Error Reduction and Effect of Step Size in Adjustment Calculus for Cam Applications

Any measurement, however carefully done, will never be free from errors. Similarly, machining of cams for automobiles is prone to contain errors. These errors are naturally a part and parcel of cam manufacturing. The nature of deviations of the manufactured cam profile from the theoretical cam determines its usability. Sometimes, allowable deviations in high speed cams may be in the order of 2540 µm. Larger deviations will disqualify the cams for applications. Velocity and acceleration of the cam are estimated from the measured displacement of the cam follower during quality control implementation. This data helps in eliminating the unfit cams. Existing methods deal with a notorious challenge from propagation of measurement errors in the displacement data to predicted velocity and acceleration values. J. Oderfeld developed a little known method called ‘Adjustment Calculus’ which is an alternative method for this purpose. This method combines the ‘marching point’ method that fits a polynomial to discrete data and a symmetric Stirling interpolation method. Until now, adjustment calculus has been applied to reduce errors in acceleration data. In this work, adjustment calculus is implemented to velocity predictions. ‘Weights’ for calculation of adjusted velocity are derived using a cubic polynomial fit and symmetric Stirling interpolation formula. The effect of step size on application of adjustment calculus to different cam profiles is probed using the Monte Carlo method. Effective step size for practical applications in automotive cam quality control is suggested for each cam profile. Practical pointers for application to cam inspection for velocity and acceleration analysis are formulated. Adviser: Wieslaw M. Szydlowsk

Ultrasonic Bioreactor as a Platform for Studying Cellular Response

The need for tissue-engineered constructs as replacement tissue continues to grow as the average age of the world’s population increases. However, additional research is required before the efficient production of laboratory-created tissue can be realized. The multitude of parameters that affect cell growth and proliferation is particularly daunting considering that optimized conditions are likely to change as a function of growth. Thus, a generalized research platform is needed in order for quantitative studies to be conducted. In this article, an ultrasonic bioreactor is described for use in studying the response of cells to ultrasonic stimulation. The work is focused on chondrocytes with a long-term view of generating tissue-engineered articular cartilage. Aspects of ultrasound (US) that would negatively affect cells, including temperature and cavitation, are shown to be insignificant for the US protocols used and which cover a wide range of frequencies and pressure amplitudes. The bioreactor is shown to have a positive influence on several factors, including cell proliferation, viability, and gene expression of select chondrocytic markers. Most importantly, we show that a total of 138 unique proteins are differentially expressed on exposure to ultrasonic stimulation, using mass-spectroscopy coupled proteomic analyses. We anticipate that this work will serve as the basis for additional research which will elucidate many of the mechanisms associated with cell response to ultrasonic stimulation

Ultrasonic Bioreactor as a Platform for Studying Cellular Response

The need for tissue-engineered constructs as replacement tissue continues to grow as the average age of the world’s population increases. However, additional research is required before the efficient production of laboratory-created tissue can be realized. The multitude of parameters that affect cell growth and proliferation is particularly daunting considering that optimized conditions are likely to change as a function of growth. Thus, a generalized research platform is needed in order for quantitative studies to be conducted. In this article, an ultrasonic bioreactor is described for use in studying the response of cells to ultrasonic stimulation. The work is focused on chondrocytes with a long-term view of generating tissue-engineered articular cartilage. Aspects of ultrasound (US) that would negatively affect cells, including temperature and cavitation, are shown to be insignificant for the US protocols used and which cover a wide range of frequencies and pressure amplitudes. The bioreactor is shown to have a positive influence on several factors, including cell proliferation, viability, and gene expression of select chondrocytic markers. Most importantly, we show that a total of 138 unique proteins are differentially expressed on exposure to ultrasonic stimulation, using mass-spectroscopy coupled proteomic analyses. We anticipate that this work will serve as the basis for additional research which will elucidate many of the mechanisms associated with cell response to ultrasonic stimulation

Short communication: Landlab v2.0: a software package for Earth surface dynamics

umerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab

Short communication: Landlab v2.0: a software package for Earth surface dynamics

Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon-Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components - modular implementations of single physical processes - and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components - for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab.</p

Development of an open-source ecohydrology model using Landlab with applications in semi-arid landscapes

Thesis (Ph.D.)--University of Washington, 2020Ecosystems are in transition globally with critical societal consequences. Global warming, growing climatic extremes, land degradation, human-introduced herbivores, and climate-related disturbances (wildfires, diseases, insect outbreaks) drive rapid changes in ecosystem productivity and structure, with complex feedbacks in watershed hydrology, geomorphology and biogeochemistry. There is need to develop models that can represent ecosystem changes by incorporating the role of individual plant patches. In my research I developed ecohydrologic components in Landlab, an open source toolkit written in Python (http://landlab.github.io/#/), to study global change drivers in watersheds with emphasis on woody plant encroachment (WPE). I will first discuss the development of Landlab, its design, architecture, and illustrate examples of building models with Landlab. I will then present the development of ecohydrologic components and illustrate examples of coupling these components for simulating local soil moisture and plant dynamics with spatially explicit cellular automaton (CA)-based plant establishment, mortality, fire, and grazing processes. Several key features of arid and semiarid ecosystems will be discussed. Coexistence of tree-grass cover on north facing slopes (NFS) and shrub cover on south facing slopes (SFS) in central New Mexico is attributed to the competitive advantage of trees due to their longer seed dispersal range against shrubs in cooler and moist NFS. Incorporating a rule to represent inhibitory effects of shrubs on grasses enhance modeled shrub cover, while both trees and grasses are favored when runon is included in the local soil moisture model. Feedbacks among livestock grazing, grassland fire frequency and size, resource redistribution on woody plant encroachment are investigated using different ecohydrologic model configurations. These feedbacks have led to a three-phase woody plant expansion processes in the model, with rates of encroachment controlled by the state transition probabilities of vegetation types in relation to plant susceptibility to fires, grazing, and age-related mortality. A critical area of woody plant emerges in the model with which a negative feedback between fire size and woody plant expansion begins, providing a spatially-explict modelling evidence to the alternative stable states hypothesis. Finally, I investigate the transient ecosystem response to climate variability since the Late Pleistocene using paleoclimatic reconstructions of precipitation and temperature in central New Mexico, USA. The interplay between ecosystem state, change in climate, resultant grass connectivity, and hence fire frequency, and topography are explored with an ecohydrologic model discussed earlier. A transition from cool-wet climate to a warm-dry climate leads to shrub expansion due to drought-induced loss of grass connectivity. Shrubs dominate the ecosystem if dry conditions persist longer. Transition back to a tree or grass dominated ecosystem from shrub dominated ecosystem can only happen when climate shifts from dry to wet. The importance of length of dry or wet spells on ecosystem structure is highlighted. Aspect plays a critical role in providing topographical refugia for trees during dry periods and influences the rate of ecosystem transitions during climate change

Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics

The ability to model surface processes and to couple them to both subsurface and atmospheric regimes has proven invaluable to research in the Earth and planetary sciences. However, creating a new model typically demands a very large investment of time, and modifying an existing model to address a new problem typically means the new work is constrained to its detriment by model adaptations for a different problem. Landlab is an open-source software framework explicitly designed to accelerate the development of new process models by providing: (1) a set of tools and existing grid structures – including both regular and irregular grids – to make it faster and easier to develop new process components, or numerical implementations of physical processes; (2) a suite of stable, modular, and interoperable process components that can be combined to create an integrated model; and (3) a set of tools for data input, output, manipulation, and visualization. A set of example models built with these components is also provided. Landlab's structure makes it ideal not only for fully developed modelling applications, but also for model prototyping and classroom use. Because of its modular nature, it can also act as a platform for model intercomparison and epistemic uncertainty and sensitivity analyses. Landlab exposes a standardized model interoperability interface, and is able to couple to third party models and software. Landlab also offers tools to allow the creation of cellular automata, and allows native coupling of such models to more traditional continuous differential equation-based modules. We illustrate the principles of component coupling in Landlab using a model of landform evolution, a cellular ecohydrologic model, and a flood-wave routing model

Landlab: Plug-and-play numerical modeling of Earth-surface dynamics

Abstract:<br>Numerical models are widely used in the environmental sciences. Among these are the sciences that deal with the Earth's surface, including geomorphology, hydrology, sedimentology, glaciology, volcanology, and landscape ecology, among others. Although the scientific questions addressed by these diverse disciplines vary widely, in many cases the development of computational models involves similar programming problems: construction of a grid, calculation of geophysical flows across a 2D topographic surface, conservation of mass, input of parameters and initial conditions, output of calculations, and other tasks. Landlab is a Python-language programming library that takes advantage of these commonalities to help modelers develop, refine, and explore models more efficiently. Landlab provides four general capabilities: easy creation and configuration of a model grid (regular or irregular) and associated data arrays, encapsulation of simulation code into reusable components that can be coupled, a framework for constructing continuous-time stochastic cellular automata, and utilities for handling input, output, and topographic data preprocessing. Landlab is an element of the Community Surface Dynamics Modeling System (CSDMS), and is available at https://landlab.github.io.  <br><br>(Poster presented at NSF SI2 PI meeting, February 2017, Arlington, VA)<br