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At least 127 records · Page 7

Structure-borne noise transmission in stiffened structures

The structure-borne noise transmission of stiffened and interconnected structures under random loads is presented. The method is based on the transfer matrix for the structural response and on the modal decomposition for the interior acoustic field. The acoustic enclosure is taken to be rectangular in shape of which portion of the boundaries are elastic while the remaining surface is acoustically rigid. Numerical results are presented for several acousto-structural problems.

Vaicaitis, Rimas↗

Efficient probabilistic fracture mechanics analysis

A systematic and efficient method for probabilistic fracture mechanics analysis is proposed. The method is based on a most-probable-point-locus concept. The locus is obtained iteratively where the initial locus is determined using the linear approximation of the service life N(X) function about the mean values of the random variables X. Linear and quadratic approximations of N(X) are established locally at the most probable points, and the reliability analysis methods are used to compute the cumulative probabilities. By using two examples, the proposed method is demonstrated to be efficient and accurate. One example involved a random loading and N(X) was computed using cycle-by-cycle integration. The method is general and can be applied to other performance functions. It is particularly suitable when the computation of the performance function is time consuming such that Monte Carlo simulation is prohibitively costly.

Wu, Y.-T.↗

Structure-borne noise generation and transmission

The structure-borne noise generation and transmission of stiffened and interconnected structures under random loads is presented. The method is based on the transfer matrix for the structural response and on the modal decomposition for the interior acoustic field. The acoustic enclosure is taken to be rectangular in shape of which portion of the boundaries are elastic while the remaining surface is acoustically rigid. Numerical results are presented for a variety of acousto-structural problems.

Lyrintzis, Constantinos S.↗

Practical theories for service life prediction of critical aerospace structural components

A new second-order theory was developed for predicting the service lives of aerospace structural components. The predictions based on this new theory were compared with those based on the Ko first-order theory and the classical theory of service life predictions. The new theory gives very accurate service life predictions. An equivalent constant-amplitude stress cycle method was proposed for representing the random load spectrum for crack growth calculations. This method predicts the most conservative service life. The proposed use of minimum detectable crack size, instead of proof load established crack size as an initial crack size for crack growth calculations, could give a more realistic service life.

Ko, William L.↗

Effect of spectral shape on acoustic fatigue life estimates

Methods for estimating fatigue life due to random loading are briefly reviewed. These methods include a probabilistic approach in which the expected value of the rate of damage accumulation is computed by integrating over the probability density of damaging events and a method which consists of analyzing the response time history to count damaging events. It is noted that it is necessary to employ a time domain approach to perform Rainflow counting, while simple peak counting may be accomplished using the probabilistic method. Data obtained indicate that Rainflow counting produces significantly different fatigue life predictions than other methods that are commonly used in acoustic fatigue predictions. When low-frequency oscillations are present in a signal along with high-frequency components, peak counting will produce substantially shorter fatigue lives than Rainflow counting. It is concluded that Rainflow counting is capable of providing reliable fatigue life predictions for acoustic fatigue studies.

Miles, R. N.↗

Probabilistic structural analysis algorithm development for computational efficiency

The PSAM (Probabilistic Structural Analysis Methods) program is developing a probabilistic structural risk assessment capability for the SSME components. An advanced probabilistic structural analysis software system, NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), is being developed as part of the PSAM effort to accurately simulate stochastic structures operating under severe random loading conditions. One of the challenges in developing the NESSUS system is the development of the probabilistic algorithms that provide both efficiency and accuracy. The main probability algorithms developed and implemented in the NESSUS system are efficient, but approximate in nature. In the last six years, the algorithms have improved very significantly.

Wu, Y.-T.↗

Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders: Preprint

This paper presents simulation results for a taxonomy of typical distribution feeders with various levels of photovoltaic (PV) penetration. For each of the 16 feeders simulated, the maximum PV penetration that did not result in steady-state voltage or current violation is presented for several PV location scenarios: clustered near the feeder source, clustered near the midpoint of the feeder,clustered near the end of the feeder, randomly located, and evenly distributed. In addition, the maximum level of PV is presented for single, large PV systems at each location. Maximum PV penetration was determined by requiring that feeder voltages stay within ANSI Range A and that feeder currents stay within the ranges determined by overcurrent protection devices. Simulations were run inGridLAB-D using hourly time steps over a year with randomized load profiles based on utility data and typical meteorological year weather data. For 86% of the cases simulated, maximum PV penetration was at least 30% of peak load.

14 SOLAR ENERGY↗

Emergency Voltage Regulation in Power Systems via Ripple-Type Control: Preprint

With increasing penetration of volatile renewable generation and cyber-physical disruptions, ensuring safe operation of bulk power systems has become unprecedentedly challenging. Since communication and computational costs restrict centralized system dispatch to be called upon every few minutes; and purely local schemes are shown to be insufficient, distributed controls have been advocated for handling unanticipated system conditions in real time. However, the applicability of distributed control schemes is fundamentally limited by their need for widespread communication and model cognizance. In this con-text, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a ‘hotline’ communication network. Under this protocol, when agents observe a constraint violation based on local measurements, they respond locally until their control resources saturate, in which case they send a beacon for assistance to peer agents. The scheme ensures that minor violations are efficiently mitigated via fast local controls, while severe violations may be handled by a collaboration of a relatively small set of agents. We evaluate the performance of this scheme via extensive numerical tests on the IEEE 14-bus test feeder, where agents act upon noisy measurements under diverse scenarios of random load variations and severe low/high voltage events.

distributed control↗

Random vibration analysis of space flight hardware using NASTRAN

During liftoff and ascent flight phases, the Space Transportation System (STS) and payloads are exposed to the random acoustic environment produced by engine exhaust plumes and aerodynamic disturbances. The analysis of payloads for randomly fluctuating loads is usually carried out using the Miles' relationship. This approximation technique computes an equivalent load factor as a function of the natural frequency of the structure, the power spectral density of the excitation, and the magnification factor at resonance. Due to the assumptions inherent in Miles' equation, random load factors are often over-estimated by this approach. In such cases, the estimates can be refined using alternate techniques such as time domain simulations or frequency domain spectral analysis. Described here is the use of NASTRAN to compute more realistic random load factors through spectral analysis. The procedure is illustrated using Spacelab Life Sciences (SLS-1) payloads and certain unique features of this problem are described. The solutions are compared with Miles' results in order to establish trends at over or under prediction.

Thampi, S. K.↗

A reliability-based cost effective fail-safe design procedure

The authors have developed a methodology for cost-effective fatigue design of structures subject to random fatigue loading. A stochastic model for fatigue crack propagation under random loading has been discussed. Fracture mechanics is then used to estimate the parameters of the model and the residual strength of structures with cracks. The stochastic model and residual strength variations have been used to develop procedures for estimating the probability of failure and its changes with inspection frequency. This information on reliability is then used to construct an objective function in terms of either a total weight function or cost function. A procedure for selecting the design variables, subject to constraints, by optimizing the objective function has been illustrated by examples. In particular, optimum design of stiffened panel has been discussed.

Hanagud, S.↗

Probalistic Finite Elements (PFEM) structural dynamics and fracture mechanics

The purpose of this work is to develop computationally efficient methodologies for assessing the effects of randomness in loads, material properties, and other aspects of a problem by a finite element analysis. The resulting group of methods is called probabilistic finite elements (PFEM). The overall objective of this work is to develop methodologies whereby the lifetime of a component can be predicted, accounting for the variability in the material and geometry of the component, the loads, and other aspects of the environment; and the range of response expected in a particular scenario can be presented to the analyst in addition to the response itself. Emphasis has been placed on methods which are not statistical in character; that is, they do not involve Monte Carlo simulations. The reason for this choice of direction is that Monte Carlo simulations of complex nonlinear response require a tremendous amount of computation. The focus of efforts so far has been on nonlinear structural dynamics. However, in the continuation of this project, emphasis will be shifted to probabilistic fracture mechanics so that the effect of randomness in crack geometry and material properties can be studied interactively with the effect of random load and environment.

Liu, Wing-Kam↗

Nonlinear Acoustic Response of an Aircraft Fuselage Sidewall Structure by a Reduced-Order Analysis

A reduced-order nonlinear analysis of a structurally complex aircraft fuselage sidewall panel is undertaken to explore issues associated with application of such analyses to practical structures. Of primary interest is the trade-off between computational efficiency and accuracy. An approach to modal basis selection is offered based upon the modal participation in the linear regime. The nonlinear static response to a uniform pressure loading and nonlinear random response to a uniformly distributed acoustic loading are computed. Comparisons of the static response with a nonlinear static solution in physical degrees-of-freedom demonstrate the efficacy of the approach taken for modal basis selection. Changes in the modal participation as a function of static and random loading levels suggest a means for improvement in the basis selection.

Przekop, Adam↗

Weather and Random Forest-based Load Profiling Approximation Models and its Transferability across Climate Zones

This study is to provide predictive understanding of the associations of various weather attributes with residential and commercial load profiles, for a variety of climate zones and seasons. In this work, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked/screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons).

load composite, Random Forest, regression tree, lo↗