Abstract

In automatic transmission design, electronic control techniques have been adopted through proportional variable-force-solenoid valves, which typically consist of spool-type valves (Christenson, W. A., 2000, SAE Technical Paper Series, 2000-01-0116). This paper presents an experimental investigation and neural network modeling of the fluid force and flow rate for a spool-type hydraulic valve with symmetrically distributed circular ports. Through extensive data analysis, general trends of fluid force and flow rate are derived as functions of pressure drop and valve opening. To further reveal the insights of the spool valve fluid field, equivalent jet angle and discharge coefficient are calculated from the measurements, based on the lumped parameter models. By incorporating physical knowledge with nondimensional artificial neural networks (NDANN), gray-box NDANN-based hydraulic valve system models are also developed through the use of equivalent jet angle and discharge coefficient. The gray-box NDANN models calculate fluid force and flow rate as well as the intermediate variables with useful design implications. The network training and testing demonstrate that the gray-box NDANN fluid field estimators can accurately capture the relationship between the key geometry parameters and discharge coefficient/jet angle. The gray-box NDANN maintains the nondimensional network configuration, and thus possesses good scalability with respect to the geometry parameters and key operating conditions. All of these features make the gray-box NDANN fluid field estimator a valuable tool for hydraulic system design.

Issue Section:
Technical Papers
Keywords:
automotive components, rotational flow, valves, hydraulic systems, jets, power transmission (mechanical), design engineering, flow control, distributed control, mechanical engineering computing, neural nets, spool valve, fluid control systems, discharge coefficient, jet angle, variable force solenoid (VFS), gray box, nondimensional artificial neural network (NDANN)
1.
Christenson
,
W. A.
, 2000, “
Using CAE Simulation Tools to Study the Performance of a Two-Stage Variable Force Solenoid Acting on a Clutch
,” SAE Technical Paper Series, 2000-01-0116.
2.
Merritt
,
H. E.
, 1967,
Hydraulic Control Systems
,
Wiley
,
New York
.
3.
Miller
,
R. H.
,
Fujii
,
Y.
,
McCallum
,
J.
,
Strumolo
,
G.
,
Tobler
,
W. E.
, and
Pritts
,
C.
, 1999, “
CFD Simulation of Steady-State Flow Forces on Spool-Type Hydraulic Valves
,” SAE 1999-01-1058.
4.
DeVries
,
L.
, 2003, “
Automotive Transmission Spool Valve Test Stand Development for Axial Flow Force Modeling
,” M.S. thesis in Mechanical Engineering, Pennsylvania State University, University Park, PA.
5.
Cao
,
M.
,
Wang
,
K. W.
,
DeVries
,
L.
,
Fujii
,
Y.
,
Tobler
,
W. E.
,
Pietron
,
G. M.
,
Tibbles
,
T.
, and
McCallum
,
J.
, 2003, “
Automotive Hydraulic Valve Fluid Field Estimator Based on Nondimensional Artificial Neural Network (NDANN)
,”
Advances in Vehicle Modeling, Simulation, Dynamics and Control, 2003 ASME International Mechanical Engineering Congress
,
Washington, D.C.
, paper IMECE2003–42523.
6.
Funahashi
,
K.
, and
Nakamura
,
Y.
, 1993, “
Approximation of Dynamical Systems by Continuous Time Recurrent Neural Networks
,”
Neural Networks
0893-6080,
6
(
6
), pp.
801
–
806
.
7.
El-Gindy
,
M.
, and
Palkovics
,
L.
, 1993, “
Possible Application of Artificial Neural Networks to Vehicle Dynamics and Control: A Literature Review
,”
Int. J. Veh. Des.
0143-3369,
14
(
5–6
), pp.
592
–
614
.
8.
Cao
,
M.
,
Wang
,
K. W.
,
Fujii
,
Y.
, and
Tobler
,
W. E.
, 2005, “
Development of a Friction Component Model for Automotive Powertrain System Analysis and Shift Controller Design Based on Parallel-Modulated Neural Networks
,”
ASME J. Dyn. Syst., Meas., Control
0022-0434,
127
(
3
), pp.
382
–
405
.
9.
Cao
,
M.
,
Wang
,
K. W.
,
Fujii
,
Y.
, and
Tobler
,
W. E.
, 2004, “
Advanced Hybrid Neural Network (AHNN) Automotive Friction Component Model for Powertrain System Dynamic Analysis, Part 1: Model Development
,”
Proc. Inst. Mech. Eng., Part D (J. Automob. Eng.)
0954-4070,
218
(
8
), pp.
831
–
843
.
10.
Cao
,
M.
,
Wang
,
K. W.
,
Fujii
,
Y.
, and
Tobler
,
W. E.
, 2004, “
Advanced Hybrid Neural Network (AHNN) Automotive Friction Component Model for Powertrain System Dynamic Analysis, Part 2: System Simulation
,”
Proc. Inst. Mech. Eng., Part D (J. Automob. Eng.)
0954-4070,
218
(
8
), pp.
845
–
857
.
11.
Cao
,
M.
,
Wang
,
K. W.
,
Fujii
,
Y.
, and
Tobler
,
W. E.
, 2004, “
A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model
,”
ASME J. Dyn. Syst., Meas., Control
0022-0434,
126
(
1
), pp.
144
–
153
.
12.
Tsutsumi
,
A.
,
Chen
,
W.
,
Hasegawa
,
T.
, and
Otawara
,
K.
, 2001, “
Neural Networks for Prediction of the Dynamic Heat-Transfer Rate in Bubble Columns
,”
Ind. Eng. Chem. Res.
0888-5885,
40
(
23
), pp.
5358
–
5361
.
13.
Faller
,
W. E.
,
Schreck
,
S. J.
, and
Helin
,
H. E.
, 1995, “
Real-Time Model of Three-Dimensional Dynamic Reattachment Using Neural Networks
,”
J. Aircr.
0021-8669,
32
(
6
), pp.
1177
–
1182
.
14.
Faller
,
W. E.
, and
Schreck
,
S. J.
, 1997, “
Unsteady Fluid Mechanics Applications of Neural Networks
,”
J. Aircr.
0021-8669,
34
(
1
), pp.
48
–
55
.
15.
Benning
,
R. M.
,
Becker
,
T. M.
, and
Delgado
,
A.
, 2001, “
Initial Studies of Predicting Flow Fields with an ANN Hybrid
,”
Adv. Eng. Software
0965-9978,
32
(
12
), pp.
895
–
901
.
16.
Bardina
,
J.
, and
Rajkumar
,
T.
, 2003, “
Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients
,”
Proc. SPIE
0277-786X,
5102
, pp.
92
–
103
.
17.
Cerri
,
G.
,
Michelassi
,
V.
,
Monacchia
,
S.
, and
Pica
,
S.
, 2003, “
Kinetic Combustion Neural Modeling Integrated Into Computational Fluid Dynamics
,”
Proc. Inst. Mech. Eng., Part A
0957-6509,
217
(
2
), pp.
185
–
192
.
18.
Cao
,
M.
,
Wang
,
K. W.
,
DeVries
,
L.
,
Fujii
,
Y.
,
Tobler
,
W. E.
,
Pietron
,
G. M.
,
Tibbles
,
T.
, and
McCallum
,
J.
, 2004, “
Steady-State Hydraulic Valve Fluid Field Estimator Based on Nondimensional Artificial Neural Network (NDANN)
,”
ASME J. Comput. Inf. Sci. Eng.
1530-9827,
4
(
3
), pp.
257
–
270
.
19.
Mises
,
V.
, 1917, “
Berechnung von Ausfluss—und Uberfallzahlen (Calculation of Discharge and Weir Coefficients)
,”
Z. Vereines Deutscher Ingenieure
,
61
, pp.
144
–
153
.
20.
Seppala
,
J.
,
Koivisto
,
H.
, and
Koivo
,
H.
, 1998, “
Modeling Elevator Dynamics Using Neural Networks
,”
IEEE International Conference on Neural Networks—Conference Proceedings
,
IEEE World Congress on Computational Intelligence
,
IEEE
,
New York
, Vol.
3
, pp.
2419
–
2424
.
21.
Zorzetto
,
L. F. M.
,
Filho
,
R. Maciel
, and
Wolf-Maciel
,
M. R.
, 2000, “
Process Modeling Development through Artificial Neural Networks and Hybrid Models
,”
Comput. Chem. Eng.
0098-1354,
24
(
2
), pp.
1355
–
1360
.
22.
Rivera-Sampayo
,
R.
, and
Velez-Reyes
,
M.
, 2001, “
Gray-Box Modeling of Electric Drive Systems Using Neural Networks
,”
Proceedings of the 2001 IEEE International Conference on Control Applications CCA “01
, pp.
146
–
151
,
Mexico City
.
23.
Oussar
,
Y.
, and
Dreyfus
,
G.
, 2001, “
How to Be a Gray Box: Dynamic Semiphysical Modeling
,”
Neural Networks
0893-6080,
14
(
9
), pp.
1161
–
1172
.
24.
Karam
,
M.
, and
Zohdy
,
M. A.
, 2001, “
Nonlinear Model-Based Dynamic Recurrent Neural Network
,”
Midwest Symposium on Circuits and Systems
, Vol.
2
, pp.
624
–
626
.
25.
Braun
,
J. E.
, and
Chaturvedi
,
N.
, 2002, “
An Inverse Gray-Box Model for Transient Building Load Prediction
,”
HVAC&R Res.
1078-9669,
8
(
1
), pp.
73
–
99
.
26.
Xiong
,
Q.
, and
Jutan
,
A.
, 2002, “
Grey-Box Modeling and Control of Chemical Processes
,”
Chem. Eng. Sci.
0009-2509,
57
(
6
), pp.
1027
–
1039
.
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