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Watch the video: https://wp.me/p3RLHQ-k4h
Learn more: https://www.santafe.edu/research/projects/thermodynamics-computation
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Thermodynamics of Computation: Far More Than Counting Bit Erasure: Far More T...inside-BigData.com
In this deck from the HPC User Forum, David Wolpert from the Santa Fe Institute presents: Thermodynamics of Computation: Far More Than Counting Bit Erasure.
"The thermodynamic restrictions on all systems that perform computation provide major challenges to modern design of computers. The time is ripe to pursue a new field of science and engineering: a modern thermodynamics of computation. This would combine the resource/time tradeoffs of concern in conventional CS with the thermodynamic tradeoffs in computation that are now being revealed. In this way we should be able to develop the tools necessary both for analyzing thermodynamic costs in biological systems and for engineering next-generation computers."
Watch the video: https://wp.me/p3RLHQ-k4h
Learn more: https://www.santafe.edu/research/projects/thermodynamics-computation
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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3. ERGONOMY OF FLIGHT
CONTROL
• In the fifties :
• How the pilot manages his aircraft ?
• What operating image ?
• Comparison between:
• Human control and Automatic control :
SURPRISE ! ….
4. Jean Piaget (1896 / 1980)
• Swiss Grand Father of Cognitive Psychology
• How the child learns and controls his
environment
. Four phases ….!
5. 4 PIAGET BASIC PRINCIPLES
– OPERATING IMAGE
– TARGET – Sub TARGET
– ACTION
– COMPARISON BETWEEN
PREDICTED AND ACHIEVED
RESULT
– INTERNAL MODEL
– REFERENCE TRAJECTORY
– STRUCTURATION OF THE MV
– ERROR COMPENSATION
• NATURAL CONTROL : “ YOU DO NOT DRIVE YOUR CAR WITH A PID SCHEME”
15. 15
R
TM
Fi, Ti Te
R Reagent
¥
r
M
C
P
MV
M
dT
M
dt
= UA T
e
- T
M
+ DHx
reCPe Ve
dTe
dt
= reFi Ti- Te + UA TM- Te
q(Fi) TM+ TM= Ti
.
1) Fi =ct /MV= Ti CV=TM / level 0 =Ti ? :PFC
2) Ti =ct (!) MV=Fi Parametric Control non linear :PPC
3) MV : Ti and Fi : Enthaplic control (power) :PPC+
4) MV : Pressure of reactor :PFC
4 CONTROL STRATEGIES OF BATCH REACTORS
21. poche de 335 t d’acier liquide à 1550 °C
distributeur de 60 t
lingotière à largeur variable
850-2200 mm,
refroidie à l’eau
capteur
radio-actif
consigne=70%
PFCPFC
mesure de niveau
cages d’extraction
consigne de poids
tquenouille
PFC
PI
vérin
mesure de position
busette rotative
busette à 2 ouïes
A
A
mesure de poids
amplificateur
de puissance
22. IDENTIFICATION
-‐
Ultra-low level test signals ! ! ….
«
I do want to see your test signal on the level ..!«
-‐
Set of harmonics signals
Eigen function
-‐
High level Parallel filtering
.-‐
Different metal alloys
23. -10
0
10
20
30
40
50
60
70
BULGING COMPLEX CONTROL
without with
STOPPER
Amp sinus
Bmp cosinus
Frequency reference
-10
0
10
20
30
40
50
60
70
-10
0
10
20
30
40
50
60
70
BULGING COMPLEX CONTROL
without
BULGING COMPLEX CONTROL
without with
STOPPER
Amp sinus
Bmp cosinus
Frequency reference
COMPLEX ALGEBRA PREDICTIVE CONTROL ?!
24.
-‐
Bench test of pump gaskets
-‐
Flow : 35000 m3/h ….! ?
- Pressure : 17.50 MPa
- Temperature : 330 d°
27. PFC CHAUD
Convexité Echangeur Chaud
TEQ = L x T31 + ( 1 – L ) x T0
Prise en tendance de la
température d’entrée
F(T0)
PFC FROID
Convexité Echangeur Froid
TEQ = L x T30 + ( 1 – L ) x T51
Prise en tendance de la
température d’entrée
F(T51)
Logique de
choix de
l’action
(Chaud ou
Froid)
T5 T0 T31
T5 T51 T30
Consigne de
température
Régulation débit
chaud
Régulation débit
froid
Débit
source
chaude
Débit
source
froide
T0
T51
T5
T31
T30
28.
29. PID
Essai A2si du 17 avril 2012 (673)
15,0
25,0
35,0
45,0
55,0
65,0
18:14:24 18:21:36 18:28:48 18:36:00 18:43:12 18:50:24 18:57:36 19:04:48
Temps (heures)
Température(°C)
positionvanne(%)
30. PFC
Essai A2si du 18 avril 2012 (676)
0,0
20,0
40,0
60,0
80,0
100,0
14:31:12 14:45:36 15:00:00 15:14:24 15:28:48 15:43:12 15:57:36 16:12:00 16:26:24
Temps (heures)
Température(°C)
positionvanne(%)
T Inj
Consigne
retard +3 mn
31. F1, T1
F2, T2
F, T
F.T =F1.T1+F2.T2 (enthalpic balance)
T=λ.T1 +(1- λ).T2 with 0 ≤ λ ≤ 1
Hyperbolic function ! λ = F1/(F1+F2)
Convexity Theorem
33.
SANOFI
VERTOLAY
VITRY sur SEINE
ARAMON
MONTPELLIER
KÖLN
Training of staff: Transfer of Know How
34.
35.
36. DIFFUSION
and
PROMOTION
• Training
in
many
countries
• Universi@es,
technical
schools
(IRA
in
France)
• Training
of
professors
• Con@nuous
educa@on
of:
–
Teachers
of
technical
schools
–
Industrial
operators
• Documenta@on:
“Techniques
de
l’ingénieur”
37. Prac@cal
implementa@on
with
Scilab
•
The
Scilab
PFC
book:
Text
+
Diagram
+
Program
Code
• 45
programs
in
Scilab
implemen@ng
PFC
control:
– Elementary
– Ordinary
– Advanced
38. Elementary
example:
control
of
an
integrator
process
with
delay
// E_com_intg12
// process H(s) :integrator with delay : H(s)=exp(-R.s)/s
clear; xdel(winsid());clc
tf=1200;w=1:1:tf;
u=zeros(1,tf); MV=u;CV=u;
SP=u;sm=u;DV=u;
tech=1;//sampling period
r=50; // dealy R=r*tech
G=0.01; // proportional gain
tau=1/G; // closed loop time constant
am=exp(-tech/tau); bm=1-am; //model parameters
trbf=250; lh=1-exp(-tech*3/trbf);
SP=100; // set point
for ii =2+r:1:tf,
// perturbation
if ii>600 then DV(ii)=-0.2; end
// MV proportional loop
ec(ii)=MV(ii-1-r)-CV(ii-1); // error
MVp(ii)=G*ec(ii);
CV(ii)=CV(ii-1)+MVp(ii)+DV(ii);
// pfc
sm(ii)=sm(ii-1)*am+bm*MV(ii-1); // model
spred=CV(ii)+DV(ii)*1+sm(ii)-sm(ii-r);
d=(SP-spred)*lh+sm(ii)*bm;
MV(ii)=d/bm; // MV pfc
end
scf(0)
plot(w,SP*ones(w),'k',w,CV,'r',w,DV*100, 'k',w,MV,'b')
a=gca(); a.grid=[1,1]; a.tight_limits="on";
a.data_bounds=[1,-40;tf,140];
xlabel('sec');
xtitle('Control of a process : integrator + delay CV r / MV b /
DV*100 k')
39. Ordinary
example:
control
of
a
loop
with
constraint
transfer
// O_pfc_transparent
// back calculation, transparent control of level
clear,xdel(winsid()),clc
tf=1000; //duration of test
w=1:1:tf; //time;
tech=1;//sampling period
u=zeros(1,tf);
CV=u;//process output
MVp=u; MV=u;//manipulated variable
eps=u; //error
sm=u; //output of model
k=0.1; // gain of integrative process level
Kp=0.07; // gain of P(ID)
tau=143; // =1/(k*Kp)
am=exp(-tech/tau);
bm=1-am; //time constant of internal closed loop system by P(ID)
trbf=200; //desired closed loop system by PFC: only tuning factor
h=1; lh=1-exp(-tech*3*h/trbf);
fl=input('fl (1 with back calculation / 0 without back calculation) = ');
MVmax=5; //maxvalue of mv
SP(1:tf)=100;
for ii=2:1:tf,
eps(ii)=MV(ii-1)-CV(ii-1); // error of P(ID)
MVp(ii)=Kp*eps(ii); // manipulated variable of P(ID)
if MVp(ii)>MVmax then MVp(ii)=MVmax;end // constraint on the mvp
mvbc=(MVp(ii)/Kp)+CV(ii-1); // back calculation
CV(ii)=CV(ii-1)+k*MVp(ii-1);// output of process
sm(ii)=sm(ii-1)*am+bm*(fl*mvbc+(1-fl)*MV(ii-1));// internal model of
PFC with/without transfer of constraint
d =(SP(ii)-CV(ii))*lh+sm(ii)*bm; // computation of mv
MV(ii)=d/bm; // computation of mv
end
scf(0)
plot(w,SP','k',w,MVp*10,'m',w,CV,'r',w,MV,'b');
a=gca(); a.grid=[1,1]; a.tight_limits="on"; a.data_bounds=[2,0;tf,230];
xlabel('sec')
if fl==0 then title('without back calculation SP k / MVp*10 m / CV r / MV
b'),end
if fl==1 then title('with back calculation SP k / MVp*10 m / CV r / MV
b'), end
40. Conclusion
• Dissemination? : where is the problem?
• The technical and economic efficiency of
PFC is clearly demonstrated on many different
processes World Wide
• Implementing PFC: the Scilab PFC book