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Expert Systems Case Studies: MYCIN

Introduction
Problem domain:

• Selection of antibiotics for patients with serious infections.

• Medical decision making, particularly in clinical medicine is regarded as an"art form" rather than a "scientific discipline": this knowledge must be systemized for practical day-to-day use and for teaching and learning clinical medicine.

Target Users: Physicians and possibly medical students and paramedics.

Originators           Edward Shortliffe, (1972)
                                Department of Medicine and Computer Science,
                                Heuristic Programming Project,
                                Stanford University School of Medicine,
                                Stanford, CALIFORNIA

References:            Shortliffe, Edward H.(1981), "CONSULTATION SYSTEMS FOR PHYSICIANS:
                                The Role of Artificial Intelligence Techniques". In Webber, Bonnie L.and Nilsson, Nils J. (Eds).
                              Readings in Artificial Intelligence. pp323-333. Tioga Publishing Company. Palo Alto, California.

MYCIN -- A typical consultation

1. Patient’s name: (first - last)

**               FRED BRAUN

**                 M

3. Age:

**               55

4. Are there any cultures for Fred Braun which may be related to the present illness, and from which organisms have been grown successfully in the microbiology laboratory?

**               Y

                    - - - CULTURE - 1 - - -

5. From what site was the specimen for CULTURE - 1 taken?

               **     BLOD
                =     BLOOD

The first organism isolated from the blood culture of 20-JUN-77 (CULTURE-1) will be referred to as:
                - - - ORGANISM - 1 - - -

7. Enter the laboratory - reported identity of ORGANISM-1:
                **     UNKNOWN

10. What is the form of the individual organisms (e.g. lancet-shaped for cocci, fusiform for rods, etc)?
                **     FUSIFORM

23. What is the suspected portal of entry of the gramneg rod from the blood culture of 20-JUN-77 (ORGANISM-1) into this sterile site?

                **     GI (6)

25. Is Fred Braun a compromised host (e.g. alcoholic, sickle-cell-disease, immunosuppressed)?
                **     N

26. Is Fred Braun febrile?
                **     Y

My therapy recommendations will be designed to treat for organisms that are either very likely, or, although less likely, would have a significant effect on therapy selection if they were present. It is important to cover for the following probable infection(s) and associated organism(s):

INFECTION-1 is BACTEREMIA

<ITEM-1> E.COLI [ORGANISM-1]
<ITEM-2> KLEBSIELLA [ORGANISM-1]
<ITEM-3> ENTEROBACTER [ORGANISM-1]
<ITEM-4> KLEBSIELLA-PNEUMONIAE [ORGANISM-1]
 

** WHAT DOES GU MANIPULATION TELL YOU ABOUT THE IDENTITY OF AN ORGANISM?
The rules listed below use:

Which do you wish to see?

                **     156

RULE 156

IF:         1. The site of the culture blood, and
              2. The gram stain of the organism is gramneg, and
              3. The morphology of the organism is rod, and
              4. The portal of entry of the organism is urine, and
              5. The patient has not had a genito-urinary manipulative procedure, and
              6. Cystitis is not a problem for which the patient has been treated

THEN: There is suggestive evidence (.6) that the identity of the organism is e.coli

**                 IS BLOOD A STERILE SITE?

Yes. Blood is one of those sites that is normally sterile.

**                 WHAT ARE THE NONSTERILE SITES?

Those sites that are normally nonsterile are: CERVIX, CUTANEOUS-ULCER, LOCHIA, NOSE, SKIN, STOOL, THROAT, URETHRA, VAGINA.

** IS BACTEROIDES AEROBIC?

No, the aerobicity of bacteroides is anaerobic.
 

WHY: Explanation of the input requirement
(The user’s questions follow the double-asterisk prompt.)

34. Was penicillinase added to this blood culture (CULTURE-1)?
**                 WHY

[i.e. WHY is it important to determine whether penicillinase was added to
CULTURE-1?]

[3.0]             This will aid in determining whether ORGANISM-1 is a contaminant.
                     It has already been established that
[3.1]             the site of CULTURE-1 is blood, and
[3.2]             the gram stain of ORGANISM-1 is grampos

Therefore, if

[3.3]             penicillinase was added to this blood culture

then

                    there is weakly suggestive evidence (.4) that ORGANISM-1 is a contaminant [RULE039]

                  **         WHY

[i.e. WHY is it important to determine whether ORGANISM-1 is a contaminant?]

[4.0]...         In order to determine whether there is significant disease associated with this occurrence of ORGANISM-1.
                    It has already been established that

                    [4.1] this blood culture was taken from a sterile source

                    Therefore, IF

                    [4.2] ORGANISM-1 is not a contaminant

THEN
                    there is strongly suggestive evidence (.9) that there is significant disease associated with this occurrence of
                    ORGANISM-1 [RULE032]

[i.e. WHY is it important to determine whether there is significant disease associated with this occurrence of ORGANISM-1]

[5.0] ...         In order to determine the organisms which require therapy.
                    It has already been established that
                    [5.1] the infection is known, and
                    [5.2] the identity of ORGANISM-1 is known

Therefore, IF
                    5.3] there is significant disease associated with this occurrence of ORGANISM-1

THEN
                    it is definite (1.0) that ORGANISM-1 is one of the organisms which require therapy
                    [RULE090]

35. Is this a hospital-acquired infection?

                  **        WHY

[i.e. WHY is it important to determine whether the infection was acquired while the patient was hospitalized?]

                    [2.0] This will aid in determining the organisms (other than those seen on cultures or smears] which
                    might be causing the infection.

It has already been established that
                    [2.1] the infection which requires therapy is meningitis, and
                    [2.2] the organisms were not seen on the stain of this pending csf culture, and
                    [2.3] the type of the infection is bacterial, and
                    [2.4] neurosurgery is not one of the relevant items from the history of Fred Braun, and
                    [2.5] the age of Fred Braun is greater than 10 days

Therefore, IF
                    [2.6] the infection was acquired while the patients was hospitalized

THEN
                    there is evidence that the organisms (other than those seen on cultures or smears) which might be
                    causing the infection is e.coli (.75) staphylococcus- coag-pos (.3) pseudomonas-aeeruginosa (.3)
                    klebsiella-pneumoniae (.5)

[back to question 35...]

HOW: Justification of the output information

** HOW DO YOU DECIDE THAT AN ORGANISM IS AN E.COLI?
The following rules conclude about:
the likelihood that the identity of the organism is E.coli.

Which of the rules do you wish to see?

** NONE

                                      ** HOW 5.2

[i.e. HOW was it established that the identity of ORGANISM-1 is known?]

Question 9 was asked, but you weren’t sure; so the following was used:

WHAT: Querying the knowledge base

** WHAT IS THE TREATMENT OF PNEUMOCOCCAL PNEUMONIA?
The MYCIN system currently can treat the following infections: BACTEREMIA MENINGITIS CYSTITIS. Because the knowledge base is incomplete, all other infections will be treated like bacteremia.

For treatment of DIPLOCOCCUS-PNEUMONIAE in BACTEREMIA, the drugs of choice (ranked by relative efficacy, assuming sensitivity) are:

RECOMMENDATION: Coming to a conclusion

Creation of the Potential Therapy List

IF:             The identity of the organism is pseudomonas
THEN:       I recommend therapy chosen from among the following drugs:

                                                                                        1 - colistin                 (.98)
                                                                                        2 - polymyxin            (.96)
                                                                                        3 - gentamicin           (.96)
                                                                                        4 - carbenicillin         (.65)
                                                                                        5 - sulfisoxazole        (.64)
 

MYCIN selects drugs only on the basis of the identity of offending organisms. Thus the program’s first task is to decide, for each current organism deemed to be significant, which hypotheses regarding the organism’s identity (IDENT) are sufficiently likely that they must be considered in choosing therapy. MYCIN uses the CF’s of the various hypotheses in order to select the most likely identities. Each identity is then given as item number (see below) and the process is repeated for each significant current organism. The Set of Indications for therapy is then printed out, e.g.:
 
 

My therapy recommendation will be based on the following possible identities of the organism(s) that seem to be significant:

<Item 1> The identity of ORGANISM-1 may be STREPTOCOCCUS-GROUP-D
<Item 2> The identity of ORGANISM-1 may be STREPTOCOCCUS-ALPHA
<Item 3> The identity of ORGANISM-2 is PSEUDOMONAS

English description

IF           1) the stain of the organism is gramneg, and
                 2) the morphology of the organism is rod, and
                 3) the aerobicity of the organism is aerobic
 

THEN      there is strongly suggestive evidence (0.8) that
                 the class of organism is enterobacteriaceae

MYCIN internal representation: STATIC

PREMISE:         ($AND
                            (SAME CNTXT GRAM GRAMNEG)
                            (SAME CNTXT MORPH ROD)
                            (SAME CNTXT AIR AEROBIC)

ACTION:
                            (CONCLUDE CNTXT CLASS ENTEROBACTERIACEAE TALLY 0.8)
 
 
 

The CNTXT symbol is really a variable which gets "instantiated" during a MYCIN consultation to the context node which is currently under consideration,e.g. ORGANISM-1:

PREMISE:     ($AND(SAME ORGANISM-1 GRAM GRAMNEG)
                        (SAME ORGANISM-1 MORPH ROD)
                        (SAME ORGANISM-1 AIR AEROBIC))

ACTION:       (CONCLUDE CNTXT CLASS ENTEROBACTERIACEAE TALLY 0.8)
 
 

IF               1) the stain of the organism is gramneg, and
                     2) the morphology of the organism is rod, and
                     3) the aerobicity of the organism is aerobic

THEN          there is strongly suggestive evidence (0.8) that the class of organism is enterobacteriaceae

IF               The identity of the organism is bacteroides

THEN          I recommend therapy chosen from the following drugs:
                                                                              1. clindamycin (0.99)
                                                                                2. chloramphenicol (0.99)
                                                                                3. erthromycin (0.57)
                                                                                4. tetracycline (0.28)
                                                                                5. carbenicillin (0.27)
 
 

Rules encapsulating knowledge about knowledge -- which rules to apply in order to satisfy a certain (sub-) goal

IF              1) the infection is pelvic abscess, and
                    2) there are rules which mention in their premise enterobacteriaceae, and
                    3) the there are rules which mention in their premise gram-positive rods

THEN         there is suggestive evidence (0.4) that the former should be applied before the later
 

MYCIN rule and fact descriptions are a formal language and have a formal syntactic description: formal syntax being essential to the definition of well-behaved inference procedure. MYCIN rules can be expressed in Backus-Naur form (BNF). BNF is a form of context free grammar used extensively to define programming languages. In the notation keywords are written in uppercase (e.g. IF, THEN, AND, OR etc) and are regarded as terminals in the (BNF) syntax. Nonterminals are enclosed in angle brackets: <> the nonterminal figure to the left of ::= can be replaced with the expressions on the right.

• Rule description
<rule>                                         ::= <IF (premise THEN <action> [ELSE <action>])

<rule>                                       ::= <premise> <action> | <premise> <action> <else>

The premise (also known in the literature as the antecedent) of a rule consists of a conjunction of conditions, each of which must hold for the the indicated action to be taken:

<premise>                                 ::= ($AND <condition> ... <condition>)

An action can lead either to a conclusion (e.g. consequent); or can lead to the invocation of an action function (e.g. procedure); or can lead to the execution of a number of conclusions or action functions:

<action>                                     ::= (<conclusion>) | (<actfunc>) |
                                                  (DO ALL <conclusion> .....<conclusion>) |
                                                  (DO ALL <actfunc>....<actfunc>)

or
<action>                                    ::= (<consequent>) .... <conclusion>|
                                                 (<procedure> ....<procedure>)

A condition may be (i) a disjunction of conditions or a predicate and its associative triple (object-attribute-value), or (ii) more generally a special function and its argument and (iii ) negations of the conditions are handled by individual predicates

<condition>                             ::= ($OR <condition> ... <condition>) |
                                               ( <special-func> <arguments>) |
                                               (<func1> <context> <parameter>) |
                                               ( <func2> <context> <parameter> <value>)

<else>                                     ::= <concpart>
                                               <concpart> ::= <conclusion> | <act func> |
                                               (DO-ALL <conclusion> ... <conclusion>) |
                                               (DO-ALL <actfunc> ... <actfunc>)
 
 

MYCIN Rules: The context tree

MYCIN's decisions involve not only the patient but also the cultures that have been grown, organisms that have been isolated, and drugs that have been administered: Each of these is a context of MYCIN's reasoning. There are 10 different context types

PERSON The patient

CURCULS A Current culture form which organisms were isolated

PRIORCULS A culture obtained in the past

CURORGS An organism isolated from a current culture

PRIORORGS An organism isolated from a prior culture

OPERS An operative procedure the patient has undergone

CURDRUGS An antimicrobial agent currently being administered to a patient

PRIORDRGS An antimicrobial agent administered to the patient in the past

OPDRGS An antimicrobial agent administered to the patient during a recent operative procedure

POSSTHER A therapy being considered for recommendation
 
 

ASSOCWITH:                     CURCUL

MAINPROPS:                     (IDENT GRAM MORPH SENSITIVS)

PROMPT2:                          (any other organisms isolated form * for which you would like a therapeutic recommendation?)

PROMPT3:                          (I will refer to the first offending organism from * as:)

PROPTYPE:                        PROP-ORG

SUBJECT:                           (ORGRULES CURORGRULES)

SYN:                                   (IDENT (the *))

TRANS :                             (CURRENT ORGANISMS OF *)

TYPE:                                  ORGANISM -

PROP-OP                 Those clinical parameters which are attributes of operative procedures (e.g., the cavity, if any, opened
                                  during the procedure)

PROP-ORG               Those clinical parameters which are attributes of organisms (e.g., identity, gram stain, morphology)

PROP-PT                   Those clinical parameters which are attributes of the patient (e.g., name, sex, age, allergies, diagnoses)

PROP-THER              Those clinical parameters which are attributes of therapies being considered for recommendation
                                    (e.g., recommended dosage, prescribing name)

EXPECT                     This property indicates the range of expected values that the parameter may have.
                                    IF equal to (YN), then the parameter is a yes-no parameter.
                                    IF equal to (NUMB), then the expected value of the parameter is a number
                                    IF equal to (ONE-OF <list>), then the value of the parameter must be a member of <list>.
                                    IF equal to (ANY), then there is no restriction on the range of values that the parameter may have

PROMPT                     This property is a sentence used by MYCIN when it requests the value of the clinical parameter
                                     from the user; if there is an asterisk in the phrase, it is replaced by the name of the context about
                                     which the question is being asked; this property is used only for yes-no or single-valued parameters

PROMPT1                   This property is similar to PROMPT but is used if the clinical parameter is a multi-valued parameter;
                                     in these cases MYCIN only asks the question about a single one of the possible parameter values; the
                                     value of interest is substituted for (VALU) in the question

LABDATA                   This property is a flag, which is either T or NIL; if T it indicates that the clinical parameter is a piece
                                     of primitive data, the value of which may be known with certainty to the user.

LOOKAHEAD             This property is a list of all rules in the system that reference the clinical parameter in the premise
 

CNTXT                        The node in the context tree about which the conclusion is being made

PARAM                       The clinical parameter whose value is being added to the dynamic data base.

VALUE                        The inferred value of the clinical parameter.

TALLY                         The certainty tally for the premise of the rule conclusion to be made regarding the value of the
                                     clinical parameter.

CONTAINED-IN        This property is a list of all rules in the system in which the action or else clause references
                                     the clinical parameter but does not cause its value to be updated.

TRANS                         This property is used to translate an occurrence of this parameter into its English representation;
                                      the context of the parameter is substituted for the asterisk during translation.

DEFAULT                    This property is used only with clinical parameters for which EXPECT = (NUMB); it gives
                                      the expected units for numerical answers (days, years, grams, etc.).

CONDITION               This property, when utilized, is an executable LISP expression that is evaluated before MYCIN
                                     requests the value of the parameter; if the CONDITION is true, the question is not asked (e.g.,
                                     "Don't ask for an organisms' subtype if its genus is not known by the user").
 
 

•MONITOR (for MYCIN rules) attempts to evaluate the premise of the current rule, condition by condition. If any of the conditions is false, or indeterminate due to lack of information, the rule is rejected, and the next rule on the list of applicable rules pending in the current context is tried. The rule application succeeds when all of the conditions in the premise are deemed to be true, and the conclusion of the rule is added to the record of the current consultation:
 
 

• FINDOUT (Mechanism) searches for data needed by the MONITOR procedure, particularly the MYCIN 'clinical' parameters referenced in the conditions which are not known. Essentially FINDOUT gathers the information that will count for or against a particular condition in the premise of the rule under consideration. If the information required is laboratory data which the user can supply, then control returns to MONITOR and the next condition is tried. Otherwise, if there are rules which can be used to evaluate the condition, by virtue of the fact that their actions reference the relevant clinical parameter, they are listed and applied in turn using MONITOR:

An example of the kind of reasoning network generated by the MONITOR and FINDOUT mechanisms. Names of the clinical parameters are underlined. When a rule has multiple conditions in the premise, numbers have been included to specify the positions of the associated clinical parameters within the premise condition

Background:

Clinical Practice: In medical diagnosis and treatment there is often uncertainty regarding attributes such as

                                                                                    • the significance the disease
                                                                                    • the efficacy of a treatment
                                                                          or       • the diagnosis itself.

Medical Terminology: Medical (and other life and human sciences) terminology allows considerable scope for vagueness, ambiguity, inexactness,imprecision and/or uncertainty:

                                                                                    • "adequate" dosage of a drug
                                                                                    • "stable" condition of patient
                                                                                    • 'the patient is "feverish"'
                                                                                    • 'this is a "possible" case of meningitis

Terminology, Knowledge and Knowledge Engineering:  The knowledge of a subject domain is encoded in its terminology. Knowledge in an expert system is used for solving problems: a knowledge engineer is expected to engineer, or decode, this knowledge and use data structures to represent the results of the decoding process on a computer system. And, if this knowledge contains descriptions which are vague, ambiguous, inexact or reflects uncertainty, then it is essential to model the vagueness such that the description can be engineered on a computer system.
 
 

Bayes' Theorem: A number of workers in medical sciences have used a variety of statistical methods and techniques for examining and utilizing evidence (e.g. clinical signs, patient history etc.) to select a diagnosis or to support a prescription. Bayes' Theorem has been used extensively in computer-based medical decision support systems.

The medical diagnostic problem can be viewed as the assignment of probabilities to specific diagnosis after all the relevant data has been analyzed:

                                                Let       e = sum of the relevant data
                                                            di = ith diagnosis of a disease

                                                then
                                                            P(di y e) = the conditional probability that the patient
                                                            has disease "i" in the light of evidence e

                                                            P(e y di) = is the probability that a patient will have a
                                                            complex of symptoms and signs represented by
                                                            "e" given that he or she has disease "i"

                                                            P(di) = a priori probability that the patient has
                                                            disease "i", before any evidence has been gathered

Bayes' Theorem allows P(di y e) to be calculated from the component conditional probabilities:

                                                            P(di y e) = P(e y di) * P(di) / ??P(e y di) * P(di)
 

Consider the following rule

                        IF
                        1) The stain of the organism in grampos, and (01)
                        2) The morphology of the organism is COCCUS, and (02)
                        3) The growth confirmation of the organisms chains (03)

                        THEN
                        There is suggestive evidence (0.7) that the identity
                        of the organism is streptococus. (h1)

The antecedent clause (ie. the IF clause) can only be proved true if each of the 3 observations ( 01, 02 and 03) are proved to be true, then and only then it is possible to confirm the hypothesis ( h1) that the identify if the organism is streptococcuss with a 70% "belief" in that hypothesis ( h1)!

                        P(h1 ¦  01&02 &03) = 0.7

During MYCIN'S knowledge acquisition sessions the knowledge engineers not only extracted the rules of the above type but also asked the experts to "weight the belief in a given conclusion"

The P-function should not be (or could not be ) treated as probability by MYCIN's developers,: the number 0.7 is not probability:

                        Because if the evidence for Streptococcuss is

                                                        P(h1 ¦ 01&02 &03) = 0.7

then logically it can be concluded that evidence against Streptococcuss is

                                                        P( not h1¦ 01&02 &03)= 0.3
 

The expert's concern here is that although evidence may support a hypothesis with degree X (e.g. 0.7), it does not support the negation of the hypothesis with degree 1-X (e.g. 0.3)
 

Swinburne has described a useful classification of statistical theories (1973). MYCIN developers have used Swinburne's analysis to point out that traditional statistical probability theories are inadequate for quantifying medical decision making process, particularly when uncertainty or inexactness is involved. The developers focus on probability theories which can deal with the verification of a belief, hypothesis, or conjecture concerning a truth in the present.
 
 
 

The concept confirmation (of a hypothesis h) can be used in three possible ways:

classificatory:         'the evidence e confirms the hypothesis h'

comparative:           'e1 confirms h more strongly than e2 confirms h'
                                 or
                                 'e confirms h1 more strongly than e confirms h2'

quantitative             'e confirms h with strength x '

• The developers of MYCIN specified a semiquantitative approach.

• The degree of confirmation is of hypothesis h (given evidence e) is written as C[h,e]. A form which roughly parallels P[h ¦ e]
 
 

• According to the subjective probability theory:

expert's personal probability, P(h), reflects his/her belief in h at any given time
therefore, 1 - P(h) can be viewed as an estimate of the expert's disbelief regarding the truth of h.

• Measure of Belief: If P[h ¦ e] is greater than P(h), the observation of 'e' increases the expert's belief in 'h' while decreasing disbelief in h. Proportionate decrease in disbelief ( alternatively, the measure of belief increment) due to the observation 'e' is

                                                                     P(h ¦ e) - P(h)
                                                MB[h , e]= --------------------------
                                                                       1 - P(h)
 

• Measure of Disbelief:  If P[h y e] is less than P(h), the observation of 'e' decreases the expert's belief in 'h' while increasing disbelief in h. Proportionate decrease in belief ( alternatively, the measure of disbelief increment) due to observation 'e' is:
 

                                                                    P(h ) - P(h ¦ e)
                                               MD[h , e]= --------------------------
                                                                            P(h)

• Belief and disbelief correspond to the intuitive concepts of confirmation and disconfirmation

•Because a given piece of evidence cannot support both belief and disbelief, therefore

                                if         MB[h , e] > 0 then MD[h , e] = 0;

                             if         MD[h , e] > 0 then MB[h , e] = 0

and

                             if         P(h ¦ e) = P(h) then MB[h , e] = MD[h , e] = 0

(evidence is independent of hypothesis)
 

Certainity factors are based on a number of observations. First, in traditional probability theory the sum of confidence for a relationship and confidence against a relationship must add upto UNITY. However, it is often the case that an expert might have confidence, say X, that some relationship is true and have no feeling about the relationship being not true.

Second, confidence measures correspond to the ibformal evaluations that human experts attach to their conclusions, e.g. 'it is probably true', or 'it is highly likely'.

The certainity factors used in MYCIN and CENTAUR is based on some simplifying assumptions for (a) creating confidence measures and (b) for combining these confidences. These assumptions involve splitting 'conficdence for' from 'confidence against' a relationship as expressed through the so-called measure of belief (MB (H|E) ) and the measure of disbelief (MD(H|E)). These two measures constrain each other in that a given piece of evidence is either for or against a particular hypothesis.

                                                           CF[h , e] = MB[h , e] - MD[h , e]
 

• Certainty Factors: The value of every clinical parameter is stored by MYCIN along with an associated certainty factor (CF) that reflects MYCIN's "belief" that the value is correct.

A CF is a number between -1 and +1 that reflects the degree of belief in a hypothesis:

                                                          CF[h , e] = MB[h , e] - MD[h , e]
 

 Range of degrees of the Belief Measures

                                                            0 <= MB[h , e] <=1
                                                            0 <= MD[h , e] <=1
                                                            -1 <= CF[h , e] <=1
 

• Given that di is the ith possible diagnosis, and sk is the kth clinical observation and e is the composite of all observed.

Suppose that MB[di,sk] is known for each of sk, MB[di,sk] is known for each of sk, and e represents the conjunction of all the sk. Then MYCIN attempts to calculate CF[di,e] from the MB's and MD's known for individual sk's:

• MYCIN uses four combination functions: Incremental, Conjunction, Disjunction and Strength (of evidence) functions

Incrementally acquired evidence

            0: if MD[h,s1 & s2]= 1

            MB[h,s1 & s2] = { MB[h,s1] + MB[h,s2] (1-MB[h,s1])
             0: if MB[h,s1 & s2]= 1

            MD[h,s1 & s2] = {MD[h,s1] + MD[h,s2] (1- MD[h,s1])

Conjunction of hypotheses

            MB[h1 &h2,e] =min( MB[h1 , e] , MB[h2 , e])
            MD[h1 &h2,e] =max( MD[h1 , e] , MD[h2 , e])

Disjunction of hypotheses

            MB[h1 &h2,e] =max( MB[h1 , e] , MB[h2 , e])
            MD[h1 &h2,e] =min( MD[h1 , e] , MD[h2 , e])
 
 

           and

            CF(h1 or h2) = MAX(CF(h1, CF(h2)).

            CF(R1) + CF(R2) - (CF(R1) * CF(R2)) when CF(R1) and CF(R2) are positive

            CF(R1) + CF(R2) + (CF(R1) * CF(R2)) when CF(R1) and CF(R2) are negative

This consultation concerns the 747 wing. The purpose of the consultation is to demonstrate the general features of a structural analysis consultation on a simple problem. Thus the consultation involves only one substructure: The total wing.

26-Jul-78 10:54:44
[consultation of 13-Jul-78 1:55PM]

- - - STRUCTURE - 28 - - -

1. What is the name of STRUCTURE-28?
**           BOEING 747 WING

2. Assuming that your characterisation of the Boeing 747 wing in terms of its geometry, material characteristics, and boundary conditions are accurate, how much error (in percent) is tolerable for the analysis?
**           10

3. Do you want to examine the responses of the Boeing 747 wing, to evaluate its instability, or both?
**           EXAMINE-RESPONSES

4. Does the Boeing 747 wing have any time dependent terms in its equations of equilibrium?
**           NO

5. Is heating or cooling of interest in determining structure responses?
**           NO

6. Have you decomposed the Boeing 747 wing into a number of potential sub-structures which are relevant to this analysis?
**           YES
 
 

            - - - SUB-STRUCTURE -1 - - -

7. What is the name of SUB-STRUCTURE-1?
**           TOTAL WING

8. What is the material composing most of the total wing (SUB- STRUCTURE-1)?
**           HIGH-STRENGTH-ALUMINIUM

9. Enter the average longest dimension of the total wing
(SUB- STRUCTURE-1) in inches (or <number> CMS):
**           14200.

10. Enter the weight of the total wing (SUB-STRUCTURE-1) in pounds (or <number> KGS):
**           46000

11. Is the total wing (SUB-STRUCTURE-1) thin-walled or solid?
**           THIN-WALLED

12. What is the configuration of support for the total wing (SUB-STRUCTURE-1)?
**           ONE-SIDE

13. Is the total wing (SUB-STRUCTURE-1) to be modelled as a 1, 2 or 3 dimensional system?
**           2

14. Would you describe the construction of the total wing (SUB-STRUCTURE-1) to be truss and/or frame elements (NETWORK), sheets and/or blocks of material (CONTINUUM), or a combination of both?
**           CONTINUUM

15. Is the overall geometry of the total wing (SUB-STRUCTURE-1) curved or planar?
**           PLANAR

The first loading applied to the total wing (SUB-STRUCTURE-1) will be called:

- - - LOADING-1 - - -

16. What is the name of LOADING-1?
**           FLIGHT

17. Enter the number of loading cycles to be applied:
**           20000

The first load component associated with LOADING-1 will be called:
- - - LOAD-COMPONENT-1 - - -

19. Which surface of the sub-structure does LOAD-COMPONENT-1 act NORMAL to? (If more than one surface, you should consider the loading as two or more component loadings)
**           WIDTH-LENGTH

20. Enter the average wall thickness of the total wing (SUB-STRUCTURE-1) in inches (or <number> CMS):
** .           31

21. Enter the average depth of the total wing (SUB-STRUCTURE-1) in inches (or <number> CMS):
**           41

22. Enter the magnitude of the distributed load (in psi):
**           1

23. Are there any other load components associated with LOADING-1?
**            NO

24. Are there any other loading conditions associated with the total wing (SUB- STRUCTURE-1)?
**           NO

25. Are there any other sub-structures of the Boeing 747 wing relevant to this analysis?
**            NO

26. Do the supports of the Boeing 747 wing involve Coulomb friction, nonlinear springs, and/or grapping?
**            NO
 

The following analysis classes are relevant to the analysis of your structure:

                                                                            1. general-inelastic

The following recommendations apply to this case:
 

Activate incremental stress - incremental strain analysis.

Model nonlinear stress-strain relation of the material.

Solution will be based on a mix of gradient and Newton methods.

Logic to scan peak stress at each step and evaluate fatigue integrity should be used.

Logic to scan deflections, calculate relative values, and compare with code limits, should be called upon.

Cumulative strain damage should be calculated.

Analysis should include two or more load cycles (if cyclic) with extrapolation for strain accumulation.

Shakedown extrapolation logic should be used.

A single cycle of loading is sufficient for the analysis.

Do you wish advice on another structure?

**            NO