The meteorological measurement system on the NASA ER-2 aircraft

A Meteorological Measurement System (MMS) was designed for the high-altitude ER-2 aircraft (NASA 706). Through dedicated instrumentation installed on the aircraft and repeated calibrations, the MMS provides accurate in situ measurements of free-stream pressure, temperature, and the wind vector. The MMS has participated in two major high-altitude scientific expeditions, the Stratosphere-Troposphere Exchange Project (STEP) based in northern Australia and the Airborne Antarctic Ozone Experiment (AAOE) based in southern Chile. Key MMS subsystems are described. The MMS consists of a dedicated inertial navigation system (INS), a randome differential pressure system, a data acquisition system, and air data instrumentation. The MMS incorporates a high-resolution INS (Litton LIN-72RH model), which is specially configured and is updated at 25 Hz. The differential pressure system, consisting of two sets of pressure ports and transducers, is installed in the ER-2 radome to provide sensitive measurements of the airflow angles (angle of attack and angle of sideslip). The data acquisition system was designed to meet aircraft requirements of compactness and light weight (2 cu ft 50 lb) and for MMS requirements to sample, control, process, and store 45 parameters (some redundant) at a sampling rate up to 10 Hz. The MMS data are stored both in a tape recorder (20 MB) and a hermatically-sealed winchester hard disc (10 MB). Special and redundant instrumentation for temperature and pressure measurements were also installed on the aircraft

The NASA-ER2 meteorological measurement system: Instrumentaion, calibration and intercomparison results

The NASA ER-2 aircraft is used as a platform for high altitude atmospheric missions. The Meteorological Measurement System (MMS) was designed specifically for atmospheric research to provide accurate, fast response, in situ measurements of pressure, temperature, and the three dimensional wind vector. The MMS consists of three subsystems: an air motion sensing system to measure the velocity of the air with respect to the aircraft, a high resolution Inertial Navigation System (INS) to measure the velocity of the aircraft with respect to the Earth, and a Data Acquisition System, to sample, process and record the measured quantities. Details of each of these systems are given. The location of the MMS instrumentation is illustrated. The calibration of the MMS is discussed and results on an intercomparison of MMS measurements, Vaisala radiosonde observation and radar tracking data are given. An illustration of the MMS measurement of vertical wind is given

Temperature and horizontal wind measurements on the ER-2 aircraft during the 1987 airborne Antarctic ozone experiment

The NASA ER-2 aircraft is equipped with special instrumentation to provide accurate in situ measurement of the atmospheric state variables during flight. The Meteorological Measurement System (MMS) on the ER-2 aircraft is described. Since the meteorological parameters (temperature, pressure, and wind vector) are extensively used by other ER-2 experimenters for data processing and interpretation, the accuracy and resolution of each of these parameters are assessed and discussed. During the 1987 Airborne Antarctic Ozone Experiment (AAOE) mission, the ER-2 aircraft was stationed at Punta Arenas, Chile (53 S, 72 W), and successfully flew over Antarctica on 12 occasions between August 17 and September 22, 1987. On each of the 12 flights, the ER-2 aircraft flight plan was to take off at approximately the same local time, fly southward at a near constant potential temperature surface, descend and ascend at the southernmost terminus at about 72 S over Antarctica and return northward at either the same or a different constant potential temperature surface. The measurements of the MMS experiment during the AAOE mission are presented. MMS data are organized to provide a composite view of the polar atmosphere, which is characterized by frigid temperatures and high zonal winds. Altitudinal variations of the temperature measurement (during takeoff/landing at Punta Arenas and during descent/ascent at the southern terminus) and latitudinal variations of the zonal wind (on near constant potential temperature surfaces) are emphasized and discussed

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

Risk Assessment Overview

Risk assessment is used in many industries to identify and manage risks. Initially developed for use on aeronautical and nuclear systems, risk assessment has been applied to transportation, chemical, computer, financial, and security systems among others. It is used to gain an understanding of the weaknesses or vulnerabilities in a system so modification can be made to increase operability, efficiency, and safety and to reduce failure and down-time. Risk assessment results are primary inputs to risk-informed decision making; where risk information including uncertainty is used along with other pertinent information to assist management in the decision-making process. Therefore, to be useful, a risk assessment must be directed at specific objectives. As the world embraces the globalization of trade and manufacturing, understanding the associated risk become important to decision making. Applying risk assessment techniques to a global system of development, manufacturing, and transportation can provide insight into how the system can fail, the likelihood of system failure and the consequences of system failure. The risk assessment can identify those elements that contribute most to risk and identify measures to prevent and mitigate failures, disruptions, and damaging outcomes. In addition, risk associated with public and environment impact can be identified. The risk insights gained can be applied to making decisions concerning suitable development and manufacturing locations, supply chains, and transportation strategies. While risk assessment has been mostly applied to mechanical and electrical systems, the concepts and techniques can be applied across other systems and activities. This paper provides a basic overview of the development of a risk assessment

A redistribution of water due to pileus cloud formation near the tropopause

International audienceThin stratiform clouds called pileus can form in the earth's atmosphere when humid air is lifted above rising convection. In the lower troposphere pileus lifetimes are short, so they have been considered little more than an attractive curiosity. This paper describes pileus cloud forming near the tropopause at low-latitudes, and discusses how they may be associated with a redistribution of water vapor and ice at cold temperatures

Structure-Function Relationships Affecting the Sensing Mechanism of Monolayer-Protected Cluster Doped Xerogel Amperometric Glucose Biosensors

A systematic study of the structure–function relationships critical to understanding the sensing mechanism of 1st generation amperometric glucose biosensors with an embedded nanoparticle (NP) network is presented. Xerogel-based films featuring embedded glucose oxidase enzyme and doped with alkanethiolate-protected gold NPs, known as monolayer protected clusters (MPCs), exhibit significantly enhanced performance compared to analogous systems without NPs including higher sensitivity, faster response time, and extended linear/dynamic ranges. The proposed mechanism involves diffusion of the glucose to glucose oxidase within the xerogel, enzymatic reaction production of H2O2 with subsequent diffusion to the embedded network of MPCs where it is oxidized, an event immediately reported via fast electron transfer (ET) through the MPC system to the working electrode. Various aspects of the film construct and strategy are systematically probed using amperometry, voltammetry, and solid-state electronic conductivity measurements, including the effects of MPC peripheral chain length, MPC functionalization via place-exchange reaction, MPC core size, and the MPC density or concentration within the xerogel composite films. The collective results of these experiments support the proposed mechanism and identify interparticle spacing and the electronic communication through the MPC network is the most significant factor in the sensing scheme with the diffusional aspects of the mechanism that may be affected by film/MPC hydrophobicity and functionality (i.e., glucose and H2O2 diffusion) shown to be less substantial contributors to the overall enhanced performance. Understanding the structure–function relationships of effective sensing schemes allows for the employment of the strategy for future biosensor design toward clinically relevant targets