Low cost 3D global instability analysis and flow sensitivity based on dynamic mode decomposition and high-order numerical tools

We explore the recently developed snapshot-based dynamic mode decomposition (DMD) technique, a matrix-free Arnoldi type method, to predict 3D linear global flow instabilities. We apply the DMD technique to flows confined in an L-shaped cavity and compare the resulting modes to their counterparts issued from classic, matrix forming, linear instability analysis (i.e. BiGlobal approach) and direct numerical simulations. Results show that the DMD technique, which uses snapshots generated by a 3D non-linear incompressible discontinuous Galerkin Navier?Stokes solver, provides very similar results to classical linear instability analysis techniques. In addition, we compare DMD results issued from non-linear and linearised Navier?Stokes solvers, showing that linearisation is not necessary (i.e. base flow not required) to obtain linear modes, as long as the analysis is restricted to the exponential growth regime, that is, flow regime governed by the linearised Navier?Stokes equations, and showing the potential of this type of analysis based on snapshots to general purpose CFD codes, without need of modifications. Finally, this work shows that the DMD technique can provide three-dimensional direct and adjoint modes through snapshots provided by the linearised and adjoint linearised Navier?Stokes equations advanced in time. Subsequently, these modes are used to provide structural sensitivity maps and sensitivity to base flow modification information for 3D flows and complex geometries, at an affordable computational cost. The information provided by the sensitivity study is used to modify the L-shaped geometry and control the most unstable 3D mode.

Numerical enclosures of the optimal cost of the Kantorovitch’s mass transportation problem

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Viability of irrigation by porous capsule method in arid and semi-arid regions.

This paper presents the results of four field experiments carried out with the objetive of evaluating the feaibility of irrigation by porous capsule method, from 1979 to 1983, at Bebedouro Experiment Station, EMBRAPA-CPATSA, Petrolina, PE, Brazil. The irrigation system consisted of fulcrum of cone shaped porous capsules, interconnected with conduit pipe and installed in the soil at equidistance and 0.10 m deep along contour lines. The hydrostatic pressures studies did not significantly influence the crop yield, but influenced, at 0.10 level, the daily water release from porous unit. The mean yields for watermelon (Citrullus vulgaris Shard), var. Charleston Gray, for muskmelon (Cucumis melo L.) var. Valenciano Amarelo, and for maize (Zea mays L.), var. Centralmex, estimated in ton/2,500 units/ha or in cobs/2,500 units/ha, were 28.5, 10 and 17,500, respectively. The water consumption for watermelon, musk melon and maize was, respectively, 60 mm, 60 mm and 100 mm in a deep sandy yellow-red latosol. The cost of the system was US$ 1.677,41/ha.

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

A COST-BENEFIT ANALYSIS OF A 25% RPS IN MICHIGAN

Michigan depends heavily on fossil fuels to generate electricity. Compared with fossil fuels, electricity generation from renewable energy produces less pollutants emissions. A Renewable Portfolio Standard (RPS) is a mandate that requires electric utilities to generate a certain amount of electricity from renewable energy sources. This thesis applies the Cost-Benefits Analysis (CBA) method to investigate the impacts of implementing a 25% in Michigan by 2025. It is found that a 25% RPS will create about $20.12 billion in net benefits to the State. Moreover, if current tax credit policies will not change until 2025, its net present value will increase to about $26.59 billion. Based on the results of this CBA, a 25% RPS should be approved. The result of future studies on the same issue can be improved if more state specific data become available.

Personal notions of time travel: reflections on love, loss, and growth through autoethnography

Using the concept of time travel as a contextual and narrative tool, the author explores themes of love, loss and growth after trauma. Reflections relate primarily to the experience of conducting the qualitative research method of autoethnography. Opening with consideration of existing work (Yoga and Loss: An Autoethnographical Exploration of Grief, Mind, and Body), discussion moves on to academic thought on mental time travel, and personal transformation, culminating in the construction of a new memory combining past, present, and future.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.