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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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Microprocessor-based system analyzes amplitudes and rise times of neural waveforms. Displaying histograms of measured parameters helps researchers determine how many nerves contribute to signal and specify waveform characteristics of each. Results are improved noise rejection, full or partial separation of overlapping peaks, and isolation and identification of related peaks in different histograms. 2
Analytical method separately examines low-frequency and highfrequency behavior. Report discusses techniques for identifying rigid-body parameters, flexible-body parameters, and quasi-static disturbances in large structures. Techniques developed to aid in planning space station. Though effort directed towards space station dynamic model and environment, proposed methodology for systems identification is generic enough to allow application to arbitrarily large space structures and spacecraft configurations.
Information derived from accelerometer readings benefits important engineering and control functions. Report discusses methodology for detection, identification, and analysis of motions within space station. Techniques of vibration and rotation analyses, control theory, statistics, filter theory, and transform methods integrated to form system for generating models and model parameters that characterize total motion of complicated space station, with respect to both control-induced and random mechanical disturbances.
The objective was to demonstrate the utility of synthetic aperture radar in ice sheet studies. The major advantage of SAR imagery over visible imagery is the all-weather capability of radar and the ability to specify look angle. Available digital SAR imagery over ice sheets was collected and examined both qualitatively and quantitatively using corroborative data, such as LANDSAT imagery, to confirm feature identification and interpretations. A simple scattering model will be developed to assess the relative importance of surface topography, composition, and subsurface layering to the intensity of radar backscatter. Recommendations of system parameters will be made for optimal SAR operation over ice sheets.
The quadrupole-polarization theory has been applied to the 3d(sup 6)4S(D-6)4f and 5g subconfigurations of Fe I by a parametric fit, and the fitted parameters are used to predict levels in the 6g and 6h subconfigurations. Using the predicted values, we have computed the 4f-6g and 5g-6h transition arrays and made identifications in the ATMOS infrared solar spectrum. The newly identified 6g and 6h levels, based on ATMOS wavenumbers, are combined with the 5g levels and found to agree with the theoretical values with a root mean-squared-deviation of 0.042/ cm. Our approach yields a polarizability of 28.07 a(sub o, sup 2) and a quadrupole moment of 0.4360 +/- 0.0010 ea(sup 2, sub o) for Fe II, as well as an improved ionization potential of 63737.700 +/- 0.010/ cm for Fe I.
A computer program called FRFit (Frequency Response Fitting) for matching single-input single-output (SISO) transfer function models to empirical frequency response data is described. The program was written in MATLAB and has a graphical user interface (GUI). It is interactive in that the user manually builds the transfer function model using ``elementary factors'' (gain, delay, differentiators and integrators, and first- and second-order poles and zeros) and adjusts their values with sliders or entry fields. A nonlinear optimization can also be used to determine maximum-likelihood estimates of the transfer function parameters and their associated uncertainties. The program has some usefulness as a teaching aid, and can be applied to model structure determination, reduced-order modeling, preliminary analysis, and other system identification problems. FRFit is demonstrated using example problems, including the identification of aircraft transfer functions and rational function approximations of Theodorsen's function.
Chemical looping combustion (CLC) is a promising combustion technology that generates heat and sequestration-ready carbon dioxide that is undiluted by nitrogen from the combustion of carbonaceous fuels with an oxygen carrier, or metal oxide. This process is highly dependent on the reactivity and stability of the oxygen carrier. The development of oxygen carriers remains one of the major barriers for commercialization of CLC. Synthetic oxygen carriers, consisting of multiple metal components, have demonstrated enhanced performance and improved CLC operation compared to single metal oxides. However, identification of the complex mixed metal oxide phases that form after calcination or during CLC reactions has been challenging. Without an understanding of the dominant metal oxide phase, it is difficult to determine reaction parameters and the oxygen carrier reduction pathway, which are necessary for CLC reactor design. This is particularly challenging for complex multi-component oxygen carriers such as copper iron manganese oxide (CuFeMnO 4 ). This study aims to differentiate the unique phase formation of a highly reactive, complex trimetallic oxygen carrier, CuFeMnO 4 , from its single and bimetallic counterparts using thermochemical and reaction data obtained from simultaneous differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) during temperature programmed reductions (TPR) with methane. DSC/TGA experiments during TPR with methane provides heat flow data and corresponding reaction rate data that can be used to determine reaction routes and mechanisms during methane reduction. Furthermore, non-isothermal TPR data provides the advantage of distinguishing reactions that may not be observable in isothermal analysis. The detailed thermochemical and reaction data, obtained during TPR with methane, distinguished a unique reduction pathway for CuFeMnO 4 that differed from its single and bimetallic counterparts. This is remarkable since X-ray diffraction (XRD) data alone could not be used to distinguish the reactive trimetallic oxide phase due to overlapping peaks from various single and mixed metal oxides. The unique reduction pathway of CuFeMnO 4 was further characterized in this study using in-situ XRD TPR with methane to determine changes in the dominant trimetallic phase that influenced the thermochemical and reaction rate data.
Here, we characterize the three-orbital Hubbard model using state-of-the-art determinant quantum Monte Carlo (DQMC) simulations with parameters relevant to the cuprate high-temperature superconductors. The simulations find that doped holes preferentially reside on oxygen orbitals and that the (π,π) antiferromagnetic ordering vector dominates in the vicinity of the undoped system, as known from experiments. The orbitally-resolved spectral functions agree well with photoemission spectroscopy studies and enable identification of orbital content in the bands. A comparison of DQMC results with exact diagonalization and cluster perturbation theory studies elucidates how these different numerical techniques complement one another to produce a more complete understanding of the model and the cuprates. Interestingly, our DQMC simulations predict a charge-transfer gap that is significantly smaller than the direct (optical) gap measured in experiment. Most likely, it corresponds to the indirect gap that has recently been suggested to be on the order of 0.8 eV, and demonstrates the subtlety in identifying charge gaps.
Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.
Given a Volterra series representations of an input-output system, the authors present a result concerning conditions under which there exists a feedback linearizable realization of the nonlinear system. In addition, they have developed a method to compute the unknown parameters in a canonical form realization from the known Volterra kernels. A finite number of parameters can be calculated from an associated finite number of the kernels. The authors restrict themselves to single-input, single-output continuous-time systems. It is assumed that the systems have real analytic state-space representations. The realization results are local, taking place a neighborhood of the origin in state space.
As a result of the increase in the practice of tattooing, the US Food and Drug Administration has identified a need for improved analytical methods to detect the pigments and potential impurities in the inks. Raman spectroscopy allows for nondestructive identification of compounds and is commonly used in art, archaeology, and forensics; however, the technique has only limitedly been applied to the identification of tattoo pigments. In this study, approximately 30 inorganic, organometallic, and organic pigments were evaluated with Raman spectroscopy by using 532, 633, and 780‐nm lasers. Individual optimization of the instrumental parameters was performed for each pigment in order to enhance spectral quality. This research highlights the need for multiple laser interrogation, as the spectra of some pigments were difficult to obtain by using a particular wavelength due to interferences from absorption or fluorescence. However, by using these multiple wavelengths, all pigments could be identified by their unique spectral features. A spectral library of the pigments was created for each laser wavelength and then challenged with pigments from multiple manufacturers. All pigments were identified correctly, and the method is poised to be an effective, noninvasive means for qualitatively identifying tattoo pigments. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.
Mechanical serial sectioning (MSS) provides detailed microstructural information across large length scales. By repeatedly removing thin layers of material and imaging the exposed surface, a 3D representation of a specimen’s internal structure can be constructed, enabling failure analysis and feature identification that are otherwise inaccessible via conventional 2D or nondestructive evaluation techniques. Achieving consistent and accurate material removal can be challenging due to system variability, requiring an experienced operator to manually adjust parameters, prolonging data collection times and necessitating post-processing routines to standardize the data. Here, to address these challenges, this paper presents the employment of a one-step model predictive control (MPC) framework tailored to a run-to-run (R2R) controller. The R2R-MPC controller automates the parameter selection process, improving the consistency of material removal through iterative feedback for disturbance rejection and accurate tracking of the target removal rate. Using a data-driven approach, the controller robustly adapts to changing material characteristics. The effectiveness of the R2R-MPC controller is demonstrated through simulation and experimental results and compared to previous data collection procedures.
The isovalent-substituted iron pnictide compound SrFe 2 (As 1– x P x ) 2 exhibits multiple evidence for nodal superconductivity via various experimental probes, such as the penetration depth, nuclear magnetic resonance and specific heat measurements. The direct identification of the nodal superconducting (SC) gap structure is challenging, partly because the presence of nodes is not protected by symmetry but instead caused by an accidental sign change of the order parameter, and also because of the three-dimensionality of the electronic structure. We have studied the SC gaps of SrFe 2 (As 0.65 P 0.35 ) 2 in three-dimensional momentum space by synchrotron and laser-based angle-resolved photoemission spectroscopy. The three hole Fermi surfaces (FSs) at the zone center have SC gaps with different magnitudes, whereas the SC gaps of the electron FSs at the zone corner are almost isotropic and k z -independent. As a possible nodal SC gap structure, we propose that the SC gap of the outer hole FS changes sign around the Z-X [(0, 0, 2 π ) – ( π , π , 2 π )] direction.
One area in which large space systems require new technology is attitude control. The paper presents an adaptive control philosophy applicable to the control of distributed systems. An adaptive control system is described for stabilization of the flexible modes of a spinning ring. The system consists of a modal decomposition and identification subsystem, a gain adjustment subsystem, and a feedback control subsystem. Simulations are presented illustrating the adaptive capability of the system. The adaptive controller did produce stable results by quickly identifying the parameter differences and adjusting the feedback controller gains.
The focus of the Worcester Polytechnic Institute (WPI) Advanced Space Design Program was the preliminary design of the Integrated Support Structure for GASCAN II, a Get Away Special canister donated by the MITRE Corporation. Two teams of three students each worked on the support structure. There was a structural design team and a thermal design team. The structure will carry three experiments also undergoing preliminary design this year, the mu-gravity Ignition Experiment, the Rotational Flow in Low Gravity Experiment, and the Ionospheric Properties and Propagation Experiment. The structural design team was responsible for the layout of the GASCAN and the preliminary design of the structure itself. They produced the physical interface specifications defining the baseline weights and volumes for the equipment and produced layout drawings of the system. The team produced static and modal finite element analysis of the structure using ANSYS. The thermal design team was responsible for the power and timing requirements of the payload and for the identification and preliminary analysis of potential thermal problems. The team produced the power, timing, and energy interface specifications and assisted in the development of the specification of the battery pack. The thermal parameters of each experiment were cataloged and the experiments were subjected to worst case heat transfer scenarios.
The SOHO (Solar and Heliospheric Observatory) coronagraphs will offer an unprecedented opportunity to observe coronal streamer material and determine its outward motion. The inner C1 and C2 coronagraphs should enable ready identification of coronal streamers and their surface manifestations. The outer C3 coronagraph should be able to trace streamers to nearly interplanetary distances from the Sun. Using a modeling program designed to determine parameters from co-rotating heliospheric material viewed by the Helios spacecraft, the locations in position angle and distance from the Sun of several modeled streamers are shown. These models demonstrate the expected differences in streamer location when different assumptions are used for the outward acceleration of the streamer material.
Spaceflight, just like aging, causes profound changes in musculoskeletal parameters, which result in decreased bone density and muscular weakness. As these conditions decrease our ability to conduct long-term manned space missions, and increase bone frailty in the elderly, the identification of genes responsible for the apparition of these physiological changes will be of great benefit. Thus, we developed and implemented a new microarray approach to investigate the changes in normal WI38 human fibroblast gene expression that arise as a consequence of space flight. Using our microarray, we identified changes in the level of expression of 10 genes, belonging to either the tumor necrosis factor- (TNF) or interleukin- (IL) related gene families in fibroblasts when WI38 cells exposed to microgravity during the STS-93 Space Shuttle mission were compared with ground controls. The genes included two ligands from the TNF superfamily, TWEAK and TNFSF15; two TNF receptor-associated proteins, NSMAF and PTPN13; three TNF-inducible genes, ABC50, PTX3, and SCYA13; TNF-alpha converting enzyme, IL-1 receptor antagonist, and IL-15 receptor alpha chain. Most of these are involved in either the regulation of bone density, and as such the development of spaceflight osteopenia, or in the development of proinflammatory status.
NOvA is a long-baseline neutrino oscillation experiment using Fermilab's 700 kW NuMI muon neutrino beam. Two functionally identical scintillator detectors are placed off the beam axis, separated by 810 km oscillation baseline. Both detectors have high active material fractions and are finely segmented allowing for precise identification and analysis of neutrino interactions. By observing both the disappearance of muon (anti)neutrinos and appearance of electron (anti)neutrinos in the beam, NOvA can impose constraints on the yet undetermined parameters of neutrino oscillation phenomenon, such as the neutrino mass ordering, CP violation and the octant of the large mixing angle. NOvA also studies neutral-current neutrino interactions, thus extending its scope beyond the standard three-flavor paradigm. This paper presents the latest NOvA results with the complete neutrino data sample up to date and first antineutrino data collected since February 2017.