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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

System Identification of Flexible Aircraft: Lessons Learned from the X-56A Phase 1 Flight Tests

The X-56A Multi-Utility Technology Testbed (MUTT) is a subscale airplane that was de-signed as an experimental flight research platform for improving aeroelastic modeling and control technologies. The Phase 1 flight tests, conducted from 2017 to 2019 at the NASA Arm-strong Flight Research Center (AFRC), included 39 flights and approximately 1000 research maneuvers, some of which demonstrated stable closed-loop flight beyond the open-loop flutter speed. This paper summarizes the system identification effort to extract nondimensional stability and control derivatives from the flight test data for constructing aeroelastic models of the flight dynamics. Topics discussed include instrumentation, experiment design, model postulation and reduction, parameter estimation, and others. Throughout the paper, unique challenges for the identification of flexible aircraft, practical aspects of the analysis, and lessons learned are presented.

Jared A Grauer↗
NASA NTRS1996

Evaluation of Star Identification Techniques

A number of different strategies are used or have been suggested for identifying star fields atitude determination in space. We offer a general classification of the existing techniques and select three representative algorithms for more comprehensive evaluation. In addition, we describe a software simulation environment we developed for the design, paramter determination, and evaluation of star identification algorithms. The identification rates and performance on the three algorithms are presented over a variety of noise conditions using two different sized onboard catalogs.

star↗
NASA NTRS2019

Identification of Absorbing Aerosol Types at a Site in the Northern Edge of Indo-Gangetic Plain and a Polluted Valley in the Foothills of the Central Himalayas

Identification of atmospheric aerosol types and characterization of absorbing aerosols, based on AErosol RObotic NETwork (AERONET) data collected during 2013–2014 over two sites in Nepal: Lumbini in the northernmost part of central Indo-Gangetic Plain (IGP) and Kathmandu Valley in foothills of the central Himalayas, have been conducted in the present study. The relationship between four aerosol parameters; Extinction Angstrom Exponent (EAE), Absorption Angstrom Exponent (AAE), Single Scattering Albedo (SSA) and Real Refractive Index (RRI) was analyzed to study the aerosol types. This resulted in the identification of two types of aerosols concerning their origin: biomass burning and urban/industrial mix. Furthermore, to understand the absorbing aerosol types, the relationship between aerosol size parameters; Fine Mode Fraction (FMF) and Angstrom Exponent (AE), and aerosol absorption characteristics; SSA and AAE were investigated. In regards to the absorbing aerosol types, ‘Mostly BC’ was the dominant absorbing aerosol, over both sites, with comparatively negligible contribution from other absorbing aerosol types such as dust. The aerosol subtypes obtained from satellite-borne CALIPSO instrument supported the results derived from the AERONET data. The CALIPSO images also indicated that the aerosols over the foothills of the Himalayas could extend to the height of >5 km above the ground, which could be transported towards the Himalayan and Tibetan Plateau (HTP) region with sensitive ecosystems. The multi-sites based study of long-term records is required to elucidate the nature and trends of aerosols in the HTP region and any perturbation to the atmospheric environment and other environments in this region.

Aerosol types↗
NASA NTRSDate not supplied

Frequency Domain Quasi-Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

Background and Motivation - Low Order Equivalent System (LOES) - From handling qualities analysis - Traditionally simplifying complex control law and plant - More easily understood form - Extend LOES to a complex model due to aeroelasticity - Maximum likelihood (Filter Error) System Identification - Z = H_loes (U + W) + V - There are a lot of parameters - System identification usually simplifies by assuming a value - Output Error and Equation Error - Results in biased estimates of the parameters - We are proposing a new method to solving this problem

Jeffrey Ouellette↗
NASA NTRSDate not supplied

Capability Gaps Assessment and Identification of Critical Technology Elements for Mars Transit Habitat

The Habitation Systems Development Office (HP40) at NASA Marshall Space Flight Center supports systems engineering, integration, and project management for next generation space habitats. For in space operations and eventual transport of humans to Mars, NASA will rely on a Mars Transit Habitat (TH). The TH will be designed for an up to 1,200-day Mars mission and will carry all food and supplies needed to support four crew for this duration. In the current concept of operations, Mars TH transfers to near rectilinear halo orbit (NRHO) following launch and docks at Gateway as a visiting vehicle. While there, the TH will complete system shakedown testing and a series of analog missions which will grow from 3 to 6+ months in duration TH also augments Gateway’s habitation capabilities beyond 60-days. Proposed Gateway-TH missions will far exceed the longest duration cislunar human missions to date. These shakedown missions will also be the first operational readiness tests of Mars TH’s long-duration deep space systems, and of the split crew (two crew on the surface, two crew in space) operations that are vital to the approach for the first human Mars mission. Once shakedown missions are complete, Mars TH departs Gateway to aggregate with the Mars propulsion system in NRHO before onboarding the crew and final supplies in Earth orbit via a co-manifested Orion-logistics module. Orion and the LM return to Earth prior to the now aggregated Deep Space Transport vehicle’s journey to Mars. Development of the Mars TH requires significant technology development and maturation. Each year the agency performs a capability gaps assessment, where gaps developed by subject matter experts (SMEs) in various engineering/science disciplines are linked to architectural elements in formulation and prioritized. A gap captures the difference between the current state-of-the-art and the maturity of the capability that is needed to enable or enhance a mission as it is currently envisioned in the government reference architecture. HP40 conducted a gap analysis for Mars TH which will be summarized in this poster. Gaps classified as enabling (which means the mission cannot achieve success without gap closure) were subsequently used to identify critical technology elements (CTEs) for Mars TH. This identification of CTEs was also informed by an examination of the product breakdown structure for Mars TH and focused conversations with SMEs in specific technology areas. CTEs identified for Mars TH to date include the following (note this is not a comprehensive list – CTEs listed represent those in MSFC’s capability areas): inflatable softgoods for habitation; enhanced CO2 recovery; life support systems with greater levels of reliability and maintainability; autonomous guidance, navigation, command and control; and radiators for the Mars TH application. The habitation systems development team is currently delving deeper into each CTE to assess technology approaches being pursued, their maturity, and the degree of difficulty in maturation to meet projected Mars TH timelines. This poster will summarize work to date on the identification of enabling capability gaps linked to Mars TH and provide insight into the associated CTEs and technology maturation efforts.

technology development↗
NASA NTRS2022

A Reinforcement Learning Hyper-Heuristic in Multi-Objective Optimization with Application to Structural Damage Identification

Multi-objective optimization allows satisfying multiple decision criteria concurrently, and generally yields multiple solutions. It has the potential to be applied to structural damage identification applications which are oftentimes under-determined. How to achieve high-quality solutions in terms of accuracy, diversity, and completeness is a challenging research subject. The solution techniques and parametric selections are believed to be problem specific. In this research, we formulate a reinforcement learning hyper-heuristic scheme to work coherently with the single-point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed). The four low-level heuristics proposed can meet various optimization requirements adaptively and autonomously using the domination amount, crowding distance, and hypervolume calculations. The new approach exhibits improved and more robust performance than AMOSA, NSGA-II, and MOEA/D when applied to benchmark test cases. It is then applied to an active damage interrogation scheme for structural damage identification where solution diversity/completeness and accuracy are critically important. Results show that this approach can successfully include the true damage scenario in the solution set identified. The outcome of this research can potentially be extended to a variety of applications.

Pei Cao↗
NASA NTRSDate not supplied

In-flight System Identification of the Ingenuity Mars Helicopter

The 68th and 69th flights of NASA’s Ingenuity Mars Helicopter marked the first dedicated system identification flights of an aerial vehicle on another planet. Chirp signals were injected into the swashplate cyclic controls for both legs of the two out-and-back flights. Frequency responses were computed from the flight data, using both the Direct Method (DM) and the Joint-Input-Output (JIO) approach, for the identification of stability and control derivatives in forward flight conditions. The resulting identified state-space models were compared against existing flight dynamics simulation models, showing excellent correlation in the higher frequency range. External disturbances were seen to introduce a bias in the identified lower frequency responses, which was partially mitigated using the JIO method. These findings will inform future modeling and flight testing efforts of Mars rotorcraft.

Ingenuity↗
DOE OSTI2014

Device for detection and identification of carbon- and nitrogen-containing materials

A device for detection and identification of carbon- and nitrogen-containing materials is described. In particular, the device performs the detection and identification of carbon- and nitrogen-containing materials by photo-nuclear detection. The device may comprise a race-track microtron, a breaking target, and a water-filled Cherenkov radiation counter.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Morphological Analysis of Zirconium Nuclear Fuel Retaining Rods Braided with SiC: Quality Assurance and Defect Identification

In the after-Fukushima world, the stability of materials under extreme conditions is an important issue for the safety of nuclear reactors. Among the methods explored currently to improve zircaloys’ thermal stability in off-normal conditions, using a protective coat of the SiC filaments is considered because silicon carbide is well known for its remarkable chemical inertness at high temperatures. A typical SiC fiber contains ~50,000 individual filaments of 5 – 10 µm in diameter. In this paper, an effort was made to develop and apply mathematical morphology to the process of automatic defect identification in Zircaloy-4 rods braided with the protective layer of the silicon carbide filament. However, the issues of the braiding quality have to be addressed to ensure its full protective potential. We present the original mathematical morphology algorithms that allow solving this problem of quality assurance successfully. In nuclear industry, such algorithms are used for the first time, and could be easily generalized to the case of automated continuous monitoring for defect identification in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Dual-Remote Raman Technology for In-Situ Identification of Tank Waste

A new Raman spectroscopic system for in-situ identification of the composition of the solid nuclear tank waste is being developed by collaborative effort between Pacific Northwest National Laboratory (PNNL) and EIC, Inc. The recent advancements in the Raman technology allow probing the chemical composition of the tank waste without sample collection. In the newly tested configuration, the Raman probe is installed on the top of the tank riser and sends the incident laser beam to the bottom of the tank, 10 – 70 feet away. The returning light containing chemical information is collected by the Raman probe and transmits it via fiber optic cable to the spectrometer located outside the tank farm area. This dual remote technology significantly expands currently limited options for the safe rapid in-situ identification of the solid tank waste needed for the retrieval decisions. The developed Raman system was extensively tested for acceptability prior tank farm deployment. This testing included calibration of the system with the respect of the distance between the Raman probe and the sample, incident laser beam angle, and presence of the optical interferences. The Raman system was successfully deployed to C-111 tank at the US DOE Hanford site. As the result of this deployment, the composition of the hardpan at the bottom of C-111 tank was identified. Further development of the dual-remote Raman technology will provide a significant safety enhancement eliminating the potential of personnel radiation exposure associated with the grab sample collection and expands options of the rapid and cost-effective in-situ chemical analysis of the tank waste.

in-situ Hanford tank, Raman measurement↗
DOE OSTI2017

System for tamper identification

A system for tamper identification. A fastener has a tamper identification surface with a unique grain structure that is altered if the fastener is removed or otherwise exposed to sufficient torque. After a period of time such as e.g., shipment and/or storage of the sealed container, a determination of whether tampering has occurred can be undertaken by examining the grain structure to determine if it has changed since the fastener was used to seal the container or secure the device.

42 ENGINEERING↗
DOE OSTI2018

Load power device, system and method of load control and management employing load identification

A load power device includes a power input, at least one power output for at least one load, a plurality of sensors structured to sense voltage and current at the at least one power output, and a processor. The processor provides: (a) load identification based upon the sensed voltage and current, and (b) load control and management based upon the load identification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Detection Identification and Quantification of Keto-Hydroperoxides in Low-Temperature Oxidation.

Keto-hydroperoxides are reactive partially oxidized intermediates that play a central role in chain-branching reactions during the low-temperature oxidation of hydrocarbons. In this Perspective, we outline how these short lived species can be detected, identified, and quantified using integrated experimental and theoretical approaches. The procedures are based on direct molecular-beam sampling from reactive environments, followed by mass spectrometry with single-photon ionization, identification of fragmentation patterns, and theoretical calculations of ionization thresholds, fragment appearance energies, and photoionization cross sections. Using the oxidation of neo-pentane and tetrahydrofuran as examples, the individual steps of the experimental approaches are described in depth together with a detailed description of the theoretical efforts. For neo-pentane, the experimental data are consistent with the calculated ionization and fragment appearance energies of the keto-hydroperoxide, thus adding confidence to the analysis routines and the employed levels of theory. For tetrahydrofuran, multiple keto-hydroperoxide isomers are possible due to the presence of nonequivalent O 2 addition sites. Despite this additional complexity, the experimental data allow for the identification of two to four keto-hydroperoxides. Mole fraction profiles of the keto-hydroperoxides, which are quantified using calculated photoionization cross sections, are provided together with estimated uncertainties as function of the temperature of the reactive mixture and can serve as validation targets for chemically detailed mechanisms.

Hansen, Nils↗
DOE OSTI2015

Reflectance Spectroscopy for Sample Identification: Considerations for Quantitative Library Results at Infrared Wavelengths

Reflectance spectroscopy for sample identification has advanced greatly in terms automation by removing the plaguing necessity of sample preparation; automated instruments in the laboratory, in the field and at the process line have made “point and shoot” operation readily accessible, in some instances with standoff detection. For successful identification, however, it is imperative to have reference libraries that are well calibrated not only on the wavelength axis, but on the reflectance axis as well. Ideally, with proper calibration such libraries would be portable to other systems.

calibration↗
DOE OSTI2021

Identification of Life Models for Li-Ion Batteries Using Penalized Regression and Bilevel Optimization

Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2013

Dual-Remote Raman Technology for In-Situ Identification of Tank Waste - 13549

A new Raman spectroscopic system for in-situ identification of the composition of solid nuclear tank waste is being developed by collaborative effort between Pacific Northwest National Laboratory (PNNL) and EIC Laboratories, Inc. The recent advancements in Raman technology allow probing the chemical composition of the tank waste without sample collection. In the newly tested configuration, the Raman probe is installed on the top of the tank riser and sends the incident laser beam to the bottom of the tank, 10 - 70 feet away. The returning light containing chemical information is collected by the Raman probe and is transmitted via fiber optic cable to the spectrometer located outside the tank farm area. This dual remote technology significantly expands currently limited options for the safe rapid in-situ identification of the solid tank waste needed for the retrieval decisions. The developed Raman system was extensively tested for acceptability prior to tank farm deployment. This testing included calibration of the system with respect of the distance between the Raman probe and the sample, incident laser beam angle, and presence of the optical interferences. The Raman system was successfully deployed on Tank C-111 at the US DOE Hanford site. As the result of this deployment, the composition of the hardpan at the bottom of C-111 tank was identified. Further development of the dual-remote Raman technology will provide a significant safety enhancement eliminating the potential of personnel radiation exposure associated with the grab sample collection and expands options of the rapid and cost-effective in-situ chemical analysis of the tank waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗