Review of fatigue and fracture research at NASA Langley Research Center

Most dynamic components in helicopters are designed with a safe-life constant-amplitude testing approach that has not changed in many years. In contrast, the fatigue methodology in other industries has advanced significantly in the last two decades. Recent research at the NASA Langley Research Center and the U.S. Army Aerostructures Directorate at Langley are reviewed relative to fatigue and fracture design methodology for metallic components. Most of the Langley research was directed towards the damage tolerance design approach, but some work was done that is applicable to the safe-life approach. In the areas of testing, damage tolerance concepts are concentrating on the small-crack effect in crack growth and measurement of crack opening stresses. Tests were conducted to determine the effects of a machining scratch on the fatigue life of a high strength steel. In the area of analysis, work was concentrated on developing a crack closure model that will predict fatigue life under spectrum loading for several different metal alloys including a high strength steel that is often used in the dynamic components of helicopters. Work is also continuing in developing a three-dimensional, finite-element stress analysis for cracked and uncracked isotropic and anisotropic structures. A numerical technique for solving simultaneous equations called the multigrid method is being pursued to enhance the solution schemes in both the finite-element analysis and the boundary element analysis. Finally, a fracture mechanics project involving an elastic-plastic finite element analysis of J-resistance curve is also being pursued

INFLATION AND ITS CONTROL

Financial Economics,

Does Intercultural-Based Professional Development have an Influence on Employees of a Multicultural Urban College

This study was created to determine if intercultural professional development influences intercultural competence scores among higher education staff through the use of the Intercultural Developmental Inventory (IDI) and Developmental Model of Intercultural Sensitivity (DMIS). The participants in the study included a small group of higher education staff working in the Office of Student Affairs at a small urban college in the central Midwest. The structure of the study included pre-testing using the IDI, 6 hours of professional development, personalized feedback and IDI post-testing. The professional development was customized based on framework of DMIS and was further modified based on the individual and group orientation stage results. The study concluded that professional development that is based on the Intercultural Development Inventory and the Developmental Model of Intercultural Sensitivity theories, positively impacts the intercultural competence scores on the Interculfural Developmental Inventory

Automating Large-Scale Simulation Calibration to Real-World Sensor Data

Many key decisions and design policies are made using sophisticated computer simulations. However, these sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine\u27s capabilities. This dissertation\u27s goal is to address these simulation deficiencies by presenting a general automated process for tuning simulation inputs such that simulation output matches real world measured data. The automated process involves the following key components -- 1) Identify a model that accurately estimates the real world simulation calibration target from measured sensor data; 2) Identify the key real world measurements that best estimate the simulation calibration target; 3) Construct a mapping from the most useful real world measurements to actual simulation outputs; 4) Build fast and effective simulation approximation models that predict simulation output using simulation input; 5) Build a relational model that captures inter variable dependencies between simulation inputs and outputs; and finally 6) Use the relational model to estimate the simulation input variables from the mapped sensor data, and use either the simulation model or approximate simulation model to fine tune input simulation parameter estimates towards the calibration system. The work in this dissertation individually validates and completes five out of the six calibration components with respect to the residential energy domain. Step 1 is satisfied by identifying the best model for predicting next hour residential electrical consumption, the calibration target. Step 2 is completed by identifying the most important sensors for predicting residential electrical consumption, the real world measurements. While step 3 is completed by domain experts, step 4 is addressed by using techniques from the Big Data machine learning domain to build approximations for the EnergyPlus (E+) simulator. Step 5\u27s solution leverages the same Big Data machine learning techniques to build a relational model that describes how the simulator\u27s variables are probabilistically related. Finally, step 6 is partially demonstrated by using the relational model to estimate simulation parameters for E+ simulations with known ground truth simulation inputs

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures, conference submission, University of Oxford. arXiv admin note: text overlap with arXiv:1709.0235

Real-time quality assurance testing using photonic techniques: Application to iodine water system

A feasibility study of the use of inspection systems incorporating photonic sensors and multivariate analyses to provide an instrumentation system that in real-time assures quality and that the system in control has been conducted. A system is in control when the near future of the product quality is predictable. Off-line chemical analyses can be used for a chemical process when slow kinetics allows time to take a sample to the laboratory and the system provides a recovery mechanism that returns the system to statistical control without intervention of the operator. The objective for this study has been the implementation of do-it-right-the-first-time and just-in-time philosophies. The Environment Control and Life Support Systems (ECLSS) water reclamation system that adds iodine for biocidal control is an ideal candidate for the study and implementation of do-it-right-the-first-time technologies

A Theory of Entrepreneurial Overconfidence, Effort, and Firm Outcomes

We present a theory of entrepreneurial behavior that explores the relationship between overconfidence and successful firm outcomes, such as acquisition or IPO. In our model, increasing overconfidence produces two conflicting effects on the probability of a successful outcome: it not only induces an entrepreneur to increase the riskiness of a venture (which lowers the likelihood of successful exit), but also drives higher entrepreneurial effort, increasing likelihood of a successful exit. Due to this conflict, a kinked or U-shaped relationship may exist between overconfidence and positive outcomes. Furthermore, our model suggests that increased outside equity mitigates the effects of overconfidence