An approximate methods approach to probabilistic structural analysis

The development of a three-dimensional inelastic analysis methodology for the Space Shuttle main engine (SSME) structural components is described. The methodology is composed of: (1) composite load spectra, (2) probabilistic structural analysis methods, (3) the probabilistic finite element theory, and (4) probabilistic structural analysis. The progress in the development of generic probabilistic models for various individual loads which consist of a steady state load, a periodic load, a random load, and a spike, is discussed. The capabilities of the Numerical Evaluation of Stochastic Structures Under Stress finite element code designed for probabilistic structural analysis of the SSME are examined. Variation principles for formulation probabilistic finite elements and a structural analysis for evaluating the geometric and material properties tolerances on the structural response of turbopump blades are being designed.

Probabilistic structural analysis methods for space propulsion system components

In probabilistic structural analysis, the performance or response functions usually are implicitly defined and must be solved by numerical analysis methods such as finite element methods. In such cases, the most commonly used probabilistic analysis tool is the mean-based, second-moment method which provides only the first two statistical moments. This paper presents a generalized advanced mean value (AMV) method which is capable of establishing the distributions to provide additional information for reliability design. The method requires slightly more computations than the second-moment method but is highly efficient relative to the other alternative methods. In particular, the examples show that the AMV method can be used to solve problems involving non-monotonic functions that result in truncated distributions.

Reliable and efficient vibroacoustic loads prediction is often critical in structural design, yet it remains a challenging task for many applications. Spacecraft structures are characterised by extensive use of composite materials, complex connections between components and various non-trivial geometrical features. Accurate treatment necessitates the construction of highly detailed numerical models, traditionally employing deterministic representations. Simultaneously, the broadband acoustic excitation due to the diffuse sound field experienced during launch requires modelling the fluid domain and solving the resulting elasto-acoustic interaction at = multiple frequencies. To alleviate the computational demand implications for large problem sizes, substructuring and reduction techniques for the structural domain are commonplace, component mode synthesis (CMS) being a framework widely adopted in the aerospace industry. Nevertheless, despite ongoing research, the topic still presents a range of difficulties when a universal, robust method of accounting for model uncertainties is sought. In this study, two CMS based approaches are proposed and evaluated. Firstly, the Craig-Bampton stochastic method (CBSM) is improved via a set of modifications enhancing its efficiency, and subsequently adapted for use in a vibroacoustic setting. Optimal perturbation levels and scope of validity of the technique are established against a probabilistic structural analysis (PSA) simulation for a spacecraft structure. Secondly, a novel stochastic finite element method (FEM) is presented. The underlying mathematical foundation is derived so that uncertainty can naturally be controlled at the subsystem level, in partitions of the corresponding condensed mass and stiffness matrices. This decomposition based approach ensures that realisations of the random matrices have key properties such as positive (semi)definiteness strictly preserved, guaranteeing complete robustness. The method is validated with a spacecraft test case, comparing its predictions against PSA, the improved CBSM and experimental data. A coupling scheme with a hierarchical matrix accelerated boundary element method is formulated, resulting in the construction of a complete stochastic vibroacoustic solver.

Derivation of site-specific remediation goals by incorporating the bioaccessibility of polycyclic aromatic hydrocarbons with the probabilistic analysis method

An effort has been ongoing to integrate manufacturing process simulations with probabilistic structural analyses in order to capture the important impacts of manufacturing uncertainties on component stress levels and life. A physics-based manufacturing process simulation code has been linked to the NESSUS structural analysis code to analyze annular deformation resistance welding manufacturing process. This paper describes the methodology developed to perform this integration, including an example. Although this effort is underway, particularly for fuller integration of a probabilistic analysis, the progress to date has been encouraging and a software interface that implements the methodology has been developed. The purpose of this paper is to report this development.

With the evolution of Internet and mobile intelligent devices, position based servers has become the borderline in the research area of information technology. WiFi technology is extensively exploited for efficient indoor floor planning due to low erection cost, suitable access, ease of expansion and popularization, etc. For effective indoor floor planning, Indoor Positioning System has to be designed initially so that objects are said to be located inside building wirelessly with minimum computational time and overhead. To explore this design, Ipin2016 Dataset is used, Principal Feature Enhanced Sampling with Auto Encoder for feature selection and built positioning model based on Kohonen Deep Structure. The method is called as Positioning of Wi-Fi devices for indoor floor planning using Principal Featured Kohonen Deep Structure (PF-KDS). First, spatial data analysis is carried out using Principal Feature Enhanced Auto Encoder algorithm. With this algorithm, principal features are first extracted, so that dimensionality reduction is said to achieve, therefore reducing the complexity involved in positioning. Next, with the dimensionality reduced features, Kohonen Self Organizing Deep Structured Learning algorithm is designed. In this algorithm, list of single medium access control (MAC) address and analogous Receiving Signal Strength (RSS) is recorded by considering a new path loss model including walls’ influence on RSSI, therefore improving the positioning accuracy. Our results indicate that combination of Principal Feature Enhanced Sampling with Kohonen Deep Structure provides high positioning accuracy with minimum time and overhead for Indoor Positioning.

Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale systems. In this paper, a new analytical and non-iterative method is presented to solve UC problem. In the proposed method, improved pre-prepared power demand (IPPD) table is used to solve UC problem, and then analytical “λ-logic” algorithm is used to solve economic dispatch (ED) sub-problem. The analytical and non-iterative nature of the mentioned methods results in simplification of the UC problem solution. Obtaining minimum cost in very small time with only one run is the major advantage of the proposed method. The proposed method has been tested on 10 unit and 40-100 unit systems with consideration of different constraints, such as: power generation limit of units, reserve constraints, minimum up and down times of generating units. Comparing the simulation results of the proposed method with other methods in literature shows that in large scale systems, the proposed method achieves minimum operational cost within minimum computational time.

An efficient method for probabilistic finite element analysis with application to contact assessment of CANDU fuel channels

Probability analysis of rock slope using FORM based on a nonlinear strength criterion

The traditional decision-making process during early design stages deals with deterministically evaluating the design candidates in accordance with concrete objectives by assuming optimal or nominal design performance values for the candidates. However, this may increase subjectivity in the decision process since the design knowledge during early design is usually imprecise and incomplete, and mostly needs to be iteratively updated throughout product design development. To diminish the subjectivity, the knowledge of the design requirements can be precisely and accurately represented by means of probabilistic constraints that describe the uncertainties in the design requirements; therefore, in this work, a systematic design framework supported by reliability analysis is developed in such a way that it is able to provide an effective connection among the early design steps especially both at system level and component level. Thus, the probability of failures of the design candidates and their sub-solutions are investigated, based on design constraints with Gaussian distributions, or lower and upper bounds, by utilizing Monte Carlo method. To illustrate the potential applicability and efficacy of the proposed framework, a two-finger gripper design problem is considered. The results clearly demonstrate that the proposed framework is effective to achieve reliable design solutions that have uncertain quantitative characteristics to be used further in probabilistic structural analysis during the next design stages such as embodiment and detail design stages.

Physics-informed probabilistic slow feature analysis

PurposeThis study was conducted to evaluate the effect of alcohol consumption on breast cancer, adjusting for alcohol consumption misclassification bias and confounders.MethodsThis was a case-control study of 932 women with breast cancer and 1000 healthy control. Using probabilistic bias analysis method, the association between alcohol consumption and breast cancer was adjusted for the misclassification bias of alcohol consumption as well as a minimally sufficient set of adjustment of confounders derived from a causal directed acyclic graph. Population attributable fraction was estimated using the Miettinen’s Formula.ResultsBased on the conventional logistic regression model, the odds ratio estimate between alcohol consumption and breast cancer was 1.05 (95% CI: 0.57, 1.91). However, the adjusted estimates of odds ratio based on the probabilistic bias analysis ranged from 1.82 to 2.29 for non-differential and from 1.93 to 5.67 for differential misclassification. Population attributable fraction ranged from 1.51 to 2.57% using non-differential bias analysis and 1.54–3.56% based on differential bias analysis.ConclusionA marked measurement error was in self-reported alcohol consumption so after correcting misclassification bias, no evidence against independence between alcohol consumption and breast cancer changed to a substantial positive association.

Based on Monte Carlo and interior point method, this paper presents a probabilistic optimal power flow analysis method for the virtual power plant (VPP) containing photovoltaic (PV) generation, which can take into account both the randomness and correlation of PV generators' power outputs and loads at different locations. An optimal power flow model of VPP is firstly given to minimize the operation cost of VPP. The inverse transformation method and Cholesky decomposition technique are used to simulate the correlations between PV power outputs and loads, which obey Beta and Normal distributions respectively. Finally, the modified IEEE 30-bus test system is used to demonstrate the effectiveness and application of the presented method in the probabilistic optimal power flow analysis of VPP.

An electrical power system is typically operated at least cost given some technical and reliability constraints. To that end, many utilities use deterministic methods such as n−1 criterion in order to ensure reliability. This method though being used worldwide and for a really long time can be expensive as contingencies occur rarely. In order to evaluate power systems, it must be noted that contingencies are stochastic in nature. Since reliability of supply and economic cost must be balanced, probabilistic analysis seems to be one of the tools which can be applied. In the case of generator dispatch, the most recognized probability tool is the Capacity Outage Probability Table (COPT). However, the synthesis of the COPT using the Recursive Method as proposed by Billinton and Allan can be cumbersome. This paper presents an integration of probabilistic and deterministic methods for outage analysis. It shows an alternative method to develop the COPT using a pseudo-deterministic method combined with a Monte Carlo method. Thus, making the process of developing a COPT simpler and can reduce computation time.