Results for “uncertain systems” — Page 35

NASA NTRS1983

Space shuttle propulsion parameter estimation using optimal estimation techniques

The first twelve system state variables are presented with the necessary mathematical developments for incorporating them into the filter/smoother algorithm. Other state variables, i.e., aerodynamic coefficients can be easily incorporated into the estimation algorithm, representing uncertain parameters, but for initial checkout purposes are treated as known quantities. An approach for incorporating the NASA propulsion predictive model results into the optimal estimation algorithm was identified. This approach utilizes numerical derivatives and nominal predictions within the algorithm with global iterations of the algorithm. The iterative process is terminated when the quality of the estimates provided no longer significantly improves.

Source record↗

NASA NTRS2022

Compound Heat and Moisture Extreme Impacts on Global Crop Yields Under Climate Change

Extreme heat, drought and moisture excess are increasingly co-occurring within a single growing season, impacting crop yields in global breadbasket regions. In this Review, we synthesize understanding of compound heat and moisture extremes, their impacts on global crop yields and implications for adaptation. Heat and moisture extremes and their impacts become compounded through crop-physiological interactions, heat–moisture couplings in the climate system and crop–atmosphere interactions. Since around 2000, these compound extremes, and hot droughts in particular, have been linked to especially poor harvests (up to 30% yield losses) in regions such as India, Ethiopia, the USA, Europe and Russia. However, in some cases, combinations of crop stresses might generate compensating effects. Compound extremes are projected to increase in frequency and amplitude in the future, but, owing to the biophysical interdependence among temperature, water and crop physiology, the net yield effects of such future compound extremes remain uncertain. Accordingly, compound extremes will necessitate comprehensive agricultural adaptation strategies geared towards multi-stress resilience, as adaptations that work for single climate stresses could be maladaptive under combined stresses. An integrated understanding of heat and water in soil–plant–atmosphere dynamics is urgently needed to understand risks and suitably adapt cropping systems to compounding climate impacts.

Agroecology↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.

Reliability↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.

Reliability↗

DOE OSTI2020

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Report for Task 10: Controllability and Observability Analysis (Deliverable D7))

It is common to have less than full observability for distribution systems, as the number of available measurements are not sufficient. Considering increasing penetration of distributed energy resources (DERs), it is imperative to understand and control distribution systems better. Developed for the transmission system, the original numerical observability analysis methods were based on the canonical DC Power Flow (DC-PF) model. However, the DC-PF model is ill-suited for the distribution system in which line resistance to reactance (R/X) ratios can be near unity. In this report, we apply the non-iterative Observability Test (OT) and Observable Island Identification Procedure (OIIP) in to the gain matrix from the distribution system state estimator. Because sensors are few in distribution systems, pseudo-measurements based on historical load profiles and zero-injection virtual measurements are often employed to ensure observability. However, both the observability and the accuracy of the state estimates are uncertain when historical data is limited/unavailable.

42 ENGINEERING↗

DOE OSTI2024

Modeling the Impact of Grain Size on Corrosion Behavior of Ni-Based Alloys in Molten Chloride Salt via Cellular Automata

Molten chloride salts hold significant promise as both thermal transfer and storage media for next-generation concentrated solar power (CSP) systems. However, molten chlorides pose a considerable corrosion risk to structural materials, particularly Ni-based alloys. One approach to enhancing corrosion resistance is through the optimization of grain structure; however, it remains uncertain whether increasing or decreasing grain size enhances corrosion resistance. A cellular automata (CA) program was developed to evaluate the interplay between grain size and corrosion in Ni-based alloy. Our CA program tracks alloy composition, surface roughness, and thickness loss via a graphical user interface, displaying corrosion and diffusion status, and multiple user input cards for tuning the simulation. CA simulations of Inconel 625 indicate enhanced corrosion resistance with increased grain size, with passivating oxides offering limited protection. Additionally, the temporal evolution of alloy surface roughness demonstrates notable fluctuations, with abrupt increases attributed to corrosion along vertical grain boundaries and sudden decreases to grain detachment from the protective film.

Materials Science↗

DOE OSTI2023

Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data

Vehicular incidents, especially those involving tractor trailers, are increasing in number every year. These events are extremely costly for fleets, in terms of damage or loss of property, loss of efficiency, and certainly in terms of loss of life. Although the U.S. Department of Transportation (DOT) is responsible for performing inspections, and fleet managers are encouraged to maintain their fleet and participate in regular inspections, it is uncertain whether these inspections are occurring at a frequency that is necessary to prevent incidents. The Federal Motor Carrier Safety Administration (FMCSA) of the DOT manages and maintains the Motor Carrier Management Information System (MCMIS) dataset, which contains all incident and inspection data regarding commercial vehicles in the U.S. The purpose of this preliminary analysis was to explore the MCMIS dataset through spatiotemporal analyses, to uncover findings that may hint at potential improvements in the DOT inspection process and highlight location-specific trends in the dataset. These analyses are novel, as previous research using the MCMIS dataset only examined the data at the state or county level, not at a national scale. The results from the analyses pinpointed specific major metropolitan areas, namely Harris County (Houston), Texas, and three of the New York boroughs (Kings, Queens, and the Bronx), which were found to have increasing incident rates during the study period (2016–2020). An overview of potential causal factors contributing to this increase are provided as well as an overview of the inspection process, and suggestions for improvement relative to the highlighted locations in Texas and New York are also provided. Ultimately, it is suggested that the incorporation of advanced technology and automation may prove beneficial in reducing the occurrence of events that lead to incidents and may also help in the inspection process.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

On the Accuracy of the ALMA Flux Calibration in the Time Domain and across Spectral Windows

A diverse array of science goals requires accurate flux calibration of observations with the Atacama Large Millimeter/submillimeter array (ALMA); however, this goal remains challenging due to the stochastic time-variability of the “grid” quasars ALMA uses for calibration. In this work, we use 343.5 GHz (Band 7) ALMA Atacama Compact Array observations of four bright and stable young stellar objects over seven epochs to independently assess the accuracy of the ALMA flux calibration and to refine the relative calibration across epochs. The use of these four extra calibrators allows us to achieve an unprecedented relative ALMA calibration accuracy of ~3%. On the other hand, when the observatory calibrator catalog is not up to date, the Band 7 data calibrated by the ALMA pipeline may have a flux calibration poorer than the nominal 10%, which can be exacerbated by weather-related phase decorrelation when self-calibration of the science target is either not possible or not attempted. We also uncover a relative flux calibration uncertainty between spectral windows of 0.8%, implying that measuring spectral indices within a single ALMA band is likely highly uncertain. We thus recommend various methods for science goals requiring high flux accuracy and robust calibration, in particular, the observation of additional calibrators combined with a relative calibration strategy, and observation of solar system objects for high absolute accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1991

Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems

A simple numerical procedure for estimating the stochastic robustness of a linear time-invariant system is described. Monte Carlo evaluations of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This analysis approach treats not only Gaussian parameter uncertainties but non-Gaussian cases, including uncertain-but-bounded variation. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. Trivial extensions of the procedure admit consideration of alternate discriminants; thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions can also be estimated. Results are particularly amenable to graphical presentation.

Stengel, Robert F.↗

DOE OSTI2020

An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

In many parts of the world, electric power systems have seen a significant shift toward generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty between power grids and gas pipelines and brings coordination and uncertainty management challenges. To address these issues, we propose an uncertainty management framework for uncertain, but bounded gas consumption by gas-fired power plants. The admissible ranges are computed based on a joint optimization problem for the combined gas and electricity networks, which involves chance-constrained scheduling for the electric grid and a novel robust optimization formulation for the natural-gas network. This formulation ensures feasibility of the integrated system with a high probability, while providing a tractable numerical formulation. A key advance with respect to existing methods is that our method is based on a physically accurate, validated model for transient gas pipeline flows. Our case study benchmarks our proposed formulation against methods that ignore how reserve activation impacts the fuel use of gas power plants and only consider predetermined gas consumption. Here, the results demonstrate the importance of considering uncertainty to avoid operating constraint violations and curtailment of gas to the generators.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering

The document presents recent algorithmic and implementation advances of the two-stage robust optimization (RO) solver PyROS, and a benchmarking study which demonstrates the utility of PyROS for two-stage RO problems. The advances include extensions of the scope of PyROS to models with uncertain variable bounds, improvements to the initializations of the subproblems used by the underlying cutting set algorithm, and extensions of the uncertainty set interfaces. The benchmarking study is performed on a library of over 8,500 instances, with variations in the nonlinearities, degree-of-freedom partitioning, uncertainty sets, and polynomial decision rule approximations. An amine-based CO2 capture case study is presented to demonstrate the utility of PyROS for large-scale process models. Overall, the results highlight the effectiveness of PyROS for obtaining robust solutions to optimization problems with uncertain equality constraints.

Sherman, Jason↗

NASA NTRS2015

Why Is Improvement of Earth System Models so Elusive? Challenges and Strategies from Dust Aerosol Modeling

Past decades have seen an accelerating increase in computing efficiency, while climate models are representing a rapidly widening set of physical processes. Yet simulations of some fundamental aspects of climate like precipitation or aerosol forcing remain highly uncertain and resistant to progress. Dust aerosol modeling of soil particles lofted by wind erosion has seen a similar conflict between increasing model sophistication and remaining uncertainty. Dust aerosols perturb the energy and water cycles by scattering radiation and acting as ice nuclei, while mediating atmospheric chemistry and marine photosynthesis (and thus the carbon cycle). These effects take place across scales from the dimensions of an ice crystal to the planetary-scale circulation that disperses dust far downwind of its parent soil. Representing this range leads to several modeling challenges. Should we limit complexity in our model, which consumes computer resources and inhibits interpretation? How do we decide if a process involving dust is worthy of inclusion within our model? Can we identify a minimal representation of a complex process that is efficient yet retains the physics relevant to climate? Answering these questions about the appropriate degree of representation is guided by model evaluation, which presents several more challenges. How do we proceed if the available observations do not directly constrain our process of interest? (This could result from competing processes that influence the observed variable and obscure the signature of our process of interest.) Examples will be presented from dust modeling, with lessons that might be more broadly applicable. The end result will either be clinical depression or there assuring promise of continued gainful employment as the community confronts these challenges.

Climate models↗

DOE OSTI2026

Energy-Optimal Vehicle Longitudinal Motion Control via Pontryagin’s Minimum Principle and Ultra-Local Model

Longitudinal vehicle motion control is essential for enhancing performance and optimizing a vehicle’s energy usage. However, it remains a challenging task due to the nonlinear and uncertain nature of vehicle dynamics, along with varying driving conditions. This paper presents a novel ultra-local optimal control approach based on Pontryagin’s Minimum Principle (PMP) that circumvents the need for detailed system identification by employing an ultra-local model. The control objective is to minimize the total energy consumption under boundary conditions while ensuring smooth traction force generation. The proposed approach is evaluated using a high-fidelity vehicle model in three representative scenarios: (i) nominal driving, (ii) a change in tire road friction coefficient (TRFC) from 0.5 to 0.65 and road slope from 0% to 5% during the maneuver, with target velocity unchanged, and (iii) a change in target velocity from 20 m/s to 0 m/s during the maneuver, while maintaining nominal TRFC and slope conditions. The simulation results demonstrate that the proposed method delivers robust performance, effectively balancing consumption and tracking accuracy in all tested scenarios.

Waleed khan, Muhammad [The University of Texas at ↗

DOE OSTI2020

Prescriptions for Correcting Ultraviolet-based Redshifts for Luminous Quasars at High Redshift

High-redshift quasars typically have their redshift determined from rest-frame ultraviolet (UV) emission lines. However, these lines, and more specifically the prominent C IV λ1549 emission line, are typically blueshifted yielding highly uncertain redshift estimates compared to redshifts determined from rest-frame optical emission lines. We present near-infrared spectroscopy of 18 luminous quasars at 2.15 < z < 3.70 that allows us to obtain reliable systemic redshifts for these sources. Together with near-infrared spectroscopy of an archival sample of 44 quasars with comparable luminosities and redshifts, we provide prescriptions for correcting UV-based redshifts. Our prescriptions reduce velocity offsets with respect to the systemic redshifts by ~140 km s -1 and reduce the uncertainty on the UV-based redshift by ~25% with respect to the best method currently used for determining such values. We also find that the redshifts determined from the Sloan Digital Sky Survey Pipeline for our sources suffer from significant uncertainties, which cannot be easily mitigated. In conclusion, we discuss the potential of our prescriptions to improve UV-based redshift corrections given a much larger sample of high-redshift quasars with near-infrared spectra.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics

Abstract. Anthropogenic emissions of aerosols and precursor compounds are known to significantly affect the energy balance of the Earth–atmosphere system, alter the formation of clouds and precipitation, and have a substantial impact on human health and the environment. Global models are an essential tool for examining the impacts of these emissions. In this study, we examine the sensitivity of model results to the assumed height of SO2 injection, seasonality of SO2 and black carbon (BC) particulate emissions, and the assumed fraction of SO2 emissions that is injected into the atmosphere as particulate phase sulfate (SO4) in 11 climate and chemistry models, including both chemical transport models and the atmospheric component of Earth system models. We find large variation in atmospheric lifetime across models for SO2, SO4, and BC, with a particularly large relative variation for SO2, which indicates that fundamental aspects of atmospheric sulfur chemistry remain uncertain. Of the perturbations examined in this study, the assumed height of SO2 injection had the largest overall impacts, particularly on global mean net radiative flux (maximum difference of −0.35 W m−2), SO2 lifetime over Northern Hemisphere land (maximum difference of 0.8 d), surface SO2 concentration (up to 59 % decrease), and surface sulfate concentration (up to 23 % increase). Emitting SO2 at height consistently increased SO2 and SO4 column burdens and shortwave cooling, with varying magnitudes, but had inconsistent effects across models on the sign of the change in implied cloud forcing. The assumed SO4 emission fraction also had a significant impact on net radiative flux and surface sulfate concentration. Because these properties are not standardized across models this is a source of inter-model diversity typically neglected in model intercomparisons. These results imply a need to ensure that anthropogenic emission injection height and SO4 emission fraction are accurately and consistently represented in global models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

TPSAS-NF1676L-12725-DND

Uncertainty analysis and robust design - increase confidence and consistency in aerospace vehicle safety predictions by developing improved methods for quantifying and managing uncertainty. Quantifying: uncertainty modeling (model uncertainty based on experimental data, simulations and/or expert opinion) and uncertainty propagation (given uncertainty models of a system’s inputs, how to propagate them through system models, to efficiently evaluate the corresponding system’s outputs?). Managing: robust design (generate designs that robustly accommodate uncertainty) and uncertainty decomposition (identify uncertainties that contribute the most to performance degradation and determine the parameters that should (not) be modeled as uncertain).

Sean P Kenny↗

NASA NTRSDate not supplied

Methods for System-Level Multidisciplinary Uncertainty Analysis of Low-Boom Flight Vehicles

Current research supporting NASA’s Commercial Supersonic Technology project is focused on the efficient prediction of uncertainty in sonic boom loudness generated by low-boom aircraft concepts. This paper focuses on research incorporating aircraft trim and aerostructural analysis into a multidisciplinary system-level uncertainty analysis. This enables the modeling of a steady-state representation of a point in the uncertainty space, simulating the vehicle as it would be flown. This approach also enables multiple uncertain parameters defining the configuration of the vehicle to be reduced to three: Mach number, altitude, and aircraft weight. To demonstrate this methodology, a case study exploring a conceptual low-boom supersonic aircraft is performed. Two different approaches are used to model the interactions between nearfield pressure signature analysis and sonic boom propagation, and their performance is evaluated in terms of accuracy and computational expense. One method uses a set of local surrogate models to generate a large number of nearfield signatures and perform Monte Carlo analysis. This method is found to produce, at a lower expense, uncertainty metrics that are comparable to the second method, in which uncertainty metrics are computed based on loudness metric values obtained directly from simulated nearfield signatures.

UQ↗

NASA NTRSDate not supplied

Method for System-Level Multidisciplinary Uncertainty Analysis of Low-Boom Flight Vehicles

Current research supporting NASA’s Commercial Supersonic Technology project is focused on the efficient prediction of uncertainty in sonic boom loudness generated by low-boom aircraft concepts. This paper focuses on research incorporating aircraft trim and aerostructural analysis into a multidisciplinary system-level uncertainty analysis. This enables the modeling of a steady-state representation of a point in the uncertainty space, simulating the vehicle as it would be flown. This approach also enables multiple uncertain parameters defining the configuration of the vehicle to be reduced to three: Mach number, altitude, and aircraft weight. To demonstrate this methodology, a case study exploring a conceptual low-boom supersonic aircraft is performed. Two different approaches are used to model the interactions between nearfield pressure signature analysis and sonic boom propagation, and their performance is evaluated in terms of accuracy and computational expense. One method uses a set of local surrogate models to generate a large number of nearfield signatures and perform Monte Carlo analysis. This method is found to produce, at a lower expense, uncertainty metrics that are comparable to the second method, in which uncertainty metrics are computed based on loudness metric values obtained directly from simulated nearfield signatures.

Supersonics↗