# Evaluation of nonlinear structural dynamic responses using a fast-running spring-mass formulation

## Abstract

In today`s world, accurate finite-element simulations of large nonlinear systems may require meshes composed of hundreds of thousands of degrees of freedom. Even with today`s fast computers and the promise of ever-faster ones in the future, central processing unit (CPU) expenditures for such problems could be measured in days. Many contemporary engineering problems, such as those found in risk assessment, probabilistic structural analysis, and structural design optimization, cannot tolerate the cost or turnaround time for such CPU-intensive analyses, because these applications require a large number of cases to be run with different inputs. For many risk assessment applications, analysts would prefer running times to be measurable in minutes. There is therefore a need for approximation methods which can solve such problems far more efficiently than the very detailed methods and yet maintain an acceptable degree of accuracy. For this purpose, we have been working on two methods of approximation: neural networks and spring-mass models. This paper presents our work and results to date for spring-mass modeling and analysis, since we are further along in this area than in the neural network formulation. It describes the physical and numerical models contained in a code we developed called STRESS, which stands formore »

- Authors:

- Publication Date:

- Research Org.:
- Sandia National Labs., Albuquerque, NM (United States)

- Sponsoring Org.:
- USDOE, Washington, DC (United States)

- OSTI Identifier:
- 46548

- Report Number(s):
- SAND-95-0349C; CONF-950788-2

ON: DE95008503

- DOE Contract Number:
- AC04-94AL85000

- Resource Type:
- Conference

- Resource Relation:
- Conference: International conference on computational engineering science, Mauna Lani, HI (United States), 30 Jul - 3 Aug 1995; Other Information: PBD: [1995]

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; 42 ENGINEERING NOT INCLUDED IN OTHER CATEGORIES; FINITE ELEMENT METHOD; S CODES; SPRINGS; EQUATIONS OF MOTION; NONLINEAR PROBLEMS; NEURAL NETWORKS

### Citation Formats

```
Benjamin, A.S., Altman, B.S., and Gruda, J.D.
```*Evaluation of nonlinear structural dynamic responses using a fast-running spring-mass formulation*. United States: N. p., 1995.
Web.

```
Benjamin, A.S., Altman, B.S., & Gruda, J.D.
```*Evaluation of nonlinear structural dynamic responses using a fast-running spring-mass formulation*. United States.

```
Benjamin, A.S., Altman, B.S., and Gruda, J.D. Wed .
"Evaluation of nonlinear structural dynamic responses using a fast-running spring-mass formulation". United States.
doi:. https://www.osti.gov/servlets/purl/46548.
```

```
@article{osti_46548,
```

title = {Evaluation of nonlinear structural dynamic responses using a fast-running spring-mass formulation},

author = {Benjamin, A.S. and Altman, B.S. and Gruda, J.D.},

abstractNote = {In today`s world, accurate finite-element simulations of large nonlinear systems may require meshes composed of hundreds of thousands of degrees of freedom. Even with today`s fast computers and the promise of ever-faster ones in the future, central processing unit (CPU) expenditures for such problems could be measured in days. Many contemporary engineering problems, such as those found in risk assessment, probabilistic structural analysis, and structural design optimization, cannot tolerate the cost or turnaround time for such CPU-intensive analyses, because these applications require a large number of cases to be run with different inputs. For many risk assessment applications, analysts would prefer running times to be measurable in minutes. There is therefore a need for approximation methods which can solve such problems far more efficiently than the very detailed methods and yet maintain an acceptable degree of accuracy. For this purpose, we have been working on two methods of approximation: neural networks and spring-mass models. This paper presents our work and results to date for spring-mass modeling and analysis, since we are further along in this area than in the neural network formulation. It describes the physical and numerical models contained in a code we developed called STRESS, which stands for ``Spring-mass Transient Response Evaluation for structural Systems``. The paper also presents results for a demonstration problem, and compares these with results obtained for the same problem using PRONTO3D, a state-of-the-art finite element code which was also developed at Sandia.},

doi = {},

journal = {},

number = ,

volume = ,

place = {United States},

year = {Wed Mar 01 00:00:00 EST 1995},

month = {Wed Mar 01 00:00:00 EST 1995}

}