Fixed gain and adaptive techniques for rotorcraft vibration control

The results of an analysis effort performed to demonstrate the feasibility of employing approximate dynamical models and frequency shaped cost functional control law desgin techniques for helicopter vibration suppression are presented. Both fixed gain and adaptive control designs based on linear second order dynamical models were implemented in a detailed Rotor Systems Research Aircraft (RSRA) simulation to validate these active vibration suppression control laws. Approximate models of fuselage flexibility were included in the RSRA simulation in order to more accurately characterize the structural dynamics. The results for both the fixed gain and adaptive approaches are promising and provide a foundation for pursuing further validation in more extensive simulation studies and in wind tunnel and/or flight tests

A molecular superfluid: non-classical rotations in doped para-hydrogen clusters

Clusters of para-hydrogen (pH2) have been predicted to exhibit superfluid behavior, but direct observation of this phenomenon has been elusive. Combining experiments and theoretical simulations, we have determined the size evolution of the superfluid response of pH2 clusters doped with carbon dioxide (CO2). Reduction of the effective inertia is observed when the dopant is surrounded by the pH2 solvent. This marks the onset of molecular superfluidity in pH2. The fractional occupation of solvation rings around CO2 correlates with enhanced superfluid response for certain cluster sizes

Observational data formats and identification of supplementary documentation for the National Geodetic Satellite Program

Observational formats for geodetic tracking data and supporting documentation for data reductio

Adaptive simulation using mode identification

Adaptive simulation using modal clustering and method of potential function

Permeability of a one-dimensional potential barrier

Permeability of one dimensional potential barrie

Testing Reionization with Gamma Ray Burst Absorption Spectra

We propose to study cosmic reionization using absorption line spectra of high-redshift Gamma Ray Burst (GRB) afterglows. We show that the statistics of the dark portions (gaps) in GRB absorption spectra represent exquisite tools to discriminate among different reionization models. We then compute the probability to find the largest gap in a given width range [Wmax, Wmax + dW] at a flux threshold Fth for burst afterglows at redshifts 6.3 < z < 6.7. We show that different reionization scenarios populate the (Wmax, Fth) plane in a very different way, allowing to distinguish among different reionization histories. We provide here useful plots that allow a very simple and direct comparison between observations and model results. Finally, we apply our methods to GRB 050904 detected at z = 6.29. We show that the observation of this burst strongly favors reionization models which predict a highly ionized intergalactic medium at z~6, with an estimated mean neutral hydrogen fraction xHI = 6.4 \pm 0.3 \times 10^-5 along the line of sight towards GRB 050904.Comment: 5 pages, 3 figures, revised to match the accepted version; major change: gap statistics is now studied in terms of the flux threshold Fth, instead of the observed J-band flux FJ; MNRAS in pres

A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Current practice and challenges towards handling uncertainty for effective outcomes in maintenance

The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study. The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle