FRICTION STIR WELDING (FSW) PROCESS MODELING AND FSW JOINT DESIGN FOR BLAST SURVIVABLE STRUCTURES

In order to satisfy the need for better ballistic performance against lethal threats, new grades of Titanium (e.g. Ti-6Al-4V) and Aluminum (e.g. AA5083) alloys are being employed in the design of blast survivable structures. These better performing alloys are not readily amenable to conventional welding process or result in inferior welds when joined using conventional welding process. On the other hand, Friction Stir Welding (FSW), a relatively new welding process, has been found to be successful in producing good quality welds in these alloys. FSW also offers better weld performance in comparison with the conventional welding process. But the methodology for employing FSW to weld blast survivable structures remains unexplored. Therefore a robust and cost-efficient three-step process to Friction-Stir-Weld blast survivable structures is introduced in the present work. The first step in the proposed three-step methodology is to identify the FSW process parameters and tool design parameters that results in best quality welds and maximum productivity of the process. Since a purely experimental investigation of FSW process is expensive, computational Finite-Element-Analysis (FEA) procedures are incorporated in the methodology to reduce the amount of experimental investigation required. A fully-coupled thermo-mechanical FEA procedure is employed to investigate the spatial distribution and temporal evolution of material properties/microstructure with the FSW joints of Aluminum (AA5083) and Titanium (Ti-6Al-4V) work-pieces. In case of Ti-6Al-4V, the thermal history result from the computational analysis is used to determine the temporal evolution of the material microstructure in the weakest Heat-Affected-Zone (HAZ) region. Based on the well-established property vs. microstructure relationship for Ti-6Al-4V, and the temporal evolution of material microstructure for HAZ region, the overall structural performance of the weld is predicted. The computational results are compared with their corresponding experimental results found in open literature, and are found to be agreeable. In the second step, the optimal weld joint designs used in different regions of the blast survivable structures are identified. In the third step, problems regarding sub-scale modeling of blast survivable vehicle test structures are analyzed. The results obtained are used to analyze the potential of the current approach in enhancing blast survivability of military structures

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management

Fiber-Based Laser MOPA Transmitter Packaging for Space Environment

NASAs Goddard Space Flight Center has been developing lidar to remotely measure CO2 and CH4 in the Earths atmosphere. The ultimate goal is to make space-based satellite measurements with global coverage. We are working on maturing the technology readiness of a fiber-based, 1.57-micron wavelength laser transmitter designed for use in atmospheric CO2 remote-sensing. To this end, we are building a ruggedized prototype to demonstrate the required power and performance and survive the required environment. We are building a fiber-based master oscillator power amplifier (MOPA) laser transmitter architecture. The laser is a wavelength-locked, single frequency, externally modulated DBR operating at 1.57-micron followed by erbium-doped fiber amplifiers. The last amplifier stage is a polarization-maintaining, very-large-mode-area fiber with ~1000 m2 effective area pumped by a Raman fiber laser. The optical output is single-frequency, one microsecond pulses with >450 J pulse energy, 7.5 KHz repetition rate, single spatial mode, and > 20 dB polarization extinction

Fiber-Based Laser Transmitter Technology Maturation for Spectroscopic Measurements from Space

NASA's Goddard Space Flight Center has been developing lidar to remotely measure CO2 in the Earth's atmosphere. We have advanced the tunable laser technology to enable high-fidelity measurements from space. In this paper, we will report on the progress of fiber-based, 1.57-micron wavelength, laser transmitter that has demonstrated the optical performance required for a low earth orbiting instrument. The laser transmitter has been packaged and is undergoing environmental testing to demonstrate its technology readiness for space

Fast Edge Splitting and Edmonds’ Arborescence Construction for Unweighted Graphs

Given an unweighted undirected or directed graph with n vertices, m edges and edge connectivity c, we present a new deterministic algorithm for edge splitting. Our algorithm splits-off any specified subset S of vertices satisfying standard conditions (even degree for the undirected case and indegree ≥ out-degree for the directed case) while maintaining connectivity c for vertices outside S in Õ(m+nc 2) time for an undirected graph and Õ(mc) time for a directed graph. This improves the current best deterministic time bounds due to Gabow [8], who splits-off a single vertex in Õ(nc 2 + m) time for an undirected graph and Õ(mc) time for a directed graph. Further, for appropriate ranges of n, c, |S | it improves the current best randomized bounds due to Benczúr and Karger [2], who split-off a single vertex in an undirected graph i

An O(mn) Gomory-Hu Tree Construction Algorithm for Unweighted Graphs

We present a fast algorithm for computing a Gomory-Hu tree or cut tree for an unweighted undirected graph G = (V,E). The expected running time of our algorithm is Õ(mc) where |E| = m and c is the maximum u-vedge connectivity, where u,v ∈ V. When the input graph is also simple (i.e., it has no parallel edges), then the u-v edge connectivity for each pair of vertices u and v is at most n-1; so the expected running time of our algorithm for simple unweighted graphs is Õ(mn).All the algorithms currently known for constructing a Gomory-Hu tree [8,9] use n-1 minimum s-t cut (i.e., max flow) subroutines. This in conjunction with the current fastest Õ(n20/9) max flow algorithm due to Karger and Levine [11] yields the current best running time of Õ(n20/9n) for Gomory-Hu tree construction on simpleunweighted graphs with m edges and n vertices. Thus we present the first Õ(mn) algorithm for constructing a Gomory-Hu tree for simple unweighted graphs.We do not use a max flow subroutine here; we present an efficient tree packing algorithm for computing Steiner edge connectivity and use this algorithm as our main subroutine. The advantage in using a tree packing algorithm for constructing a Gomory-Hu tree is that the work done in computing a minimum Steiner cut for a Steiner set S ⊆ V can be reused for computing a minimum Steiner cut for certain Steiner sets S' ⊆ S