The concept of artificial intelligence as it applies to computational fluid dynamics simulation is investigated. How expert systems can be adapted to speed the numerical aerodynamic simulation process is also examined. A proposed expert grid generation system is briefly described which, given flow parameters, configuration geometry, and simulation constraints, uses knowledge about the discretization process to determine grid point coordinates, computational surface information, and zonal interface parameters

Andrews, A.

Kutler, P.

Mehta, U. B.

English

NASA Technical Reports Server

NASA Technical Memorandum 85976 
NASA-TM-8597619840023023 
Potential Application of Artificial 
Intelligence Concepts to 
Numerical Aerodynamic 
Simulation 
Paul Kutler, Unmeel B. Mehta and Alison Andrews 
June 1984 
NI\S/\ 
National Aeronautics and 
Space Administration 
t': rn IN V'r'G:rJ'A 
111111111 111111111111111111111111111111111111 
NF00831 
https://ntrs.nasa.gov/search.jsp?R=19840023023 2020-03-20T22:42:57+00:00Z
NASA Technical Memorandum 85976 
Potential Application of Artificial 
Intelligence Concepts to 
Numerical Aerodynamic 
Simulation 
Paul Kutler 
Unmeel B Mehta 
Alison Andrews, Ames Research Center, Moffett Field, California 
NI\S/\ 
National Aeronautics and 
Space Administration 
Ames Research Center 
Moffett Field California 94035 
POTENTIAL APPLICATION OF ARTIFICIAL INTELLIGENCE CONCEPTS 
TO NUMERICAL AERODYNAMIC SIMULATION 
Paul Kutler, Unmeel B. Mehta. and Alison Andrews 
NASA Ames Research Center 
Moffett Field. CA 94035 USA 
I. INTRODUCTION 
The development of sophisticated computational fluid dynamic (CFD) tools for 
simulating the external flow field about complicated three-dimensional flight 
veh1cles or internal flows within vehicle components requires vast expertise and 
enormous resources in terms of both human researchers and computer capacity (speed 
and storage). The creation of such simulation tools requires knowledge of the 
disciplines of numerical analysis. fluid dynamics, computer science, and aerodynam-
1CS, and the development of such tools takes an inordinate amount of time. Further-
more, the wr1ting of software is becoming more expensive every year [1,2]. Shorter. 
less expensive development times resulting in more powerful. versatile, easy-to-use, 
and easy-to-1nterpret simulation tools are necessary if computational aerodynamics 
1S to fulfill 1ts potential in the vehicle design process. To this end. some of the 
concepts of artif1c1al 1ntelligence (AI) can be applied. It is the purpose of this 
paper, first, to br1efly introduce these concepts and. second, to indicate how some 
of these concepts can be adapted to speed the numerical aerodynamic simulation 
process. 
II. ARTIFICIAL INTELLIGENCE BACKGROUND 
Art1ficial intelligence is a discipline of computer science concerned with the 
study of symbo11c reasoning by a computer and symbolic representation of knowledge. 
The objective of applied AI is to design and construct computer programs that exh1b1t 
the characterist1cs normally associated with human intelligence (for example. per-
formance, adaptab1lity. and self-knowledge). The core elements of artificial intel-
ligence are (1) heurist1c search (rules of thumb to gU1de the search of the problem's 
solut10n space, as opposed to blind. exhaustive search, or an algorithmic solution 
procedure); (2) symbolic representation (representing knowledge by means of first-
order predicate calculus or frames, for example); and (3) symbolic inference (methods 
of man1pulating symbols to do reasoning). Research in these core areas is conducted 
through the study of such topics as natural language processing. formal theorem 
proving, concept learning, automatic programming, robot control, computer vision/ 
perception, and problem solving and planning. Formal approaches to this research 
(using a formal, unambiguous language for representing facts and ideas, and a formal 
logic to reason about those ideas) have performed successfully on some rather simple 
problems. However, the elusiveness of high-level performance by formal methods on 
more difficult tasks led many AI researchers to an approach that emphasizes the 
importance of knowledge in expert problem solving [3]. That shift in approach has 
resulted in the emergence of expert systems technology. 
-- --y-------------~------------~Jr---~------_T---
Expert systems are knowledge-based AI programs which are capable of performing 
at the level of a human expert as a result of their emphasis on domain-specific 
knowledge and strategies. In addition to the characteristics of high-level perfor-
mance and reliance on domain-dependent knowledge. expert systems are distinguished 
from other AI programs and computer programs in general by their ability to reason 
about their own processes of inference. and to furnish explanations regarding those 
processes [3]. These distinguishing characteristics are made possible by the under-
lying architecture common to most expert systems. There are two major components 
[4]: a knowledge base (domain-dependent facts. rules. heuristics) and a separate 
inference procedure. Knowledge acquisition and input/output components are usually 
included. 
Expert systems are particularly well suited to two generic types of problems 
[4]. First. there are the problems in which pursuit of an exact or optimal solution 
would lead to a combinatorial explosion of computation; second. there are the prob-
lems that require interpretation of a large amount of data. In addition, the 
domains where application of expert systems technology 1S most appropriate are those 
fields in which "the difficult choices, the matters that set experts apart from 
beginners, are symbolic, inferential, and rooted in experiential knowledge" [4]. 
Expert systems have been constructed in such domains as medical diagnosis. chemistry. 
symbolic mathematics, geology, circuit design, structural engineering, and computer 
system configuration (Refs. 3-7 contain descriptions of these systems). 
Expert system techniques are currently powerful enough to produce a few success-
ful systems, such as MACSYMA, DENDRAL. and Rl [3]. But the state of the art still 
falls short of ideal intelligent behavior. or a mature technology. The domain of 
expertise must be very narrow. the problem representation languages and I/O languages 
are lim1ted. there is little self-knowledge (which affects explanation capabilities 
and recognition of the system's own limitations). expertise 1S restricted to that 
from a single source. and much of the knowledge and problem-solving approach incor-
porated into an expert system is painstakingly hand-crafted, resulting in relatively 
long construction times [3]. Research continues to push the boundaries of capability 
of expert systems outward. 
III. EXPERT SYSTEMS IN CFD 
The design and application of computational aerodynamic simulation tools 
involves the synthesis of many facets (Fig. I), each of which requires expertise 
and experience for its formulation, development. and use. It is conceivable that an 
expert system could be designed that would act as a flow-field synthesizer; that is, 
act on all of the facets depicted in Fig. 1 for a CFD computation. Present expert 
systems techniques could be used in at least five aspects of the CFD computation 
that would involve some of these facets (Fig. 2): three-dimensional grid generation 
(a pacing item in CFD [8]); flow problem definition and initialization; construction 
and analysis of numerical schemes; flow-solver selection and use; and data reduction, 
analysis, and display. Because grid generation has been identified as having the 
most promise, more detail is presented below. 
One of the most important facets required to solve accurately a three-
dimensional CFD problem using finite-difference procedures is the proper location of 
the nodal points in the flow region to be resolved. There are basically two decision 
stages and a feedback stage involved in the discretization process. The two decision 
stages involve (1) the grid topology and (2) the grid-generation scheme; the feedback 
stage involves an analysis and modification of the grid based on the geometric deriva-
tives, the flow-solver algorithm employed, and the flow solution generated. Although 
grid generation is intrinsically complex, the elements of the decision stages are well 
understood by experts in the field and the feedback stage is currently receiving atten-
tion. Grid generation is, therefore, likely to offer the greatest potential for an 
early successful design of an expert system in computational fluid dynamics. 
The schematic of an expert grid-generation system (EGGS) shown in Fig. 3 is 
based on some of the major components of an expert system; it depicts in detail the 
essential ingredients of some of those components. Input consists of three groups 
of information: (1) flow parameters such as the Mach number, angles of attack and 
yaw, and Reynolds number (these would determine, for example, whether planes of sym-
metry can be used, the position of the outer computational domain, and the nodal 
point clustering near surfaces); (2) geometric data (for external flows, the multi-
ple, time-varying body coordinates at the inner boundary of the computational volume); 
and (3) qualitative program control information such as the level of accuracy 
required (e.g., calculations for understanding complicated fluid physics might 
require a fine grid, whereas those for performing preliminary engineering design 
might require a coarse grid), and the permissible level of expense to be incurred. 
The knowledge base consists of facts (grid-generation schemes and grid-analysis 
theory) and heur~stics (experience and good judgment regarding grid topology deci-
sions, for example). Modern grid-generation philosophy concerned with three-
dimensional discretizations dictates that some form of a zonal grid topology be 
employed. There currently exists no theory that can determine the zoning or grid 
patchwork for either two- or three-dimensional problems, so heuristics are used. It 
is hoped that theory can eventually replace many of these heuristics. Once the flow 
field has been zoned, each zone can then be discretized using the procedures denoted 
in Fig. 3; the procedures include either algebraic or differential approaches. With 
the flow region discretized, various levels of grid-analysis procedures can be used 
to judge the quality of the resulting grid. These vary in complexity from proce-
dures that simply look at grid parameters, such as the transformation Jacobian, geo-
metric derivatives, and ratio of the metrics, to procedures that combine these func-
tions with the flow-solver algorithm and flow solution to yield an improved grid. 
EGGS produces as output three pieces of information to be used by the flow 
solver: (1) the coordinate location of the nodal points, (2) the definition of each 
surface of the computational cube (e.g., plane of symmetry, body, shock wave), and 
(3) the zonal interface control parameters. The latter piece of information tells 
the flow solver which parts of the zonal grid boundaries are adjacent to each other. 
This is required by the boundary condition routines in the flow solver. 
IV. RESOURCE REQUIREMENTS 
Development of knowledge-based systems requires a significant ~nvestment of 
time and money, and requires a new kind of professional - the knowledge engineer. 
One time-estimate for building an expert system is anywhere from 7 months (for 
simple systems in a friendly environment with existing tools) to 15 yr (for complex 
systems in demanding environments where new tools must be researched and developed) [9]. 
Although the proposed expert grid-generation system would fall toward the simple end 
of the spectrum. a more comprehensive expert flow-simulation system will undoubtedly 
be more complex, and may require more powerful AI tools than are presently available. 
For a discussion of the issues involved in expert system development, see Refs. 3 
and 9. 
V. CONCLUDING REMARKS 
The techniques of artific~al intelligence, in particular those of expert sys-
tems, can be applied to most facets of the numerical aerodynamic simulation process. 
This paper describes some of the concepts underlying those techniques, and indicates 
the areas of aerodynam~c s~mulation in wh~ch those techn~ques could play a signifi-
cant role. A proposed expert grid-generation system is briefly described which, 
given flow parameters, configuration geometry, and simulation constraints, uses 
expert knowledge about the discretizat~on process to determine grid-point coordi-
nates, computational surface information, and zonal 1nterface parameters. Addi-
tional details of this and other possible CFD expert systems can be found in Ref. 10. 
The potential payoff from the use of expert systems 1n the numerical aerodynamic 
simulation process is worthy of attention and warrants the allocation of resources 
as an investment ~n the future. Expert systems ~n CFD w1ll promote the fus~on, 
preservation, and distribution of aerodynamic knowledge, and will streamline research 
and design by managing the complexities of those processes. The users of these 
future systems will be freed from attending to the deta~ls of numerical simulation, 
and allowed to explore. innovate, and create at a higher level of abstraction. 
VI . REFERENCES 
1. Mueller, G. E., "The Future of Data Processing in Aerospace," Aeronaut. J., 
Apr. 1979, pp. 149-158. 
2. Fleckenstein, W.O., "Challenges in Software Development," Computer. Mar. 1983. 
pp. 60-64. 
3. Hayes-Roth, F., Waterman, D. A., and Lenat, D. B., Eds., Building Expert Systems, 
Teknowledge Series ~n Knowledge Engineering, Vol. I, Addison-Wesley Publishing 
Co., Inc., 1983. 
4. Fe~genbaum, E. A. and McCorduck, P., The Fifth Generation, Addison-Wesley Pub-
lishing Co., Inc., 1983. 
5. Gevarter, W. B .• "Expert Systems - Limited but Powerful," IEEE Spectrum. Aug. 
1983. pp. 39-45. 
6. Gevarter. W. B.. "An Overview of Artificial Intelligence and Robotics. Vol. I. 
Artificial Intelligence. Pt. A: The Core Ingredients." NASA TM-85836. June 1983. 
7. Barr. A. and Feigenbaum. E. A •• The Handbook of Artificial Intelligence. Vol. 2. 
William Kaufmann. Inc •• 1982. 
8. Kutler. P.. "A Perspective of Theoretical and Applied Computational Fluid Dynam-
iCS." AIAA Paper 83-0037. Reno. Nev •• 1983. 
9. Hayes-Roth. F •• "Codifying Human Knowledge for Machine Reading." IEEE Spectrum. 
Nov. 1983. pp. 79-81. 
10. Kut1er, P. and Mehta, U. B., "Computational Aerodynamics and Artificial Intelli-
gence." AIAA Paper 84-1531. Snowmass. Colo •• June 1984. 
X 
OUTPUT 
AND DATA 
REDUCTION 
IX 
INITIAL 
CONDITIONS 
VIII 
BOUNDARY 
CONDITIONS 
I 
PROBLEM 
DEFINITION 
VII 
GRID 
GENERATION 
II 
SOLUTION 
CONCEPT 
VI 
CONFIGU 
RATION 
DEFINITION 
III 
GOVERNING 
EQUATIONS 
IV 
TURBULENCE 
MODEL 
V 
ALGORITHM 
Fig. 1 Numerical aerodynamic s~mulation synthesizer (NASS). 
GRID GENERATION 
• CONCEPTUAL OR TOPOLOGICAL 
DISCRETIZATION 
• DEFINITION AND APPLICATION 
OF GRID-GENERATION PROCEDURE 
• GRID-QUALITY ANALYSIS 
• GRID POINT LOCATION ADJUST 
MENTS 
FLOW-PROBLEM DEFINITION 
• PHYSICAL ASPECTS OF FLOW 
SPEED REGIME 
VISCOUS OR INVISCID 
STEADY OR UNSTEADY 
THIN. SLENDER OR 
COMPLEX SHEAR LAYERS 
SHOCK WAVES - FLOW 
DISCONTINUITIES 
• SURFACE AND FIELD VARIABLES 
• PHYSICAL BOUNDARY AND 
INITIAL CONDITIONS 
• SUGGESTIONS AS TO SOLUTION 
METHODOLOGY 
FLOW SOLVERS ~ J~elJ C) • NAVIER STOKES SOLVER • PNSSOLVER 
• BOUNDARY LAYER SOLVER 
• INVISCID SOLVER 
U~++ll - 2U~+1 + U~.:"11 
.lx2 
CONSTRUCTION AND ANALYSIS 
OF NUMERICAL METHODS 
• DEVELOPMENT OF SCHEMES 
WITH SPECIFIC PROPERTIES 
• STABILITY ANALYSIS 
• ACCURACY ANAL YSIS 
DATA REDUCTION 
(ANAL YSIS AND DISPLAY) 
• ENGINEERING QUANTITIES 
• FLOW VISUALIZATION 
• ERROR ANAL YSIS 
Fig. 2 Expert systems in numer1cal aerodynamic simulation. 
CONFORMAL 
NONCONFORMAL 
ELLIPTIC 
PARABOLIC 
HYPERBOLIC 
FLOW 
PARAMETERS 
PROGRAM 
CONTROL 
PARAMETERS 
LEVEL OF 
ACCURACY 
COST 
BODY 1 t x Y z 
• • 
• • 
• 
• • 
Fig. 3 Expert grid-generation system (EGGS). 
BODY 1 txyz 
BODY 2 txyz 
• 
• • 
• • 
• • 
COORDINATE 
LOCATIONS 
COMPUTATIONAL 
SURFACE TYPE 
ZONAL INTERFACE 
INFORMATION 
l Ni esr ~-85976 I 2 Government Accession No J Recipient's Catalog No 
4 Title and Subtitle 5 Report Date 
POTENTIAL APPLICATION OF ARTIFICIAL CONCEPTS June 1984 6 Performing Organization Code TO NUMERICAL AERODYNAMIC SIMULATION 
7 Author(s' 8 Performing Organization Report No 
Paul Kut1er, Unmee1 B. Mehta, and A11son Andrews A-9798 
10 Work Unot No 
9 Performing Organization Name and Address T-6465 
Ames Research Center, 11 Contract or Grant No 
Moffett Field, CA 94035 
13 Type of Report and Period Covered 
12 Sponsorong Agency Name and Address Techn1ca1 Memorandum 
Nat1ona1 Aeronautics and Space Adm1n1strat1on 14 Sponsorong Agency Code 
WaSh1ngton DC 20546 505-31-01 
15 Supplementary Notes 
P01nt of contact: Paul Kut1er, Ames Research Center, MS 202A-14, 
Moffett Field, CA 94035 (415) 965-6032 or FTS 448 6032 
16 Abstract 
Computat1ona1 fluid dynam1cs slmu1at1on tools that are less expens1ve 
to develop, more powerful, versat11e, easy-to-use, and easy-to-interpret 
than currently available tools are necessary 1f computat1ona1 aerodynam1cs 
1S to fulfill its potent1a1 1n the veh1c1e des1gn process. To this end, 
some of the concepts of artific1a1 1nte11igence can be app11ed. 
Th1S paper br1ef1y 1ntroduces these concepts, and then 1t 1ndicates how 
expert systems developed from some of them can be adapted to speed the 
numer1ca1 aerodynam1c slmu1at1on process. A proposed expert gr1d-generat1on 
system 1S br1ef1y described which, given flow parameters, configuration 
geometry, and simu1at1on constra1nts, uses knowledge about the discretizat10n 
process to determ1ne grid p01nt coordinates, computat1ona1 surface informa-
t1on, and zonal 1nterface parameters. It 1S concluded that the potent1a1 
benef1t from the use of expert systems in the numer1ca1 aerodynam1c 
slmu1at1on process 1S worthy of attent1on. 
17 Key Words (Suggested by Author(s" 18 Dlstrobutlon Statement 
Computat1ona1 aerodynam1cs 
Computat1onal f1u1d dynam1cs Unl1m1ted 
Art1f1C1a1 inte111gence Subject category: 02 
Expert systems 
Gr1d generat10n 
19 Securoty Oasslf (of thiS report) 
1
20 Securoty Classlf (of thiS page) 
121 
No of Pages 122 Price' 
Unc1. Unc1. 8 A02 
'For sale by the National Technical Information Service, Sprongf.eld, Virginia 22161 
End of Document 


Potential application of artificial concepts to aerodynamic simulation

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