Investigation to develop a multistage forest sampling inventory system using ERTS-1 imagery

The author has identified the following significant results. The annotation system produced a RMSE of about 200 m ground distance in the MSS data system with the control data used. All the analytical MSS interpretation models tried were highly significant. However, the gains in forest sampling efficiency that can be achieved by using the models vary from zero to over 50 percent depending on the area to which they are applied and the sampling method used. Among the sampling methods tried, regression sampling yielded substantial and the most consistent gains. The single most significant variable in the interpretation model was the difference between bands 5 and 7. The contrast variable, computed by the Hadamard transform was significant but did not contribute much to the interpretation model. Forest areas containing very large timber volumes because of large tree sizes were not separable from areas of similar crown cover but containing smaller trees using ERTS image interpretation only. All correlations between space derived timber volume predictions and estimates obtained from aerial and ground sampling were relatively low but significant and stable. There was a much stronger relationship between variables derived from MSS and U2 data than between U2 and ground data

Three-dimensional turbopump flowfield analysis

A program was conducted to develop a flow prediction method applicable to rocket turbopumps. The complex nature of a flowfield in turbopumps is described and examples of flowfields are discussed to illustrate that physics based models and analytical calculation procedures based on computational fluid dynamics (CFD) are needed to develop reliable design procedures for turbopumps. A CFD code developed at NASA ARC was used as the base code. The turbulence model and boundary conditions in the base code were modified, respectively, to: (1) compute transitional flows and account for extra rates of strain, e.g., rotation; and (2) compute surface heat transfer coefficients and allow computation through multistage turbomachines. Benchmark quality data from two and three-dimensional cascades were used to verify the code. The predictive capabilities of the present CFD code were demonstrated by computing the flow through a radial impeller and a multistage axial flow turbine. Results of the program indicate that the present code operated in a two-dimensional mode is a cost effective alternative to full three-dimensional calculations, and that it permits realistic predictions of unsteady loadings and losses for multistage machines

ESTIMATING THE VALUE OF SEQUENTIAL UPDATING SOLUTIONS FOR INTRAYEAR CROP MANAGEMENT

Results of comparing updating versus nonupdating modeling assumptions call into question the use of models based on nonupdating strategies as valid representations of actual farmer actions. If farmers are sequential updaters, the results indicate that models assuming no updating are inaccurate. The degree of this inaccuracy ranges between 4% and 10% of profits for the study area. Further, the results indicate that updating appears to be important for both descriptive and prescriptive studies of farmer behavior.Crop Production/Industries,

THERMODYNAMICS OF DEVELOPMENT OF ENERGY SYSTEMS WITH APPLICATIONS TO THERMAL MACHINES AND LIVING ORGANISMS

We define and analyse thermodynamic limits for various traditional and work-assisted processes of sequential development with finite rates important in engineering and biology. The thermodynamic limits are expressed in terms of classical exergy change and a residual minimum of dissipated exergy, or some extension including time penalty. We consider processes with heat and mass transfer that occur in a finite time and with equipment of finite dimension. These processes include heat and separation operations and are found in heat and mass exchangers, thermal networks, energy converters, energy recovery units, storage systems, chemical reactors, and chemical plants. Our analysis is based on the condition that in order to make the results of thermodynamic analyses usable in engineering economics it is the thermodynamic limit, not the maximum of thermodynamic efficiency, which must be overcome for prescribed process requirements. A creative part of this paper outlines a general approach to the construction of `Carnot variables´ as suitable controls. Finite-rate, endoreversible models include minimal irreducible losses caused by thermal resistances to the classical exergy potential. Functions of extremum work, which incorporate residual minimum entropy production, are formulated in terms of initial and final states, total duration and (in discrete processes) number of stages

Winners and losers from Johne’s disease eradication from the Scottish dairy herd: a Markov-Chain simulation

In this paper, we evaluated the welfare effects of a hypothetical programme of Johne's disease eradication from the Scottish dairy herd on different stakeholders in the domestic milk market. We undertook the evaluation using a Markov-Chain simulation and applying an economic welfare analysis which takes into consideration the effects of an eradication programme on product price, on output quantity, on cost and on milk yield for given levels of supply and demand elasticities. We found that, following the eradication of the disease, milk yield per cow increased for all herd sizes in Scotland whereas price and unit cost of milk production fell. Consequently, milk consumers gained around £14.3 million in discounted economic surplus and producers with infected herds around £13.4 million whereas producers with uninfected herds lost around £10.7 million in discounted surplus. The gain in surplus made by consumers and owners of infected herds, however, more than made up for the loss in surplus made by owners of un-infected herds. Therefore, on balance, Scotland gained a net economic surplus of £17 million from the programme.Johne's, eradication programme, economic welfare effects, economic surplus, I180,

Solution methods and bounds for two-stage risk-neutral and multistage risk-averse stochastic mixed-integer programs with applications in energy and manufacturing

This dissertation presents an integrated method for solving stochastic mixed-integer programs, develops a lower bounding approach for multistage risk-averse stochastic mixed-integer programs, and proposes an optimization formulation for mixed-model assembly line sequencing (MMALS) problems. It is well known that a stochastic mixed-integer program is difficult to solve due to its non-convexity and stochastic factors. The scenario decomposition algorithms display computational advantage when dealing with a large number of possible realizations of uncertainties, but each has its own advantages and disadvantages. This dissertation presents a solution method for solving large-scale stochastic mixed-integer programs that integrates two scenario-decomposition algorithms: Progressive Hedging (PH) and Dual Decomposition (DD). In this integrated method, fast progress in early iterations of PH speeds up the convergence of DD to an exact solution. In many applications, the decision makers are risk-averse and are more concerned with large losses in the worst scenarios than with average performance. The PH algorithm can serve as a time-efficient heuristic for risk-averse stochastic mixed-integer programs with many scenarios, but the scenario reformulation for time consistent multistage risk-averse models does not exist. This dissertation develops a scenario-decomposed version of time consistent multistage risk-averse programs, and proposes a lower bounding approach that can assess the quality of PH solutions and thus identify whether the PH algorithm is able to find near-optimal solutions within a reasonable amount of time. The existing optimization formulations for MMALS problems do not consider many real-world uncertainty factors such as timely part delivery and material quality. In addition, real-time sequencing decisions are required to deal with inevitable disruptions. This dissertation formulates a multistage stochastic optimization problem with part availability uncertainty. A risk-averse model is further developed to guarantee customers’ satisfaction regarding on-time performance. Computational studies show that the integration of PH helps DD to reduce the run-time significantly, and the lower bounding approach can obtain convergent and tight lower bounds to help PH evaluate quality of solutions. The PH algorithm and the lower bounding approach also help the proposed MMALS formulation to make real-time sequencing decisions