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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.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Sowers, T. Shane↗
NASA NTRS2008

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance, there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight, and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Sowers, T. Shane↗
NASA NTRS2010

An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related technologies that assist operators in managing the health of their gas turbine engine assets. Trend monitoring is the process of monitoring the gradual performance change that an aircraft engine will naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of detecting and isolating the occurrence of any faults impacting engine flow-path performance. Today, performance trend monitoring and gas path fault diagnostic functions are performed by a combination of on-board and off-board strategies. On-board engine control computers contain logic that monitors for anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine trend monitoring and fault diagnostics based on data collected from each engine each flight. Continuing advances in avionics are enabling the migration of portions of the ground-based functionality on-board, giving rise to more sophisticated on-board engine health management capabilities. This paper reviews the conventional engine performance trend monitoring and gas path fault diagnostic architecture commonly applied today, and presents a proposed enhanced on-board architecture for future applications. The enhanced architecture gains real-time access to an expanded quantity of engine parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the enhanced architecture include the real-time continuous monitoring of engine health, the early diagnosis of fault conditions, and the estimation of unmeasured engine performance parameters. A future vision to advance the enhanced architecture is also presented and discussed

Simon, Donald L.↗
NASA NTRS2010

Application of Diagnostic Analysis Tools to the Ares I Thrust Vector Control System

The NASA Ares I Crew Launch Vehicle is being designed to support missions to the International Space Station (ISS), to the Moon, and beyond. The Ares I is undergoing design and development utilizing commercial-off-the-shelf tools and hardware when applicable, along with cutting edge launch technologies and state-of-the-art design and development. In support of the vehicle s design and development, the Ares Functional Fault Analysis group was tasked to develop an Ares Vehicle Diagnostic Model (AVDM) and to demonstrate the capability of that model to support failure-related analyses and design integration. One important component of the AVDM is the Upper Stage (US) Thrust Vector Control (TVC) diagnostic model-a representation of the failure space of the US TVC subsystem. This paper first presents an overview of the AVDM, its development approach, and the software used to implement the model and conduct diagnostic analysis. It then uses the US TVC diagnostic model to illustrate details of the development, implementation, analysis, and verification processes. Finally, the paper describes how the AVDM model can impact both design and ground operations, and how some of these impacts are being realized during discussions of US TVC diagnostic analyses with US TVC designers.

Maul, William A.↗
NASA NTRS2009

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.

Kurtoglu, Tolga↗
NASA NTRS2013

Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by the aircraft EHM community. It provides a standard benchmark problem enabling users to develop, evaluate and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.

Simon, Donald L.↗
NASA NTRS2018

A Laser-Induced Fluorescence Diagnostic for HERMeS and High-Power Electric Propulsion

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗
NASA NTRS2018

A Laser-Induced Fluorescence Diagnostic for HERMeS and High-Power Electric Propulsion

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗
NASA NTRSDate not supplied

SPIKE-Dx : A Low-Power High-Throughput Fault Diagnostics Tool using Spiking Neural Networks for Constrained Systems

Diagnostic systems are important for many aerospace systems, which are severely limited in available power, like cubesats or UAVs. Therefore, traditional diagnostics systems cannot be used due to their substantial footprint and constraints. In this paper, we present our very low power diagnostic tool SPIKE-DX to monitor critical systems with constrained computational and energy resources. This is made possible through spiking neural networks (SNNs), which are executable within optimized simulation environments and further implemented on on cutting-edge neuromorphic hardware. Based upon FMEA (Failure Mode and Effect Analysis) framework, Diagnostic Bayesian Networks (DBNs) can be constructed that provide powerful means for diagnostic reasoning. In this paper, we describe such DBNs and a method to automatically translate the DBN into highly structured networks of spiking neurons for execution in SPIKE-DX.

Spiking Neural Networks↗
DOE OSTI2020

Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules

Battery packs for electric and stationary applications experience varied operating conditions, including abuse—e.g., fast charging, overcharging, thermal, vibration, shock, etc.—throughout their lifetimes. Innovative diagnostic tools and algorithms that go beyond single cells and deal with modules and packs are essential for early detection of off-normal issues. High-resolution tools with known detection limits are key to developing appropriate mitigation strategies. With the advent of rapid impedance spectroscopy that can generate a broadband impedance spectrum in ~10 sec, the case for impedance-based diagnostics that can be readily aligned with other methods, such as incremental capacity or dQ.dV -1 , has become promising. This study used the aforementioned diagnostic methods to identify realistic in-vehicle battery abnormalities (e.g., localized self-discharge and non-uniform aging), in series (up to 10S) and parallel (4P) strings, using 16 Ah graphite/NMC cells. The impedance-based diagnostic is found to be sensitive to the string size and state. Depending on the type of abnormality, detection frequency varied. The dQ.dV -1 method showed the potential to detect long-term aging-related heterogeneity in modules. In general, both the impedance and dQ.dV -1 methods were able to detect series strings’ abnormalities, but struggled to find those issues within parallel modules.

25 ENERGY STORAGE↗
DOE OSTI2021

Compact, portable, laser induced fluorescence diagnostic for laboratory plasma sources

As diagnostic groups are increasingly called upon to participate in experimental campaigns at remote facilities, there is a need to develop portable versions of plasma diagnostic systems. One such diagnostic is laser induced fluorescence (LIF). Here, we describe a portable LIF apparatus that eliminates the need for an optical table, beam splitters, and an optical chopper. All of the light exiting the laser system is coupled through optical fibers to the experiment and housekeeping diagnostics. Here, the collected light is coupled through an optical fiber as well. A key feature is modulation of the tapered amplifier current instead of physical modulation of the laser output. Using this portable LIF system, measurements of ion temperature, ion flow, and relative metastable ion density are reported for two different remote experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Coherent microwave scattering for diagnostics of small plasma objects: A review

Measurements of parameters of small-size plasmas are very challenging because many traditional diagnostic approaches cannot be used. Constructive coherent microwave scattering (CMS) offers a convenient diagnostic solution for such small plasmas. This work reviews the development and applications of constructive coherent microwave scattering by the Electric Propulsion and Plasma Laboratory at Purdue University. It presents fundamentals of CMS with an emphasis on Thomson, collisional, and Rayleigh scattering in short, thin, unmagnetized plasma media. Additionally, we review examples of CMS application for diagnostics of temporally resolved plasma dynamics and electron decay, photoionization rates, electron momentum-transfer collision frequencies, and number densities of selective species in gaseous mixtures. These applications are relevant for various research fields including strong field and femtosecond filamentation physics, plasma-assisted ignition and combustion, and combustion and spacecraft electric propulsion diagnostics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Electrical design of the flexible imaging diffraction diagnostic for laser experiments (FIDDLE) at the National Ignition Facility (NIF)—Requirements, design, and performance

The Flexible Imaging Diffraction Diagnostic for Laser Experiments (FIDDLE) is a newly developed diagnostic for imaging time resolved diffraction in experiments at the National Ignition Facility (NIF). It builds on the successes of its predecessor, the Gated Diffraction Development Diagnostic (G3D). The FIDDLE was designed to support eight Daedalus version 2 sensors (six more hCMOS sensors than any other hCMOS-based diagnostic in NIF to date) and an integrated streak camera. We will review the electrical requirements, design, and performance of the electrical subsystems that were created to support this large number of cameras in the FIDDLE. The analysis of the data that the FIDDLE is intended to collect relies heavily on the accurate and well-understood timing of each sensor. We report camera-to-camera timing jitter of less than 100 ps rms and sensor integration times of 2.2 ns FWHM in 2-2 timing mode. Additionally, diffraction experiments on the NIF produce electric fields (EMI) on the order of 1 kV/m, which have been observed to negatively impact the performance of some electrical components of the FIDDLE. We report on the results of testing hCMOS camera electronics in a similar EMI environment generated in an offline lab. In addition, we also summarize the use of a novel approach to using a vector network analyzer as an EMI leak detector to understand and reduce the negative impacts of EMI on the FIDDLE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Progress in ITER ECE diagnostic design and integration

The ITER electron cyclotron emission (ECE) diagnostic system has primary roles in providing measurements of the core electron temperature profile and the electron temperature fluctuation associated with the neoclassical tearing modes. The ITER ECE system includes a radial and oblique line-of-sight. Four 43-meter long low-loss transmission lines (TLs) are designed to transmit millimeter wave power in the frequency range of 70–1000 GHz in both X- and O-mode polarization from the port plug to the ECE instrumentation room in the diagnostic building. The measurement instrumentation includes two Fourier transform spectrometer (FTS) systems and two radiometer systems. The Indian Domestic Agency (IN-DA) and United States Domestic Agency share the responsibility. The IN-DA scope excluding instrumentation and control has passed its preliminary design review and is progressing towards the final design review (FDR). In parallel, the diagnostic integration in different areas is ongoing. Furthermore, several captive components for the TLs have passed FDR and will be manufactured for installation in the tokamak building soon. A peer review meeting has been held on the prototype hot calibration source, and its integration and new thermal analysis in the diagnostic shield module are continuing. A prototype TL is being tested. A prototype polarizing Martin-Puplett type FTS, operating in the frequency range 70–1000 GHz, features an in-vacuo fast scanning mechanism and a cryo-cooled dual-channel THz detector system. Its performance has been assessed in detail against ITER requirements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The Physics behind Precipitation Onset Bias in CMIP6 Models: The Pseudo-Entrainment Diagnostic and Trade-Offs between Lapse Rate and Humidity

Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. Here we diagnose these biases in a cohort of nine CMIP6 models with sub-daily output, assessing conditional instability in profiles of equivalent potential temperature θ e and saturation equivalent potential temperature θ es in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θ es vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θ es measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θ es lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θ e /θ es structure (those using variants of the Tiedtke Scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Diagnostic Evaluation of the IS1081-Targeted Real-Time PCR for Detection of Mycobacterium bovis DNA in Bovine Milk Samples

The ability of Mycobacterium bovis (M. bovis) to survive in bovine milk has emerged as a serious public health concern. The first objective of this study was to evaluate the diagnostic utility of IS1081-targeted real-time PCR for the detection of M. bovis DNA in different fractions of bovine milk. In a model study, bovine milk samples were spiked with serially diluted M. bovis BCG to investigate the detection limit of M. bovis DNA in whole milk and milk fractions (cream, pellet, and pellet + cream combined) using IS1081 real-time PCR. The assay was then used to detect M. bovis DNA in whole milk and milk fractions from naturally infected animals. The results showed that the IS1081 real-time PCR was more sensitive when detecting M. bovis DNA in the cream layer alone and cream + pellet combined compared to whole milk or the pellet alone. While PCR-based diagnostic assays for the detection of M. bovis in milk samples provide a quicker diagnostic tool for bovine tuberculosis, safe processing, and handling of M. bovis-infected milk samples remain a challenge and pose a human health risk. PrimeStore Molecular Transport Medium (MTM) has been shown to rapidly inactivate infected specimens while preserving nucleic acid for subsequent Molecular analysis. Therefore, the secondary objective of this study was to evaluate the ability of MTM to inactivate M. bovis BCG in spiked milk samples as well as its ability to preserve BCG DNA for the PCR assay. The results showed that MTM can successfully inactivate BCG alone or in spiked milk samples while preserving DNA for the PCR assay. The CT values of M. bovis BCG alone and spiked milk samples aliquoted in MTM and without MTM were similar at various dilutions. Taken together, our results indicate that using DNA extracted from the milk cream fraction alone or combined milk cream and pellet improved the recovery rate of M. bovis DNA in bovine milk samples. MTM has the potential to provide a safe and rapid sample processing tool for M. bovis inactivation in milk samples and preserve DNA for molecular diagnostics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.

battery aging reconstruction↗
DOE OSTI2025

Harnessing Nanobodies for Precision Targeting of Proteoforms: Opportunities and Challenges in Therapeutics and Diagnostics

Proteoforms are biologically distinct yet structurally similar proteins that play key roles in driving disease progression but are rarely accounted for in the development of therapeutics and diagnostics. Nanobodies (Nbs) have emerged as a therapeutic and diagnostic “silver bullet” as they possess unique structural and functional attributes that offer advantages over traditional antibodies. One of the most profound advantages of Nbs is the heightened sensitivity and ability to distinguish subtle changes in the conformation of a given protein. Thus, Nbs have significant potential as therapeutic and diagnostic agents that can identify and distinguish specific pathological proteoforms that underlie a given disease. However, there remain significant challenges in obtaining sufficient quantities and purities of specific proteoform antigens that are required for engineering proteoform-specific Nbs. Recent advancements in chemical biology tools for precision proteoform synthesis have made this task feasible for the first time. In this perspective, we discuss the advantages and challenges associated with developing proteoform-specific Nbs and how success in this endeavor will significantly advance the fields of therapeutics and diagnostics.

Antigens↗