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Report discusses recent and expected future developments in continuing research on active suppression of vibrations in truss structure. Focuses on development of digital controller as part of vibration-suppressing feedback control system including noncollocated vibration sensors and actuators.
Prototype autonomous deicing system for airplane includes network of electronic and electromechanical modules at various locations in wings and connected to central data-processing unit. Small, integrated solid-state device, using long coils installed under leading edge, exciting small vibrations to detect ice and larger vibrations to knock ice off. In extension of concept, outputs of vibration sensors and other sensors used to detect rivet-line fractures, fatigue cracks, and other potentially dangerous defects.
System developed that monitors integrity of composite-material structural components of aircraft in service. Includes strain gauges and accelerometers installed permanently in components to monitor vibrations, microprocessor-based data-acquisition system to process outputs of these vibration sensors, and desktop computer to analyze acquired data. By automating significant part of inspection process, system reduces amount of time needed for inspection and cost of inspection equipment. Contributes to safety by giving timely warning of hidden flaws that necessitate early, detailed inspection of critical components to determine whether components should be replaced immediately.
Micro-electro-mechanical systems (MEMS) provide an emerging technology that has the potential for revolutionizing the way space systems are designed, assembled, and tested. The high launch costs of current space systems are a major determining factor in the amount of functionality that can be integrated in a typical space system. MEMS devices have the ability to increase the functionality of selected satellite subsystems while simultaneously decreasing spacecraft weight. The Air Force Phillips Laboratory (PL) is supporting the development of a variety of MEMS related technologies as one of several methods to reduce the weight of space systems and increase their performance. MEMS research is a natural extension of PL research objectives in micro-electronics and advanced packaging. Examples of applications that are under research include on-chip micro-coolers, micro-gyroscopes, vibration sensors, and three-dimensional packaging technologies to integrate electronics with MEMS devices. The first on-orbit space flight demonstration of these and other technologies is scheduled for next year.
NASA is focusing renewed attention on the topic of large, ultra-lightweight space structures, also known as 'gossamer' spacecraft. Nearly all of the details of the giant spacecraft are still to be worked out. But it's already clear that one of the most challenging aspects will be developing techniques to align and control these systems after they are deployed in space. A critical part of this process is creating new ground test methods to measure gossamer structures under stationary, deploying and vibrating conditions for validation of corresponding analytical predictions. In addressing this problem, I considered, first of all, the possibility of simply using conventional displacement or vibration sensor that could provide spatial measurements. Next, I turned my attention to photogrammetry, a method of determining the spatial coordinates of objects using photographs. The success of this research and development has convinced me that photogrammetry is the most suitable method to solve the gossamer measurement problem.
A general overview of NASA Dryden Flight Research Center's C-17 Aircraft is presented. The topics include: 1) 2006 Activities PHM Instrumentation Refurbishment; 2) Acoustic and Vibration Sensors; 3) Gas Path Sensors; 4) NASA Instrumentation System Racks; 5) NASA C-17 Simulator; 6) Current Activities; 7) Future Work; 8) Lawn Dart ; 9) Weight Tub; and 10) Parachute Test Vehicle.
When micrometeorite or debris impacts occur on a space habitat, crew members need to be quickly informed of the likely extent of damage, and be directed to the impact location for possible repairs. The goal of the Habitat Particle Impact Monitoring System (HIMS) is to develop a fully automated, end-to-end particle impact detection system for crewed space exploration modules, both in space and on the surfaces of Solar System bodies. The HIMS uses multiple thin film piezo-polymer vibration sensors to detect impacts on a surface, and computer processing of the acoustical signals to characterize the impacts. Development and demonstration of the HIMS is proceeding in concert with NASA's Habitat Demonstration Unit (HDU) Project. The HDU Project is designed to develop and test various technologies, configurations, and operational concepts for exploration habitats. This paper describes the HIMS development, initial testing, and HDU integration efforts. Initial tests of the system on the HDU were conducted at NASA?s 2010 Desert Research and Technologies Studies (Desert-RATS). Four sensor locations were assigned near the corners of a rectangular pattern. To study the influence of wall thickness, three sets of four sensors were installed at different layer depths: on the interior of the PEM wall, on the exterior of the same wall, and on the exterior of a layer of foam insulation applied to the exterior wall. Once the system was activated, particle impacts were periodically applied by firing a pneumatic pellet gun at the exterior wall section. Impact signals from the sensors were recognized by a data acquisition system when they occurred, and recorded on a computer for later analysis. Preliminary analysis of the results found that the HIMS system located the point of impact to within 8 cm, provided a measure of the impact energy / damage produced, and was insensitive to other acoustic events. Based on this success, a fully automated version of this system will be completed and demonstrated as part of a crew "Caution/Warning" system at the 2011 Desert-RATS, along with a crew response procedure.
The National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center completed a series of research flights to better understand the challenges of aircraft wake surfing using civilian airplanes and commercial avionics. Airlines and air cargo carriers have identified uncertainty about increased passenger/crew discomfort due to noise and vibrations as a potential obstacle to the widespread adoption of aircraft wake surfing. To measure the effects of wake surfing on passenger ride quality, NASA instrumented a business jet with cabin noise and vibration sensors. The airplane was then flown under control of an experimental autopilot at multiple locations within the wake of a similar airplane. This paper presents a summary of the measurements collected on those flights, an assessment of passenger discomfort correlated with wake surfing performance benefits, and qualitative evaluations collected from passengers aboard during the research flights.
In 2015 and 2016, the National Aeronautics and Space Administration Marshall Space Flight Center designed, fabricated, assembled and hot-fire tested an oxygen/RP-1 propellant multi-element oxidizer-rich staged-combustion test article. The main objective was to provide thrust chamber combustion stability data as part of the Combustion Stability Tool Development program, although demonstration of performance and compatibility of oxidizer-rich main injectors was also important. Funding was provided by the Air Force Space and Missile Systems Center. Five configurations of main injectors were designed and fabricated, using conventional gas-centered swirl coaxial injector element designs generally similar to those used in oxygen/kerosene oxidizer-rich staged combustion engines such as the Russian RD-180 or NK-33 engines. Variations of element features included element size, recess depth, fuel gap width, and the presence of the sleeve separating the swirling fuel flow from the axial oxidizer flow. Ablative combustion chambers were fabricated based on hardware previously used at the NASA MSFC for testing at similar size and pressure. Existing oxygen/RP-1 oxidizer-rich subscale preburner injectors and hot gas ducts from a previous NASA-funded program were modified for use to supply the oxidizer-rich combustion products to the oxidizer circuit of the main injector of the thrust chamber. Testing of the resulting integrated test article - which included the preburner, inter-connecting hot gas duct, main injector, and ablative combustion chamber - was conducted at Test Stand 116 at the East Test Area of the NASA MSFC. The test article was well instrumented with static and dynamic pressure, temperature, and vibration sensors. This paper presents and discusses all the hot-fire test results of the integrated test article thrust chamber. Eighteen successful hot-fire tests of the integrated rig were conducted. Testing was accomplished with all five of the injector element concepts. Main combustion chamber pressures ranged from 710 to 2350 psia, and main combustion chamber mixture ratios ranged from 2.47 to 2.87. A chamber barrier fuel film coolant of about 2% to 4% of the total fuel flow was used for most tests. Characteristic exhaust velocity efficiency excluding the influence of the fuel film cooling ranged from 91% to 98% of theoretical. All tests of the thrust chamber exhibited stable combustion, even down to 40% of nominal operating pressures. Compatibility of the injector face and combustion chamber walls was acceptable. This paper is a follow-on to publication of preliminary test data presented at the 2016 JANNAF Liquid Propulsion Subcommittee meeting.
As part of the ongoing effort to transition from preventive maintenance strategies to condition-based maintenance strategies in nuclear power plants, there is significant reliance on using machine learning techniques. To develop a robust machine learning model that can diagnose all the fault modes of a vertical motor-driven pump, data capturing the unique signature of each fault mode is required. In practice, it is difficult to collect or capture data that captures all the fault modes from a single plant site. So to address this situation, a computational model of a vertical motor-driven pump is developed using the multipurpose finite element software COMSOL Multiphysics. The developed model is used to generate simulated data under normal operation and is compared with the vibration data collected using vibration sensors. Once the simulation model is verified under normal operating condition, simulated data for the fault mode for which minimal or no evidence is available in historical plant process data is developed. This simulated data is used to develop fault signatures to achieve robust predictive models. This paper presents modeling details and verification of the model that can used to generate data for fault modes that are not available at a plant site for condition monitoring purpose.
As part of the ongoing effort to transition from preventive maintenance strategies to condition-based maintenance strategies in nuclear power plants, there is significant reliance on using machine learning techniques. To develop a robust machine learning model that can diagnose all the fault modes of a vertical motor-driven pump, data capturing the unique signature of each fault mode is required. In practice, it is difficult to collect or capture data that captures all the fault modes from a single plant site. So to address this situation, a computational model of a vertical motor-driven pump is developed using the multipurpose finite element software COMSOL Multiphysics. The developed model is used to generate simulated data under normal operation and is compared with the vibration data collected using vibration sensors. Once the simulation model is verified under normal operating condition, simulated data for the fault mode for which minimal or no evidence is available in historical plant process data is developed. This simulated data is used to develop fault signatures to achieve robust predictive models. This paper presents modeling details and verification of the model that can used to generate data for fault modes that are not available at a plant site for condition monitoring purpose.
Objectives: Introduce a sensor selection approach to assist test engineers with MIMO test design. Demonstrate the capability of the approach. Approach: Define a desired response from a field model. Supply the sensor selection technique to two lab models, with different boundary conditions than the “field” model. Compare the laboratory response to the field response using sensors selected from the approach.
Summary: Sensor selection approach for MIMO testing was defined and demonstrated on a model of a practical system. Laboratory response obtained using the approach matched the field response well. Information about how many control sensors, and where to locate sensors can be gleaned from the approach.
Limitations on inertial sensor testing produced by test platform vibrations
The use of a modal domain sensor in a vibration control experiment is described. An optical fiber is bonded along the length of a flexible beam. A control signal derived from the output of the modal domain sensor is used to suppress vibrations induced in the beam. A distributed effect model for the modal domain sensor is developed and combined with models of the beam and actuator dynamics to produce a system suitable for control design.
One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.
A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation table in the presence of various sources of laboratory noise is shown. The output of the neural network is called the degradable classification index. The curve was generated by a simultaneous comparison of means, and it shows a peak-to-peak sensitivity of about 100 nm. The following graph uses model generated data from a compressor blade to show that much higher sensitivities are possible when the environment can be controlled better. The peak-to-peak sensitivity here is about 20 nm. The training procedure was modified for the second graph, and the data were subjected to an intensity-dependent transformation called folding. All the measurements for this approach to calibration were optical. The peak-to-peak amplitudes of the vibration modes were measured using heterodyne interferometry, and the modes themselves were recorded using television (electronic) holography.
Sensors affixed to various such structures, where the sensors can withstand, remain affixed, and operate while undergoing both cryogenic temperatures and high vibrations. In particular, piezoelectric single crystal transducers are utilized, and these sensors are coupled to the structure via a low temperature, heat cured epoxy. This allows the transducers to monitor the structure while the engine is operating, even despite the harsh operating conditions. Aspects of the invention thus allow for real time monitoring and analysis of structures that operate in conditions that previously did not permit such analysis. A further aspect of the invention relates to use of piezoelectric single crystal transducers. In particular, use of such transducers allows the same elements to be used as both sensors and actuators.