Engineering topics

Hasselman, T. K.

Publications and source records attributed to Hasselman, T. K..

At least 19 records

NASA NTRS1993

Estimation of full modal damping matrices from complex test modes

This paper describes the refinements of a previously published method for estimating a full modal damping matrix from complex test modes. It also documents application of the refined method to a structure where complex test modes were derived by the ERA method from multi-input random vibration test data. A numerical example based on simulated test data is presented to demonstrate the validity of the method. The application using real data was not successful, presumably because of noise in the small phase angles of the measured complex modes. Alternative test and data reduction procedures are suggested as possible remedies to the problem. A careful analysis of measurement and data processing errors should be made to examine basic feasibility before implementing the alternative procedures. The ability to estimate a full modal damping matrix is considered important for the preflight estimation of on-orbit damping, and for the synthesis of structural damping from substructure tests.

Hasselman, T. K.↗

NASA NTRS1991

A recent case study in system identification

Results of a recent study of a ten-bay truss structure at the NASA Langley Research Center are reported. First, the conditioning of complex eigenvectors derived by the ERA method is discussed. Results of parameter estimation using the SSID (Structural System Identification) code are then presented. Based on the results of the study, it is concluded that (1) parameter estimation based on modal data should include eigenvectors as well as eigenvalues; (2) the eigenvectors should be orthogonalized when orthogonality is poor due to closely spaced modes; and (3) the parameters used in the estimation should enable the model to match the data.

Hasselman, T. K.↗

NASA NTRS1990

Methods for evaluating the predictive accuracy of structural dynamic models

Uncertainty of frequency response using the fuzzy set method and on-orbit response prediction using laboratory test data to refine an analytical model are emphasized with respect to large space structures. Two aspects of the fuzzy set approach were investigated relative to its application to large structural dynamics problems: (1) minimizing the number of parameters involved in computing possible intervals; and (2) the treatment of extrema which may occur in the parameter space enclosed by all possible combinations of the important parameters of the model. Extensive printer graphics were added to the SSID code to help facilitate model verification, and an application of this code to the LaRC Ten Bay Truss is included in the appendix to illustrate this graphics capability.

Hasselman, T. K.↗

NASA NTRS1990

Statistical analysis of modeling error in structural dynamic systems

The paper presents a generic statistical model of the (total) modeling error for conventional space structures in their launch configuration. Modeling error is defined as the difference between analytical prediction and experimental measurement. It is represented by the differences between predicted and measured real eigenvalues and eigenvectors. Comparisons are made between pre-test and post-test models. Total modeling error is then subdivided into measurement error, experimental error and 'pure' modeling error, and comparisons made between measurement error and total modeling error. The generic statistical model presented in this paper is based on the first four global (primary structure) modes of four different structures belonging to the generic category of Conventional Space Structures (specifically excluding large truss-type space structures). As such, it may be used to evaluate the uncertainty of predicted mode shapes and frequencies, sinusoidal response, or the transient response of other structures belonging to the same generic category.

Hasselman, T. K.↗

NASA NTRS1989

Evaluation of predictive accuracy in structural dynamic models

The evaluation of the predictive accuracy of dynamic models for future large space structures is addressed. Mass and stiffness uncertainties derived from a comparison of analytical and experimental modes are used to evaluate the uncertainty of response predictions based on the analytical model.

Hasselman, T. K.↗

NASA NTRS1988

Identification of large structures on orbit - A survey

This paper seeks to provide a brief overview of the somewhat unfamiliar concept underlying system identification especially as it applies to large flexible space structures. Having elaborated on the concept, the authors provide a detailed description of the identification process including model development, its experimental validation and final certification. This discussion is followed by a classification of the different identification methods and a brief evaluation of the potential of existing methodology to address special circumstances of large flexible space structures. The paper concludes by making a few recommendations that are deemed necessary to meet the enormous challenges posed by the deployment or erection of large space structures.

Denman, Eugene E.↗

NASA NTRS1988

Identification of large space structures - Overview

The system identification process presently discussed for the case of large space structures uses the observed input to a system and its observed response, or output, to derive an analytical model of the system which can then be used to predict its response to future inputs. Due to their size and complexity, as well as the intrinsic difficulty of identifying the environment in which they function, large space structures will require vast amounts of information, encompassing both experimental and analytical data for identification. A status evaluation is made of the structural system identification literature to date.

Denman, Eugene↗

NASA NTRS1988

Identification of large space structures on orbit : A survey

The Task Committee on Methods for Identification of Large Structures in Space was founded in Jul. 1984. The charter of the committee was to prepare a state-of-the-art report on methods of system identification applicable to large space structures (LSS). Funding to support preparation of the report was received in Aug. 1985 from the Air Force Rocket Propulsion Laboratory (now the Air Force Astronautics Laboratory), in the form of a contract to the ASCE. The report was completed, and published by AFRPL in Sep. 1986. The Task Committee consisted of ten members, including ASCE and AFRPL representatives. The membership represented Government, Industry, and Universities, and consisted of electrical, mechanical, and civil engineers, with backgrounds in Structural Dynamics, Optimization, and Controls. An effort was made to use consistent terminology and notation throughout the report which would be compatible with the terminology used in both the structures and controls communities.

Denman, Eugene E.↗

NASA NTRS1988

Effects of model deficiencies on parameter estimation

Reliable structural dynamic models will be required as a basis for deriving the reduced-order plant models used in control systems for large space structures. Ground vibration testing and model verification will play an important role in the development of these models; however, fundamental differences between the space environment and earth environment, as well as variations in structural properties due to as-built conditions, will make on-orbit identification essential. The efficiency, and perhaps even the success, of on-orbit identification will depend on having a valid model of the structure. It is envisioned that the identification process will primarily involve parametric methods. Given a correct model, a variety of estimation algorithms may be used to estimate parameter values. This paper explores the effects of modeling errors and model deficiencies on parameter estimation by reviewing previous case histories. The effects depend at least to some extent on the estimation algorithm being used. Bayesian estimation was used in the case histories presented here. It is therefore conceivable that the behavior of an estimation algorithm might be useful in detecting and possibly even diagnosing deficiencies. In practice, the task is complicated by the presence of systematic errors in experimental procedures and data processing and in the use of the estimation procedures themselves.

Hasselman, T. K.↗

NASA NTRS1987

A computer program for model verification of dynamic systems

Dynamic model verification is the process whereby an analytical model of a dynamic system is compared with experimental data, and then qualified for future use in predicting system response in a different dynamic environment. There are various ways to conduct model verification. The approach adopted in MOVER II employs Bayesian statistical parameter estimation. Unlike curve fitting whose objective is to minimize the difference between some analytical function and a given quantity of test data (or curve), Bayesian estimation attempts also to minimize the difference between the parameter values of that function (the model) and their initial estimates, in a least squares sense. The objectives of dynamic model verification, therefore, are to produce a model which: (1) is in agreement with test data, (2) will assist in the interpretation of test data, (3) can be used to help verify a design, (4) will reliably predict performance, and (5) in the case of space structures, facilitate dynamic control.

Chrostowski, J. D.↗

NASA NTRS1984

Component testing for dynamic model verification

Dynamic model verification is the process whereby an analytical model of a dynamic system is compared with experimental data, adjusted if necessary to bring it into agreement with the data, and then qualified for future use in predicting system response in a different dynamic environment. These are various ways to conduct model verification. The approach taken here employs Bayesian statistical parameter estimation. Unlike curve fitting, whose objective is to minimize the difference between some analytical function and a given quantity of test data (or curve), Bayesian estimation attempts also to minimize the difference between the parameter values of that funciton (the model) and their initial estimates, in a least squares sense. The objectives of dynamic model verification, therefore, are to produce a model which: (1) is in agreement with test data; (2) will assist in the interpretation of test data; (3) can be used to help verify a design; (4) will reliably predict performance; and (5) in the case of space structures, will facilitate dynamic control.

Hasselman, T. K.↗

NASA NTRS1982

Model Verification of Mixed Dynamic Systems

MOVER uses experimental data to verify mathematical models of "mixed" dynamic systems. The term "mixed" refers to interactive mechanical, hydraulic, electrical, and other components. Program compares analytical transfer functions with experiment.

Evensen, D. A.↗

NASA NTRS1978

Dynamic model verification of large structural systems

The objective of the present methodology is two-fold: (1) to process test data obtained from either modal survey tests, or slow sine-sweep tests, to extract a set of orthogonal modes best matching the test data while being commensurate with the dynamic model, and (2) to modify submatrices of the dynamic model mass and stiffness matrices to adjust the model to best fit the test data. The method has been implemented using a linear statistical sequential estimator for computation on a CDC computer. Demonstration problems involving Space Shuttle quarter-scale vibration test data and dynamic models have been run. This paper will discuss the general methodology and experience to date.

Lee, L. T.↗

NASA NTRS1978

Model verification of large structural systems

A computer program for the application of parameter identification on the structural dynamic models of space shuttle and other large models with hundreds of degrees of freedom is described. Finite element, dynamic, analytic, and modal models are used to represent the structural system. The interface with math models is such that output from any structural analysis program applied to any structural configuration can be used directly. Processed data from either sine-sweep tests or resonant dwell tests are directly usable. The program uses measured modal data to condition the prior analystic model so as to improve the frequency match between model and test. A Bayesian estimator generates an improved analytical model and a linear estimator is used in an iterative fashion on highly nonlinear equations. Mass and stiffness scaling parameters are generated for an improved finite element model, and the optimum set of parameters is obtained in one step.

Lee, L. T.↗

NASA NTRS1978

Model verification of mixed dynamic systems

A parameter-estimation method is described for verifying the mathematical model of mixed (combined interactive components from various engineering fields) dynamic systems against pertinent experimental data. The model verification problem is divided into two separate parts: defining a proper model and evaluating the parameters of that model. The main idea is to use differences between measured and predicted behavior (response) to adjust automatically the key parameters of a model so as to minimize response differences. To achieve the goal of modeling flexibility, the method combines the convenience of automated matrix generation with the generality of direct matrix input. The equations of motion are treated in first-order form, allowing for nonsymmetric matrices, modeling of general networks, and complex-mode analysis. The effectiveness of the method is demonstrated for an example problem involving a complex hydraulic-mechanical system.

Chrostowski, J. D.↗

NASA NTRS1977

Model verification of large structural systems

A methodology was formulated, and a general computer code implemented for processing sinusoidal vibration test data to simultaneously make adjustments to a prior mathematical model of a large structural system, and resolve measured response data to obtain a set of orthogonal modes representative of the test model. The derivation of estimator equations is shown along with example problems. A method for improving the prior analytic model is included.

Lee, L. T.↗

NASA NTRS1974

Model optimization using statistical estimation

Program revises initial or prior estimate of stiffness and mass parameters to parameters yielding frequency and mode characteristics in agreement with test data. Variances are also calculated and consequently define uncertainties of final estimates.

Collins, J. D.↗

NASA NTRS1974

Damping synthesis from substructure tests

A new method is proposed for synthesizing structural damping from substructure test data. It utilizes the off-diagonal coupling terms in the substructure modal damping matrices as well as the diagonal terms which correspond to uncoupled modal damping. The coupling terms are evaluated from complex resonant response measured at each substructure mode. Both analytical and experimental verification of the method have been made. Accuracy requirements on experimental data and data reduction have been established.

Hasselman, T. K.↗