second step, called downscaling, where the macro-scale image is used as data, and the goal is to recover micro-scale parameters. All the information contained in the waveforms is extracted in the HFWI step. The solution of the downscaling step is highly non-unique as many fine-scale models may share the same long wavelength effective properties. We therefore rely on the introduction of external a priori information. In this step, the forward theory is the homogenization itself. It is computationally cheap, allowing to consider geological models with more complexity.</p><p>In a first approach to downscaling, the ensemble of potential fine-scale models is described with an object-based parametrization, and explored with a MCMC algorithm. We illustrate the method with a synthetic cavity detection problem. In a second approach, the prior information is introduced by the means of a training image, and the fine-scale model is recovered with a multi-point statistics algorithm. We apply this method on a subsurface synthetic problem, where the goal is to recover geological facies.</p><p> </p>

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