1. Field of the Invention
The present invention relates generally to finite element models for crash test dummies and, more particularly, to a method of material modeling for crash test dummy finite element models.
2. Description of the Related Art
Automotive, aviation, and other vehicle manufacturers conduct a wide variety of collision testing to measure the effects of a collision on a vehicle and its occupants. Through collision testing, a vehicle manufacturer gains valuable information that can be used to improve the vehicle.
Collision testing often involves the use of anthropomorphic mannequins, better known as “crash test dummies.” During collision testing, an operator places a crash test dummy inside a vehicle, and the vehicle undergoes a simulated collision. The collision exposes the crash test dummy to high inertial loading, and sensors inside the crash test dummy, such as accelerometers, pressure gauges, and the like, generate electrical signals of data corresponding to the loading. Cables transmit these electrical signals of data to a data acquisition system (DAS) for subsequent processing. This data reveals information about the effects of the collision on the crash test dummy and can be correlated to the effects a similar collision would have on a human occupant.
In any commercially available crash simulation softwares such as LS-DYNA, PAM-CRASH, and ABAQUS, users cannot handle J (relative volume). In these softwares, to make the material stiff or soft, the user scales up or down the stress-strain test curve, which disturbs the original test curve and is not efficient. In addition, the maximum number of parameters from these softwares is 3 or 4, which results in poor curve fitting because these softwares do not use Genetic Algorithm.
Accordingly, the present invention is a computer method of material modeling for crash test dummy finite element models. The method includes the steps of making a material card for the material and applying the material card to validate a finite element model of a crash test dummy component. The method also includes the steps of determining whether the finite element model is acceptable, ending the method if the finite element model is acceptable, and adjusting a relative volume (J) range for the material to make the material soft or stiff if the finite element model is not acceptable.
One advantage of the present invention is that a new computer method of material modeling is provided for crash test dummy finite element models. Another advantage of the present invention is that the method includes adjusting a relative volume (J) range for the material to make the material soft or stiff for a finite element model to be acceptable. Yet another advantage of the present invention is that the method solves both constrained and unconstrained optimization problems. Still another advantage of the present invention is that the method evaluates an objective function directly by putting in parameter values. A further advantage of the present invention is that the method solves highly nonlinear optimization problems. Yet a further advantage of the present invention is that the method solves optimization problems having a large number of parameters. Still a further advantage of the present invention is that method provides curve fitting of constitutive equations, resulting in large time savings and better qualities to users in model validations.
Other features and advantages of the present invention will be readily appreciated, as the same becomes better understood, after reading the subsequent description taken in conjunction with the accompanying drawings.
Referring to the drawings and in particular
Referring to
Strain Energy Functional
μj: parameters to be determined through curvefitting
αj: parameters to be determined through curvefitting
n: number of terms, up to 8
λ*i: principal stretches that volumetric effects have been eliminated
K: bulk modulus to be determined within acceptable range through curve fitting
J: relative volume to be determined within controlled range through curve fitting
Stress Relaxation by a Convolution Integral Form
Prony Series in Shear Relaxation
gijkl(t−τ): relaxation function for stress measure
Gi: shear moduli (to be determined through curve fitting)
βi: decay constant (to be determined through curve fitting)
N: up to 6 terms
The method then advances to block 124 and evaluates the work (W) for W0.1, W0.15, . . . , W0.m according to the equation as follows:
Uniaxial Test Principal Stretches
Relative Volume
Principal Stretches that Volumetric Effects have been eliminated
λ*i=J−⅓λi
Work from test=Work from strain energy functional
The method then advances to block 126 and calculates or minimizes the work error (E), which is an objective function, according to the following equation:
Error=(WT0−W*0)2+(WT0.1−W*0.1)2+(WT0.2−W*0.2)2+ . . . +(WT0.m−W*0.m)2
with the parameters to be determined:
The method then advances to diamond 128 and determines whether the work error (E) is less than a predetermined tolerance. If not, the method advances to block 130 and makes many trial vectors using MATLAB Genetic Algorithm by performing strain energy curve fitting as illustrate in
If the work error is less than the predetermined tolerance in diamond 128, the method advances to block 132 and outputs the variables α1, . . . , αs, μ1, . . . , μs, J0.1, J0.15, . . . , J0.m. The method then advances through bubble A to block 158 of
Referring to
The method advances to block 142 and makes the initial range for the variables G1, G2, . . . , Gn, β1, β2, . . . , βn based on testing and engineering judgment. The method advances to block 144 and checks lower and upper bounds for the variables
The method then advances to block 148 and evaluates the stress σ which is obtained in block 146 against test stress. The method advances to block 150 and minimizes the sum of stress errors (objective equation) at the several characteristic points over the time range between the stress test curve and the Prony series equations and calculates the error (E) as follows:
Error=(σ0−σP
with the parameters to be determined:
The method then advances to diamond 152 and determines whether the error (E) is less than a predetermined tolerance. If not, the method advances to block 154 and makes many trial vectors using MATLAB Genetic Algorithm by performing Prony series curve fitting as illustrate in
If the error is less that the predetermined tolerance in diamond 152, the method advances to block 156 and outputs the parameters G1, G2, . . . , Gn, β1, β2, . . . , βn. The method then advances through bubble B to block 158 of
Referring to
Referring to
The present invention has been described in an illustrative manner. It is to be understood that the terminology, which has been used, is intended to be in the nature of words of description rather than of limitation.
Many modifications and variations of the present invention are possible in light of the above teachings. Therefore, the present invention may be practiced other than as specifically described.
The present application claims the priority date of co-pending U.S. Provisional Patent Application Ser. No. 61/428,577, filed Dec. 30, 2010.
Number | Date | Country | |
---|---|---|---|
61428577 | Dec 2010 | US |