Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Identification”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Detection, Identification, Location, and Remote Sensing Using SAW RFID Sensor Tags

The Electromagnetic Systems Branch (EV4) of the Avionic Systems Division at NASA Johnson Space Center in Houston, TX is studying the utility of surface acoustic wave (SAW) radiofrequency identification (RFID) tags for multiple wireless applications including detection, identification, tracking, and remote sensing of objects on the lunar surface, monitoring of environmental test facilities, structural shape and health monitoring, and nondestructive test and evaluation of assets. For all of these applications, it is anticipated that the system utilized to interrogate the SAW RFID tags may need to operate at fairly long range and in the presence of considerable multipath and multiple-access interference. Towards that end, EV4 is developing a prototype SAW RFID wireless interrogation system for use in such environments called the Passive Adaptive RFID Sensor Equipment (PARSED) system. The system utilizes a digitally beam-formed planar receiving antenna array to extend range and provide direction-of-arrival information coupled with an approximate maximum-likelihood signal processing algorithm to provide near-optimal estimation of both range and temperature. The system is capable of forming a large number of beams within the field of view and resolving the information from several tags within each beam. The combination of both spatial and waveform discrimination provides the capability to track and monitor telemetry from a large number of objects appearing simultaneously within the field of view of the receiving array. In this paper, we will consider the application of the PARSEQ system to the problem of simultaneous detection, identification, localization, and temperature estimation for multiple objects. We will summarize the overall design of the PARSEQ system and present a detailed description of the design and performance of the signal detection and estimation algorithms incorporated in the system. The system is currently configured only to measure temperature (jointly with range and tag ID), but future versions will be revised to measure parameters other than temperature as SAW tags capable of interfacing with external sensors become available. It is anticipated that the estimation of arbitrary parameters measured using SAW-based sensors will be based on techniques very similar to the joint range and temperature estimation techniques described in this paper.

Barton, Richard J.↗
NASA NTRS2011

Ares I-X In-Flight Modal Identification

Operational modal analysis is a procedure that allows the extraction of modal parameters of a structure in its operating environment. It is based on the idealized premise that input to the structure is white noise. In some cases, when free decay responses are corrupted by unmeasured random disturbances, the response data can be processed into cross-correlation functions that approximate free decay responses. Modal parameters can be computed from these functions by time domain identification methods such as the Eigenvalue Realization Algorithm (ERA). The extracted modal parameters have the same characteristics as impulse response functions of the original system. Operational modal analysis is performed on Ares I-X in-flight data. Since the dynamic system is not stationary due to propellant mass loss, modal identification is only possible by analyzing the system as a series of linearized models over short periods of time via a sliding time-window of short time intervals. A time-domain zooming technique was also employed to enhance the modal parameter extraction. Results of this study demonstrate that free-decay time domain modal identification methods can be successfully employed for in-flight launch vehicle modal extraction.

Bartkowicz, Theodore J.↗
NASA NTRS2005

Hidden Identification on Parts: Magnetic Machine-Readable Matrix Symbols

All NASA parts have identification, usually expressed in terms of part number, serial number, and the like. In most cases, this identification is permanently marked directly on the part for tracking throughout its life cycle. NASA has developed a method for reading the matrix symbol through up to 15 mils (25 m) of paint (5 or 6 layers). This method of part identification involves coating selected patches on the objects with magnetic materials in matrix symbol patterns and reading the patterns by use of magneto-optical imaging equipment.

Schramm, Harry F.↗
NASA NTRS2004

Bayesian-Augmented Identification of Stars in a Narrow View

An algorithm for the identification of stars from a charge-coupled-device (CCD) image of a star field has been extended for use with narrower field-of-view images. Previously, the algorithm had been shown to be effective at a field of view of 8 degrees. This work augments the earlier algorithm using Bayesian decision theory. The new algorithm is shown to be capable of effective star identification down to a field of view of 2 degrees. The algorithm was developed for use in estimating the attitude of a spacecraft and could be used on Earth to help in the identification of stars and other celestial objects for astronomical observations.

Clouse, Daniel↗
NASA NTRS2013

Combined Cycle Engine Large-Scale Inlet for Mode Transition Experiments: System Identification Rack Hardware Design

The System Identification (SysID) Rack is a real-time hardware-in-the-loop data acquisition (DAQ) and control instrument rack that was designed and built to support inlet testing in the NASA Glenn Research Center 10- by 10-Foot Supersonic Wind Tunnel. This instrument rack is used to support experiments on the Combined-Cycle Engine Large-Scale Inlet for Mode Transition Experiment (CCE‒ LIMX). The CCE‒LIMX is a testbed for an integrated dual flow-path inlet configuration with the two flow paths in an over-and-under arrangement such that the high-speed flow path is located below the lowspeed flow path. The CCE‒LIMX includes multiple actuators that are designed to redirect airflow from one flow path to the other; this action is referred to as "inlet mode transition." Multiple phases of experiments have been planned to support research that investigates inlet mode transition: inlet characterization (Phase-1) and system identification (Phase-2). The SysID Rack hardware design met the following requirements to support Phase-1 and Phase-2 experiments: safely and effectively move multiple actuators individually or synchronously; sample and save effector control and position sensor feedback signals; automate control of actuator positioning based on a mode transition schedule; sample and save pressure sensor signals; and perform DAQ and control processes operating at 2.5 KHz. This document describes the hardware components used to build the SysID Rack including their function, specifications, and system interface. Furthermore, provided in this document are a SysID Rack effectors signal list (signal flow); system identification experiment setup; illustrations indicating a typical SysID Rack experiment; and a SysID Rack performance overview for Phase-1 and Phase-2 experiments. The SysID Rack described in this document was a useful tool to meet the project objectives.

Thomas, Randy↗
NASA NTRS2014

Rapid Electrochemical Detection and Identification of Microbiological and Chemical Contaminants for Manned Spaceflight Project

Microbial control in the spacecraft environment is a daunting task, especially in the presence of human crew members. Currently, assessing the potential crew health risk associated with a microbial contamination event requires return of representative environmental samples that are analyzed in a ground-based laboratory. It is therefore not currently possible to quickly identify microbes during spaceflight. This project addresses the unmet need for spaceflight-compatible microbial identification technology. The electrochemical detection and identification platform is expected to provide a sensitive, specific, and rapid sample-to-answer capability for in-flight microbial monitoring that can distinguish between related microorganisms (pathogens and non-pathogens) as well as chemical contaminants. This will dramatically enhance our ability to monitor the spacecraft environment and the health risk to the crew. Further, the project is expected to eliminate the need for sample return while significantly reducing crew time required for detection of multiple targets. Initial work will focus on the optimization of bacterial detection and identification. The platform is designed to release nucleic acids (DNA and RNA) from microorganisms without the use of harmful chemicals. Bacterial DNA or RNA is captured by bacteria-specific probe molecules that are bound to a microelectrode, and that capture event can generate a small change in the electrical current (Lam, et al. 2012. Anal. Chem. 84(1): 21-5.). This current is measured, and a determination is made whether a given microbe is present in the sample analyzed. Chemical detection can be accomplished by directly applying a sample to the microelectrode and measuring the resulting current change. This rapid microbial and chemical detection device is designed to be a low-cost, low-power platform anticipated to be operated independently of an external power source, characteristics optimal for manned spaceflight and areas where power and computing resources are scarce.

Pierson, Duane↗
NASA NTRS2014

Vibro-Acoustic Modulation Based Damage Identification in a Composite Skin-Stiffener Structure

The vibro-acoustic modulation method is applied to a composite skin-stiffener structure to investigate the possibilities to utilize this method for damage identification in terms of detection, localisation and damage quantification. The research comprises a theoretical part and an experimental part. An impact load is applied to the skin-stiffener structure, resulting in a delamination underneath the stiffener. The structure is interrogated with a low frequency pump excitation and a high frequency carrier excitation. The analysis of the response in a frequency band around the carrier frequency is employed to assess the damage identification capabilities and to gain a better understanding of the modulations occurring and the underlying physical phenomena. Though vibro-acoustic is shown to be a sensitive method for damage identification, the complexity of the damage, combined with a high modal density, complicate the understanding of the relation between the physical phenomena and the modulations occurring. more research is recommended to reveal the physics behind the observations.

Ooijevaar, T. H.↗
NASA NTRS1994

Linear Algorithms for Worst Case Identification in H infinity with Applications to Flexible Structures

This paper is concerned with linear algorithms for identification in which have been studied in [9]. It is shown that the two different linear algorithms in [9] can be unified into a single one which can be further extended to nonuniformly spaced frequency response samples with exponential convergence for the noise free case. Improved upper bounds for the corresponding identification errors are derived. Applications to the identification of lightly damped systems such as flexible structures are also considered.

linear↗
NASA NTRS2023

Molecular Identification of Microbial Contaminants

Microorganisms can have significant impacts on the success of NASA’s missions, including the integrity of materials, the reliability of scientific results, and maintenance of crew health. Robust cleaning and sterilization protocols are currently in place in NASA facilities, but agency experts agree that microbial contamination is unavoidable and its impact on NASA’s missions and science must be minimized. Therefore, it is critical to understand: 1) what specific microorganisms are present, 2) how they may impact scientific objectives, and 3) how to select appropriate mitigation strategies. The Marshall Space Flight Center (MSFC) Planetary Protection (PP) microbiology lab historically relied solely upon enumeration of culturable microbial contamination associated with spacecraft materials or cleanrooms. However, this process is time consuming, many microbes cannot be cultured, and very few can be identified with any fidelity using NASA standard microbiological methods. The work described in this white paper includes the establishment of molecular identification capabilities at MSFC, including DNA isolation, amplification, purification, and Sanger sequencing. This capability will not only improve planetary protection efforts at MSFC (i.e. by identifying contaminating microorganisms in cleanrooms or on spacecraft) but also offers a service center-wide for the identification of contaminants that arise in other projects, processing locations, or during set up and roll out of spacecraft. This work also lays the foundation for higher throughput efforts to identify large populations of microbes across the lifetime of a project and serves as the starting point for future work into whole genome sequencing, non-culture based methods, or additional characterization studies. Ultimately, accurate identification informs appropriate mitigation strategies, increasing the chances of success for NASA’s missions and objectives.

C. D. Cassilly↗
DOE OSTI2018

Clutter identification based on kernel density estimation and sparse recovery

A cognitive radar framework is being developed to dynamically detect changes in the clutter characteristics, and to adapt to these changes by identifying the new clutter distribution. In our previous work, we have presented a sparse-recovery based clutter identification technique. In this technique, each column of the dictionary represents a specific distribution. More specifically, calibration radar clutter data corresponding to a specific distribution is transformed into a distribution through kernel density estimation. When the new batch of radar data arrives, the new data is transformed to a distribution through the same kernel density estimation method and its distribution characteristics is identified through sparse-recovery. In this paper, we extend our previous work to consider different kernels and kernel parameters for sparse-recovery-based clutter identification and the numerical results are presented as well. The impact of different kernels and kernel parameters are analyzed by comparing the identification accuracy of each scenario.

Wang, Haokun↗
DOE OSTI2020

Phase identification using co‐association matrix ensemble clustering

Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co‐association matrix‐based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A constraint-reduced MPC algorithm for convex quadratic programming, with a modified active set identification scheme

A constraint-reduced Mehrotra-predictor-corrector algorithm for convex quadratic programming is proposed. (At each iteration, such algorithms use only a subset of the inequality constraints in constructing the search direction, resulting in CPU savings.) The proposed algorithm makes use of a regularization scheme to cater to cases where the reduced constraint matrix is rank deficient. Global and local convergence properties are established under arbitrary working-set selection rules subject to satisfaction of a general condition. A modified active-set identification scheme that fulfills this condition is introduced. Numerical tests show great promise for the proposed algorithm, in particular for its active-set identification scheme. While the focus of the present paper is on dense systems, application of the main ideas to large sparse systems is briefly discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Chapter 4 - Recent Advances in Identification of Differential Equations from Noisy Data: IDENT Review

Differential equations and numerical methods are extensively used to model various real-world phenomena in science and engineering. With modern developments, we aim to find the underlying differential equation from a single observation of time-dependent data. If we assume that the differential equation is a linear combination of various linear and nonlinear differential terms, then the identification problem can be formulated as solving a linear system. The goal then reduces to finding the optimal coefficient vector that best represents the time derivative of the given data. We review some recent works on the identification of differential equations. We find some common themes for the improved accuracy: (i) The formulation of linear system with proper denoising is important, (ii) how to utilize sparsity and model selection to find the correct coefficient support needs careful attention, and (iii) there are ways to improve the coefficient recovery. We present an overview and analysis of recent developments on the topic.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Phenomena Identification and Ranking Table (PIRT) study for metallic structural materials for advanced High-Temperature reactor

The Fluoride High-Temperature Reactor (FHR) technology promises many benefits including passive safety, proliferation-resistant waste forms, and improved economics. However, selection of reliable structural materials and identification of the possible degradation mechanisms for these is important for the licensure and the safe operation of FHRs. In order to address this task, the Georgia Tech led Integrated Research Project (IRP) hosted a Phenomena Identification and Ranking Table (PIRT) panel of experts to address degradation mechanisms and other materials related issues of importance to the FHRs. Futhermore, materials, ones that come in contact with FLiBe or FLiNaK molten salts or other related environments like high temperature steam etc., were considered in this PIRT. Focus of this PIRT was the metallic alloys, especially the ones that are permitted for the construction of elevated temperature Class A components by the ASME code. Degradation mechanisms considered in this PIRT included chemical degradation, mechanical degradation, radiation degradation, and synergistic effect of these mechanisms that may negatively impact operations or cause some safety concerns for the major structural components of FHRs. Main components which were considered included vessel and primary piping, primary heat exchangers, steam generator vessel, steam generator tubes, intermediate loop piping, valves and pumps. Welds in all structural components were identified as an important class of material, which varies in composition and properties, and needs more attention. Importance of impurity control in molten fluorides considered for FHR was highlighted throughout PIRT panel discussions. As a result, this paper gives a summary of important results from the PIRT panel discussions and report.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance

An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem of simultaneously identifying an aggregate thermal dynamic model and unknown disturbances from input–output data of multi-zone buildings. The unknown disturbance is a key challenge since it is not measurable but non-negligible. In this paper, we first present a principled method to aggregate a multi-zone building model into a single zone model, and show the aggregation is not as trivial as it has been assumed in the prior art. We then provide a method to identify the parameters of the model and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data collected from a multi-zone building testbed in Oak Ridge National Laboratory. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain, even in the case of a specially designed test in which the disturbances affecting each zone are known (as the case of our experimental testbed). This insight is used to provide a heuristic that can be used to assess when the identification results are likely to have high or low accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cause identification of electromagnetic transient events using spatiotemporal feature learning

This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with softmax function for the classification. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine autoencoder, and tapered multi-layer perceptron neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the Electromagnetic Transients Program (EMTP) simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation of the Western System Coordinating Council (WSCC) 9-bus system.

42 ENGINEERING↗
DOE OSTI2020

Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

deep belief network↗
DOE OSTI2017

Contaminant source identification using semi-supervised machine learning

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

54 ENVIRONMENTAL SCIENCES↗