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  • 1
    Electronic Resource
    Electronic Resource
    IMPACTS OF AGRICULTURAL DRAINAGE WELL CLOSURE ON CROP PRODUCTION: A WATERSHED CASE STUDY (1994)
    Oxford, UK : Blackwell Publishing Ltd
    Journal of the American Water Resources Association 30 (1994), S. 0 
    Details
    ISSN: 1752-1688
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Notes: : Much of north-central Iowa is characterized by flat topography, shallow depressions, and poor natural surface drainage. Land drainage systems comprising of tile drains and agricultural drainage wells (ADWs) are used as outlets for subsurface drainage of cropland under corn and soybean production. Studies have shown that these drainage systems, mainly the ADWs, are potential routes for agricultural chemicals to underground aquifers. To protect the region's vital groundwater resource, researchers are evaluating alternative outlets ranging from complete closure of existing ADWs (and creation of wetlands) to continued use of ADWs and chemical management in a comprehensive policy framework.This paper presents the results of a study designed to provide government jurisdictions, farmers, and land managers information for assessing the impact of closing ADWs on crop production. The study couples a geographic information systems database for a 471-hectare watershed in Humboldt County, Iowa, with a groundwater flow model (MODFLOW) and an empirical crop yield loss model to predict long-term effects of complete closure of ADWs on crop production. The cropland areas inundated and the relative crop yield loss due to ADW closure are determined as a function of long-term climatic data. The results indicate that elimination of drainage outlets in the watershed could result in ponding of low-lying areas and poorly drained soils, making them unsuitable for crop production. Such wetness also decreases the efficiency of production in the no-ponding areas by isolating fields, and the crop yield loss can be reduced by an annual average of about 18 percent.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1752-1688.1994.tb03323.x
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  • 2
    Electronic Resource
    Electronic Resource
    Numerical determination of minimum mass structures with specified natural frequenciesThis paper was presented at ASME Energy Technology Conference and Exhibition, Session on Structural Optimization Methods, Houston, Texas, 1977. This research was supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-76-3075. The Authors are indebted to Dr. V.B. Venkayya, Wright-Patterson A.F.B., Ohio, for suggesting the topic and stimulating discussion. This paper is a condensation of the investigations presented in References 1-3. (1978)
    Chichester [u.a.] : Wiley-Blackwell
    International Journal for Numerical Methods in Engineering 13 (1978), S. 265-282 
    Details
    ISSN: 0029-5981
    Keywords: Engineering ; Engineering General
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Mathematics , Technology
    Notes: The problem of the axial vibration of a cantilever beam is investigated both analytically and numerically. The mass distribution that minimizes the total mass for a given value of the frequency parameter β is determined using both the sequential ordinary gradient-restoration algorithm (SOGRA) and the modified quasilinearization algorithm (MQA). Concerning the minimum value of the mass, SOGRA leads to a solution precise to at least 4 significant digits and MQA leads to a solution precise to at least 6 significant digits.Comparison of the optimal beam (a variable-section beam) with a reference beam (a constant-section beam) shows that the weight reduction depends strongly on the frequency parameter β. This weight reduction is negligible for β → 0, is 11.3 per cent for β = 1, is 55.3 per cent for β = 1.4, and approaches 100 per cent for β → π/2.
    Additional Material: 16 Tab.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/nme.1620130205
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  • 3
    Electronic Resource
    Electronic Resource
    Numerical solution of minimax problems of optimal control, part 2 (1982)
    Springer
    Journal of optimization theory and applications 38 (1982), S. 111-135 
    Details
    ISSN: 1573-2878
    Keywords: Minimax problems ; minimax function ; minimax function depending on the state ; minimax function depending on the control ; optimal control ; minimax optimal control ; numerical methods ; computing methods ; transformation techniques ; gradient-restoration algorithms ; sequential gradient-restoration algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In a previous paper (Part 1), we presented general transformation techniques useful to convert minimax problems of optimal control into the Mayer-Bolza problem of the calculus of variations [Problem (P)]. We considered two types of minimax problems: minimax problems of Type (Q), in which the minimax function depends on the state and does not depend on the control; and minimax problems of Type (R), in which the minimax function depends on both the state and the control. Both Problem (Q) and Problem (R) can be reduced to Problem (P). In this paper, the transformation techniques presented in Part 1 are employed in conjunction with the sequential gradient-restoration algorithm for solving optimal control problems on a digital computer. Both the single-subarc approach and the multiple-subarc approach are employed. Three test problems characterized by known analytical solutions are solved numerically. It is found that the combination of transformation techniques and sequential gradient-restoration algorithm yields numerical solutions which are quite close to the analytical solutions from the point of view of the minimax performance index. The relative differences between the numerical values and the analytical values of the minimax performance index are of order 10−3 if the single-subarc approach is employed. These relative differences are of order 10−4 or better if the multiple-subarc approach is employed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00934326
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  • 4
    Electronic Resource
    Electronic Resource
    Numerical solution of minimax problems of optimal control, part 1 (1982)
    Springer
    Journal of optimization theory and applications 38 (1982), S. 97-109 
    Details
    ISSN: 1573-2878
    Keywords: Minimax problems ; minimax function ; minimax function depending on the state ; minimax function depending on the control ; optimal control ; minimax optimal control ; numerical methods ; computing methods ; transformation techniques ; gradient-restoration algorithms ; sequential gradient-restoration algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper contains general transformation techniques useful to convert minimax problems of optimal control into the Mayer-Bolza problem of the calculus of variations [Problem (P)]. We consider two types of minimax problems: minimax problems of Type (Q), in which the minimax function depends on the state and does not depend on the control; and minimax problems of Type (R), in which the minimax function depends on both the state and the control. Both Problem (Q) and Problem (R) can be reduced to Problem (P). For Problem (Q), we exploit the analogy with a bounded-state problem in combination with a transformation of the Jacobson type. This requires the proper augmentation of the state vectorx(t), the control vectoru(t), and the parameter vector π, as well as the proper augmentation of the constraining relations. As a result of the transformation, the unknown minimax value of the performance index becomes a component of the parameter vector being optimized. For Problem (R), we exploit the analogy with a bounded-control problem in combination with a transformation of the Valentine type. This requires the proper augmentation of the control vectoru(t) and the parameter vector π, as well as the proper augmentation of the constraining relations. As a result of the transformation, the unknown minimax value of the performance index becomes a component of the parameter vector being optimized. In a subsequent paper (Part 2), the transformation techniques presented here are employed in conjunction with the sequential gradient-restoration algorithm for solving optimal control problems on a digital computer; both the single-subarc approach and the multiple-subarc approach are discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00934325
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