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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Validation of Shadowgraph Spectral Analysis using an SLS Block 2 Wind-Tunnel Model

A flow quantification method has previously been developed that uses high-speed shadowgraph images to extract frequency content of a transonic flow field around a wind-tunnel model. In this method, a classical spectral analysis is performed on shadowgraph video pixels to identify their intensity fluctuation frequencies. The spatial change of the image intensity is related to the second derivative of the density gradient. Examining the change of the density gradient as a function of time can be used to understand the flow-field pressure fluctuations. Time-varying pressure measurements collected during a Space Launch System (SLS) unsteady aerodynamic test of an advanced Block 2 booster design are used to calibrate the shadowgraph-based measurements. The test was performed at the NASA Ames Research Center Unitary Plan Wind Tunnel 11- by 11-foot test section and investigated the aerodynamic environment downstream of candidate booster forward attachment hardware geometries of the SLS Block 2 Cargo configuration. Time-correlated shadowgraph video and model surface pressure measurements were acquired during the test and served as an ideal opportunity to calibrate and validate the shadowgraph image intensity method.

Shadowgraph↗
NASA NTRSDate not supplied

Validation of Shadowgraph Spectral Analysis using an SLS Block 2 Wind-Tunnel Model

A flow quantification method has previously been developed that uses high-speed shadowgraph images to extract frequency content of a transonic flow field around a wind-tunnel model. In this method, a classical spectral analysis is performed on shadowgraph video pixels to identify their intensity fluctuation frequencies. The spatial change of the image intensity is related to the second derivative of the density gradient. Examining the change of the density gradient as a function of time can be used to understand the flow-field pressure fluctuations. Time-varying pressure measurements collected during a Space Launch System (SLS) unsteady aerodynamic test of an advanced Block 2 booster design are used to calibrate the shadowgraph-based measurements. The test was performed at the NASA Ames Research Center Unitary Plan Wind Tunnel 11- by 11-foot test section and investigated the aerodynamic environment downstream of candidate booster forward attachment hardware geometries of the SLS Block 2 Cargo configuration. Time-correlated shadowgraph video and model surface pressure measurements were acquired during the test and served as an ideal opportunity to calibrate and validate the shadowgraph image intensity method.

Shadowgraph↗
NASA NTRS2022

How Well do Multisatellite Products Capture the Space-Time Dynamics of Precipitation? Part II: Building an Error Model Through Spectral System Identification

Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatio-temporal scale. We propose a spectral error model which accounts for the neighboring space-time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency-wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space-time linear filtering term. The random errors are represented as a non-stationary additive noise. The spectral error model is applied to the IMERG multi satellite precipitation product and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge-radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass. While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from totally different sources shall not be expected to automatically have statistically independent errors.

Precipitation↗
DOE OSTI2024

Geomagnetic Disturbance Uncertainty Quantification Modeling: An Electromagnetic Transient and Steady-State Simulation Based Approach

Geomagnetic disturbances have been shown to disrupt the operation of the bulk electrical system through low-frequency effects in the earth's magnetic field that in turn induce changing electrical fields on the earth's surface. As a result, geomagnetically induced currents flow in transmission lines, introducing the risk for widespread damage to high-voltage transformers and voltage collapse due to induced reactive power loss. In a previous paper, the authors showed how certain modeling assumptions can lead to results that are unacceptable in certain edge case scenarios. In this paper, the authors build on their previous results using modeling uncertainty quantification in order to compare the results from electromagnetic transient program modeling software, ATP, to the positive-sequence calculation software, PowerWorld.

Bretas, Arturo Suman↗
NASA NTRS2018

Quantification of Dynamic Model Validation Metrics Using Uncertainty Propagation from Requirements

The Space Launch System, NASA's new large launch vehicle for long range space exploration, is presently in the final design and construction phases, with the first launch scheduled for 2019. A dynamic model of the system has been created and is critical for calculation of interface loads and natural frequencies and mode shapes for guidance, navigation, and control (GNC). Because of the program and schedule constraints, a single modal test of the SLS will be performed while bolted down to the Mobile Launch Pad just before the first launch. A Monte Carlo and optimization scheme will be performed to create thousands of possible models based on given dispersions in model properties and to determine which model best fits the natural frequencies and mode shapes from modal test. However, the question still remains as to whether this model is acceptable for the loads and GNC requirements. An uncertainty propagation and quantification (UP and UQ) technique to develop a quantitative set of validation metrics that is based on the flight requirements has therefore been developed and is discussed in this paper. There has been considerable research on UQ and UP and validation in the literature, but very little on propagating the uncertainties from requirements, so most validation metrics are "rules-of-thumb;" this research seeks to come up with more reason-based metrics. One of the main assumptions used to achieve this task is that the uncertainty in the modeling of the fixed boundary condition is accurate, so therefore that same uncertainty can be used in propagating the fixed-test configuration to the free-free actual configuration. The second main technique applied here is the usage of the limit-state formulation to quantify the final probabilistic parameters and to compare them with the requirements. These techniques are explored with a simple lumped spring-mass system and a simplified SLS model. When completed, it is anticipated that this requirements-based validation metric will provide a quantified confidence and probability of success for the final SLS dynamics model, which will be critical for a successful launch program, and can be applied in the many other industries where an accurate dynamic model is required.

Brown, Andrew M.↗
NASA NTRSDate not supplied

SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method

Uncertainty in structural loading during launch is a significant concern in the development of spacecraft and launch vehicles. Small variations in launch vehicle and payload mode shapes and their interaction can result in significant variation in system loads. In many cases involving large aerospace systems it is difficult, not economical, or impossible to perform a system modal test. However, it is still vital to obtain test results that can be compared with analytical predictions to validate models. Instead, the “Building Block Approach” is used in which system components are tested individually. Component models are correlated and updated to agree as best they can with test results. The Space Launch System consists of a number of components that are assembled into a launch vehicle. Finite element models of the components are developed, reduced to Hurty/Craig-Bampton models and assembled to represent different phases of flight. The only opportunity to obtain modal test data from an assembled Space Launch System will be during the Integrated Modal Test. There is always uncertainty in every model, which flows into uncertainty in predicted system results. Uncertainty Quantification is used to determine statistical bounds on prediction accuracy based on model uncertainty. For the Space Launch System, model uncertainty is at the Hurty/Craig-Bampton component level. Uncertainty in the Hurty/Craig-Bampton components is quantified using the hybrid parametric variation approach that combines parametric and nonparametric uncertainty. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. This type of uncertainty cannot be represented by variations infinite element model input parameters and thus cannot be included in a parametric approach. However, model-form uncertainty can be modeled using a nonparametric approach based on random matrix theory. The hybrid parametric variation method requires the selection of dispersion values for the Hurty/Craig-Bampton fixed-interface eigenvalues, and the Hurty/Craig-Bampton stiffness matrices. Component test/analysis frequency error is used to identify the fixed-interface eigenvalue dispersions, while test/analysis cross-orthogonality is used to identify stiffness dispersion values. The hybrid parametric variation uncertainty quantification approach is applied to the Space Launch System Integrated Modal Test configuration. Monte Carlo analysis is performed, and statistics are determined for modal correlation metrics, frequency response from Integrated Modal Test shakers to selected accelerometers, as well as other metrics for determining how well target modes are excited and identified. If the predicted uncertainty envelopes future Integrated Modal Test results, then there will be increased confidence in the utility of the component-based hybrid parametric variation uncertainty quantification approach.

Uncertainty Quantification↗
NASA NTRS2003

The Conference Proceedings of the 2003 Air Transport Research Society (ATRS) World Conference, Volume 5

The UNO Aviation Institute Monograph Series began in 1994 as a key component of the education outreach and information transfer missions of the Aviation Institute and the NASA Nebraska Space Grant & EPSCoR Programs. The series is an outlet for aviation materials to be indexed and disseminated through an efficient medium. Publications are welcome in all aspects of aviation. Publication formats may include, but are not limited to, conference proceedings, bibliographies, research reports, manuals, technical reports, and other documents that should be archived and indexed for future reference by the aviation and world wide communities. The Conference proceedings of the 2003 Air Transport Research Society (ATRS) world conference, volume 5 is presented. The topics include: 1) The Temporal Configuration of Airline Networks in Europe; 2) Determination and Applications of Environmental Costs at Different Sized Airports-Aircraft Noise and Engine Emissions; 3) Cost Effective Measures to Reduce CO2 Emissions in the Air Freight Sector; 4) An Assessment of the Sustainability of Air Transport System: Quantification of Indicators; 5) Regulation, Competition and Network Evolution in Aviation; 6) Regulation in the Air: Price and Frequency Cap; 7) Industry Consolidation and Future Airline Network Structures in Europe; 8) Application of Core Theory to the U.S. Airline Industry; 9) Air Freight Transshipment Route Choice Analysis; 10) A Fuzzy Approach of the Competition on Air Transport Market; and 11) Developing Passenger Demand Models for International Aviation from/to Egypt: A Case Study of Cairo Airport and Egyptair.

Bowen, Brent↗
NASA NTRS2001

A Model for Rate-Dependent Hysteresis in Piezoceramic Materials Operating at Low Frequencies

This paper addresses the modeling of certain rate-dependent mechanisms which contribute to hysteresis inherent to piezoelectric materials operating at low frequencies. While quasistatic models are suitable for initial material characterization in some applications, the reduction in coercive field and polarization values which occur as frequencies increase must be accommodated to achieve the full capabilities of the materials. The model employed here quantifies the hysteresis in two steps. In the first, anhysteretic polarization switching is modeled through the application of Boltzmann principles to balance the electrostatic and thermal energy. Hysteresis is then incorporated through the quantification of energy required to translate and bend domain walls pinned at inclusions inherent to the materials. The performance of the model is illustrated through a fit to low frequency data (0.1 Hz - 1 Hz) from a PZT5A wafer.

Smith, Ralph C.↗
DOE OSTI2022

Caution on Using Tetrahydrofuran for Processing Crystalline Silica Samples From Engineered Stone for XRD Analysis

Abstract We conducted laboratory experiments to investigate a suspected effect of tetrahydrofuran (THF) on quantifying crystalline silica in samples collected from working with engineered stone when THF is used to process samples prior to the X-ray diffraction (XRD) analysis. Two groups of samples from grinding either engineered stone or granite were simultaneously taken from a laboratory testing system, with one group of samples using THF for processing and another group using muffle furnace for ashing. For each stone type, we also tested four levels of respirable dust loading on the samples by varying the grinding time from 1 to 8 min. Statistical analysis of the experimental results on crystalline silica contents of the two groups of samples showed that the difference between the two methods was not significant (P ≥ 0.05) for the granite at all four levels of respirable dust loading and for the engineered stone at the two levels of respirable dust loading greater than 0.5 mg. However, the crystalline silica content from using THF processing was significantly lower (P = 0.001) than that from using muffle furnace ashing for engineered stone when the respirable dust loading levels were less than 0.5 mg. For the engineered stone dust samples with grinding times of 1 and 2 min, the average respirable dust loading was about 0.19 and 0.34 mg, respectively; while the crystalline silica content from using THF processing was 30.9 and 21.5% lower than that from using muffle furnace ashing, respectively. Since most full-shift samples from field assessments in this industry are expected to have respirable dust loading less than 0.5 mg, muffle furnace or radio frequency plasma ashing should be specified as the preferred sample processing method instead of the THF processing method for quantification of crystalline silica when engineered stone is expected to present to avoid artificially reduced silica content values, which are likely caused by the reactions between THF and the resins in engineered stone.

Public, Environmental & Occupational Health↗
DOE OSTI2020

Efficient compressive and Bayesian characterization of biphoton frequency spectra

Frequency-bin qudits constitute a promising tool for quantum information processing, but their high dimensionality can make for tedious characterization measurements. Here we introduce and compare compressive sensing and Bayesian mean estimation for recovering the spectral correlations of entangled photon pairs. Using a conventional compressive sensing algorithm, we reconstruct joint spectra with up to a 26-fold reduction in measurement time compared to the equivalent raster scan. Applying a custom Bayesian model to the same data, we then additionally realize reliable and consistent quantification of uncertainty. Finally, these efficient methods of biphoton characterization should advance our ability to use the high degree of parallelism and complexity afforded by frequency-bin encoding.

42 ENGINEERING↗
DOE OSTI2025

Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions

Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF

Li, Bonan↗
DOE OSTI2022

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

Field intercomparison of a novel optical sensor for formaldehyde quantification

A one-week in situ intercomparison campaign was completed on the Rice University campus for measuring HCHO using three different techniques, including a novel optical sensor based on difference frequency generation (DFG) operating at room temperature. Two chemical derivatization methods, 2,4-dinitrophenylhydrazine (DNPH) and o-(2,3,4,5,6-pentafluorobenzyl) hydroxylamine (PFBHA), were deployed during the daylight hours for three- to four-hour time-integrated samples. A real-time optical sensor based on laser absorption spectroscopy was operated simultaneously, including nighttime hours. This tunable spectroscopic source based on difference frequency mixing of two fiber-amplified diode lasers in periodically poled LiNb03 (PPLN) was operated at 3.5315 micrometers (2831.64 cm 1) to access a strong HCHO ro-vibrational transition free of interferences from other species. The results showed a bias of -1.7 and -1.2 ppbv and a gross error of 2.6 and 1.5 ppbv for DNPH and PFBHA measurements, respectively, compared with DFG measurements. These results validate the DFG sensor for time-resolved measurements of HCHO in urban areas.

NASA Discipline Life Sciences Technologies↗
DOE OSTI2021

Hydrogen Component Leak Rate Quantification for System Risk and Reliability Assessment through QRA and PHM Frameworks: Preprint

The National Renewable Energy Laboratory's (NREL) Hydrogen Safety Research and Development (HSR&D) program in collaboration with the University of Maryland's Systems Risk and Reliability Analysis Laboratory (SyRRA) are working to improve reliability and reduce risk in hydrogen systems. This approach strives to use quantitative data on component leaks and failures, together with Prognosis and Health Management (PHM), and Quantitative Risk Assessment (QRA) to identify at-risk components, reduce component failures and downtime, and predict when components require maintenance. Hydrogen component failures increase facility maintenance cost, facility downtime, and reduce public acceptance of hydrogen technologies, ultimately increasing facility size and cost because of potentially overly conservative requirements. Leaks are a predominant failure mode for hydrogen components. However, uncertainties in the amount of hydrogen emitted from leaking components and the frequency of those failure events limit the understanding of the risks that they present under real-world operational conditions. NREL has deployed a test fixture, the Leak Rate Quantification Apparatus (LRQA), to quantify the mass flow rate of leaking gases from medium and high-pressure components that have failed while in service. Quantitative hydrogen leak rate data from this system could ultimately be used to better inform risk assessment and Regulation Codes and Standards (RCS). Parallel activity explores the use of PHM and QRA techniques to assess and reduce risk, thereby improving safety and reliability of hydrogen systems. The results of QRAs could further provide a systematic and science-based foundation for the design and implementation of RCS, as in the latest versions of the NFPA 2 code for gaseous hydrogen stations. Alternatively, data-driven techniques of PHM could provide new damage diagnosis and health-state prognosis tools. This research will help end users, station owners and operators, and regulatory bodies move towards risk-informed preventative maintenance versus emergency corrective maintenance, reducing cost and improving reliability. Predictive modelling of failures could improve safety and affect RCS requirements such as setback distances at liquid refuelling sites. The combination of leak rate quantification research, PHM, and QRA can lead to better informed models enabling data-based decision to be made for hydrogen system safety improvements.

codes and standards↗
DOE OSTI2022

Characterizing the effect of incrementally increasing dry bulb temperature on linear and nonlinear measures of heart rate variability in nonpregnant, mid-gestation, and late-gestation sows

Abstract Characterizing the sow physiological response to an increased heat load is essential for effective heat stress mitigation. The study objective was to characterize the effects of a 400-min heating episode on sow heart rate variability (HRV) at different reproductive stages. HRV is a commonly used noninvasive proxy measure of autonomic function. Twenty-seven sows were enrolled in the study according to their gestation stage at time of selection: 1) nonpregnant (NP; n = 7), 2) mid-gestation (MID; 57.3 ± 11.8 d gestation; n = 11), and 3) late-gestation (LATE; 98.8 ± 4.9 d gestation; n = 8). The HRV data utilized in the study were collected from each pig as the dry bulb temperature in the room increased incrementally from 19.84 ± 2.15 °C to 35.54 ± 0.43 °C (range: 17.1–37.5 °C) over a 400-min period. After data collection, one 5-min set of continuous heart rate data were identified per pig for each of nine temperature intervals (19–20.99, 21–22.99, 23–24.99, 25–26.99, 27–28.99, 29–30.99, 31–32.99, 33–34.99, and 35–36.99 °C). Mean inter-beat interval length (RR), standard deviation of r-r intervals (SDNN), root mean square of successive differences (RMSSD), high frequency spectral power (HF), sample entropy (SampEn), short-term detrended fluctuation analysis (DFAα1), and three measures (%REC, DET, LMEAN) derived from recurrence quantification analysis were calculated for each data set. All data were analyzed using the PROC GLIMMIX procedure in SAS 9.4. Overall, LATE sows exhibited lower RR than NP sows (P < 0.01). The standard deviation of r–r intervals and RMSSD differed between each group (P < 0.01), with LATE sows exhibiting the lowest SDNN and RMSSD and NP sows exhibiting the greatest SDNN and RMSSD. Late-gestation sows exhibited lower HF than both MID and NP sows (P < 0.0001), greater DFA values than NP sows (P = 0.05), and greater DET compared to MID sows (P = 0.001). Late-gestation also sows exhibited greater %REC and LMEAN compared to MID (P < 0.01) and NP sows (all P < 0.01). In conclusion, LATE sows exhibited indicators of greater autonomic stress throughout the heating period compared to MID and NP sows. However, temperature by treatment interactions were not detected as dry bulb increased. Future studies are needed to fully elucidate the effect of gestational stage and increasing dry bulb temperature on sow HRV.

Agriculture↗
NASA NTRS2022

NASA Engineering and Safety Center Technical Bulletin No. 22-04: Uncertainty Quantification of Reduced Order Structural Dynamic Models

Uncertainty quantification (UQ) provides statistical bounds on prediction accuracy based on finite element model (FEM) uncertainty. An alternate method for UQ, called the Hybrid Parametric Variation (HPV) combines a parametric variation of the Hurty/Craig-Bampton (HCB) fixed-interface (FI) modal frequencies with a nonparametric variation (NPV) method. This provides a UQ method that can be traced to test data, which can be updated as additional data and improved correlated models become available.

Uncertainty Quantification↗
DOE OSTI2026

MULTI-FIDELITY MODELING AND UNCERTAINTY QUANTIFICATION OF INVERTER BASED RESOURCES IN INTEGRATED T&D SYSTEMS

Uncertainty quantification plays a pivotal role in improving the accuracy and reliability of inverter operation within modern power systems that are increasingly dominated by inverter-based resources (IBRs). IBRs, especially those operating under grid forming (GFM) control, rely heavily on a complex set of control parameters and system measurements to maintain voltage, frequency, and power balance. Traditional deterministic modeling approaches often fail to capture these parameter deviations, potentially resulting in suboptimal control actions, reduced system stability, or even instability under high penetration of IBRs. In this paper, we demonstrate the application of model calibration and uncertainty quantification (UQ) principles to an integrated transmission and distribution (T&D) model involving a GFM converter and provide a framework for prioritizing control improvements, guiding robust design, and informing adaptive strategies that can accommodate real-time variability in system conditions. The proposed approach could be valuable in enhancing the robustness of current and future power systems under increased IBR penetrations.

24 POWER TRANSMISSION AND DISTRIBUTION↗