NASA Scientific and Technical Aerospace Reports

20040112056 NASA Langley Research Center, Hampton, VA, USA
Parametric Cost Analysis: A Design Function
Dean, Edwin B.; [1989]; 12 pp.; In English; Transaction of the American Association of Cost Engineers 33rd Annual Meeting,
25-28 Jun.1989, San Diego, CA, USA; Original contains color illustrations; No Copyright; Avail: CASI; A03, Hardcopy
Parametric cost analysis uses equations to map measurable system attributes into cost. The measures of the system
attributes are called metrics. The equations are called cost estimating relationships (CER’s), and are obtained by the analysis
of cost and technical metric data of products analogous to those to be estimated. Examples of system metrics include mass,
power, failure_rate, mean_time_to_repair, energy _consumed, payload_to_orbit, pointing_accuracy,
manufacturing_complexity, number_of_fasteners, and percent_of_electronics_weight. The basic assumption is that a
measurable relationship exists between system attributes and the cost of the system. If a function exists, the attributes are cost
drivers. Candidates for metrics include system requirement metrics and engineering process metrics. Requirements are
constraints on the engineering process. From optimization theory we know that any active constraint generates cost by not
permitting full optimization of the objective. Thus, requirements are cost drivers. Engineering processes reflect a projection
of the requirements onto the corporate culture, engineering technology, and system technology. Engineering processes are an
indirect measure of the requirements and, hence, are cost drivers.
Author
Cost Estimates; Functional Analysis; Functions (Mathematics); Costs
20040121015 NASA Marshall Space Flight Center, Huntsville, AL, USA
Analytical Modeling and Test Correlation of Variable Density Multilayer Insulation for Cryogenic Storage
Hastings, L. J.; Hedayat, A.; Brown, T. M.; May 2004; 46 pp.; In English
Report No.(s): NASA/TM-2004-213175; M-1109; No Copyright; Avail: CASI; A03, Hardcopy
A unique foam/multilayer insulation (MLI) combination concept for orbital cryogenic storage was experimentally
evaluated using a large-scale hydrogen tank. The foam substrate insulates for ground-hold periods and enables a gaseous
nitrogen purge as opposed to helium. The MLI, designed for an on-orbit storage period for 45 days, includes several unique
features including a variable layer density and larger but fewer perforations for venting during ascent to orbit. Test results with
liquid hydrogen indicated that the MLI weight or tank heat leak is reduced by about half in comparison with standard MLI.
The focus of this effort is on analytical modeling of the variable density MLI (VD-MLI) on-orbit performance. The
foam/VD-MLI model is considered to have five segments. The first segment represents the optional foam layer. The second,
third, and fourth segments represent three different MLI layer densities. The last segment is an environmental boundary or
shroud that surrounds the last MLI layer. Two approaches are considered: a variable density MLI modeled layer by layer and
a semiempirical model or ‘modified Lockheed equation.’ Results from the two models were very comparable and were within
5-8 percent of the measured data at the 300 K boundary condition.
Author
Mathematical Models; Cryogenic Equipment; Multilayer Insulation
20040121065 NASA Langley Research Center, Hampton, VA, USA
The Design-To-Cost Manifold
Dean, Edwin B.; [1990]; 13 pp.; In English; International Academy of Astronautics Symposium on Space Systems Cost
Methodologies and Applications, 10-11 May 1990, San Diego, CA, USA; Original contains black and white illustrations; No
Copyright; Avail: CASI; A03, Hardcopy
Design-to-cost is a popular technique for controlling costs. Although qualitative techniques exist for implementing design
to cost, quantitative methods are sparse. In the launch vehicle and spacecraft engineering process, the question whether to
minimize mass is usually an issue. The lack of quantification in this issue leads to arguments on both sides. This paper presents
a mathematical technique which both quantifies the design-to-cost process and the mass/complexity issue. Parametric cost
analysis generates and applies mathematical formulas called cost estimating relationships. In their most common forms, they
are continuous and differentiable. This property permits the application of the mathematics of differentiable manifolds.
Although the terminology sounds formidable, the application of the techniques requires only a knowledge of linear algebra
and ordinary differential equations, common subjects in undergraduate scientific and engineering curricula. When the cost c
is expressed as a differentiable function of n system metrics, setting the cost c to be a constant generates an n-1 dimensional
subspace of the space of system metrics such that any set of metric values in that space satisfies the constant design-to-cost
criterion. This space is a differentiable manifold upon which all mathematical properties of a differentiable manifold may be
applied. One important property is that an easily implemented system of ordinary differential equations exists which permits
optimization of any function of the system metrics, mass for example, over the design-to-cost manifold. A dual set of equations
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