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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

PMSM Parameter Estimation using a Linear Unknown Input Interval Observer

In this paper, the problem of permanent magnet synchronous motor (PMSM) speed and unknown load torque estimation is addressed. For this purpose, a interval unknown input observer (UIO) for linear time-invariant (LTI) systems is used. First, the PMSM model is linearized in order to make it in a suitable form for the linear interval UIO. Then, the interval UIO is applied to allow the joint estimation of the motor speed and the unknown load torque disturbance. The main advantages of this approach is that it not only allows the joint state and unknown input estimation, but also to take the different uncertainty sources into account. Indeed, taking model and measurement uncertainty into account is crucial. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated with the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗
NASA NTRS1995

Credible Computations: Standard and Uncertainty

The discipline of computational fluid dynamics (CFD) is at a crossroad. Most of the significant advances related to computational methods have taken place. The emphasis is now shifting from methods to results. Significant efforts are made in applying CFD to solve design problems. The value of CFD results in design depends on the credibility of computed results for the intended use. The process of establishing credibility requires a standard so that there is a consistency and uniformity in this process and in the interpretation of its outcome. The key element for establishing the credibility is the quantification of uncertainty. This paper presents salient features of a proposed standard and a procedure for determining the uncertainty. A customer of CFD products - computer codes and computed results - expects the following: A computer code in terms of its logic, numerics, and fluid dynamics and the results generated by this code are in compliance with specified requirements. This expectation is fulfilling by verification and validation of these requirements. The verification process assesses whether the problem is solved correctly and the validation process determines whether the right problem is solved. Standards for these processes are recommended. There is always some uncertainty, even if one uses validated models and verified computed results. The value of this uncertainty is important in the design process. This value is obtained by conducting a sensitivity-uncertainty analysis. Sensitivity analysis is generally defined as the procedure for determining the sensitivities of output parameters to input parameters. This analysis is a necessary step in the uncertainty analysis, and the results of this analysis highlight which computed quantities and integrated quantities in computations need to be determined accurately and which quantities do not require such attention. Uncertainty analysis is generally defined as the analysis of the effect of the uncertainties involved in all stages of a process on the final responses. There are two approaches for conducting the uncertainty analysis: experimental and computational. These analyses and approaches are briefly described.

Mehta, Unmeel B.↗
DOE OSTI2025

Structural response reconstruction using a system-equivalent singular vector basis

Here, this paper develops a novel method for reconstructing the full-field response of structural dynamic systems using sparse measurements. The singular value decomposition is applied to a frequency response matrix relating the structural response to physical loads, base motion, or modal loads. The left singular vectors form a non-physical reduced basis that can be used for response reconstruction with far fewer sensors than existing methods. The contributions of the singular vectors to measured response are termed singular-vector loads (SVLs) and are used in a regularized Bayesian framework to generate full-field response estimates and confidence intervals. The reconstruction framework is applicable to the estimation of single data records and power spectral densities from multiple records. Reconstruction is successfully performed in configurations where the number of SVLs to identify is less than, equal to, and greater than the number of sensors used for reconstruction. In a simulation featuring a seismically excited shear structure, SVL reconstruction significantly outperforms modal FRF-based reconstruction and successfully estimates full-field responses with as few as two uniaxial accelerometers. SVL reconstruction is further verified in a simulation featuring an acoustically excited cylinder. Finally, response reconstruction and uncertainty quantification are performed on an experimental structure with three shaker inputs and 27 triaxial accelerometer outputs.

42 ENGINEERING↗
NASA NTRSDate not supplied

Lidar Simulations of Backscatter and Extinction Uncertainty Estimates for ACCP Assessments

As part of the NASA Aerosols and Clouds, Convection, and Precipitation (ACCP) pre-formulation study, several lidar instruments are being assessed for their capabilities in advancing the science objectives enumerated in the most recent Earth Sciences Decadal Survey. These assessments are conducted using the NASA Langley Research Center(LaRC) lidar simulator, which produces profiles of backscatter and extinction uncertainty estimates for a given set of input parameters. Specifically, attention is focused onevaluating the science benefits and improvements of a candidate two-wavelength space-based High Spectral Resolution Lidar (HSRL) compared to an elastic backscatter lidar with similar capabilities to the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP), flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In this talk, simulations of satellite-based HSRL and those of a CALIOP-like system are compared for different atmospheric scenes that capture varying total aerosol loadings, aerosol vertical distributions, and aerosol types.The results of these analyses are presented as functions of averaging resolution(horizontal/vertical), lighting conditions (daytime/nighttime), surface type (land/ocean),and wavelength (532 nm/1064 nm). Also, simulations of multiple aerosol layers and of aerosols beneath transparent cirrus clouds are shown to demonstrate the consequent impacts on underlying backscatter and extinction uncertainties, and to better differentiate the performance of HSRL compared to that of CALIOP for real-world atmospheric scenarios.

Travis D. Toth↗
NASA NTRS2004

Employing Sensitivity Derivatives to Estimate Uncertainty Propagation in CFD

Two methods that exploit the availability of sensitivity derivatives are successfully employed to predict uncertainty propagation through Computational Fluid Dynamics (CFD) code for an inviscid airfoil problem. An approximate statistical second-moment method and a Sensitivity Derivative Enhanced Monte Carlo (SDEMC) method are successfully demonstrated on a two-dimensional problem. First- and second-order sensitivity derivatives of code output with respect to code input are obtained through an efficient incremental iterative approach. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables); these sensitivity derivatives enable one to formulate first- and second-order Taylor Series approximations for the mean and variance of CFD output quantities. Additionally, incorporation of the first-order sensitivity derivatives into the data reduction phase of a conventional Monte Carlo (MC) simulation allows for improved accuracy in determining the first moment of the CFD output. Both methods are compared to results generated using a conventional MC method. The methods that exploit the availability of sensitivity derivatives are found to be valid when considering small deviations from input mean values.

Putko, Michele M.↗
NASA NTRS2013

Computational Fluid Dynamics Uncertainty Analysis Applied to Heat Transfer over a Flat Plate

There have been few discussions on using Computational Fluid Dynamics (CFD) without experimental validation. Pairing experimental data, uncertainty analysis, and analytical predictions provides a comprehensive approach to verification and is the current state of the art. With pressed budgets, collecting experimental data is rare or non-existent. This paper investigates and proposes a method to perform CFD uncertainty analysis only from computational data. The method uses current CFD uncertainty techniques coupled with the Student-T distribution to predict the heat transfer coefficient over a at plate. The inputs to the CFD model are varied from a specified tolerance or bias error and the difference in the results are used to estimate the uncertainty. The variation in each input is ranked from least to greatest to determine the order of importance. The results are compared to heat transfer correlations and conclusions drawn about the feasibility of using CFD without experimental data. The results provide a tactic to analytically estimate the uncertainty in a CFD model when experimental data is unavailable

Computational↗
DOE OSTI2023

Kernel-based global sensitivity analysis obtained from a single data set

Results from global sensitivity analysis (GSA) often guide the understanding of complicated input–output systems. Kernel-based GSA methods have recently been proposed for their capability of treating a broad scope of complex systems. In this paper, we develop a new set of kernel GSA tools when only a single set of input–output data is available. Three key advances are made: (1) A new numerical estimator is proposed that demonstrates an empirical improvement over previous procedures. (2) A computational method for generating inner statistical functions from a single data set is presented. (3) A theoretical extension is made to define conditional sensitivity indices, which reveal the degree that the inputs carry shared information about the output when inherent input–input correlations are present. Utilizing these conditional sensitivity indices, a decomposition is derived for the output uncertainty based on what is called the optimal learning sequence of the input variables, which remains consistent when correlations exist between the input variables. Further, while these advances cover a range of GSA subjects, a common single data set numerical solution is provided by a technique known as the conditional mean embedding of distributions. The new methodology is implemented on benchmark systems to demonstrate the provided insights.

42 ENGINEERING↗
NASA NTRS2007

Blurring the Inputs: A Natural Language Approach to Sensitivity Analysis

To document model parameter uncertainties and to automate sensitivity analyses for numerical simulation codes, a natural-language-based method to specify tolerances has been developed. With this new method, uncertainties are expressed in a natural manner, i.e., as one would on an engineering drawing, namely, 5.25 +/- 0.01. This approach is robust and readily adapted to various application domains because it does not rely on parsing the particular structure of input file formats. Instead, tolerances of a standard format are added to existing fields within an input file. As a demonstration of the power of this simple, natural language approach, a Monte Carlo sensitivity analysis is performed for three disparate simulation codes: fluid dynamics (LAURA), radiation (HARA), and ablation (FIAT). Effort required to harness each code for sensitivity analysis was recorded to demonstrate the generality and flexibility of this new approach.

Kleb, William L.↗
DOE OSTI2020

Excavation Volume Growth Factors at Environmental Remediation Sites - 20318

The U.S. Army Corps of Engineers (USACE) manages numerous environmental remediation projects in accordance with the Comprehensive Environmental Response, Compensation, and Liability Act, otherwise known as CERCLA. These include projects for the Formerly Utilized Sites Remedial Action Program (FUSRAP). Utilizing the CERCLA framework to address these projects requires detailed cost estimates and a significant portion of costs are associated with the volume of material to be excavated and that material's final disposition. Accordingly, understanding the expected volumes and uncertainty associated with those volumes is vital to project cost estimating. These estimates are completed at various stages of the CERCLA process, thus the project team has varying degrees of information available from which to develop these estimates depending on how far along the project is. Understanding the factors contributing to volume growth (increase in volume of material from that originally estimated) is a crucial element to determining volume and cost uncertainty. This paper presents a case study of a FUSRAP site which involves excavation and offsite disposal of contaminated soils from complex commercial, industrial, and residential properties. Original volume estimates were compared to post excavation volumes on a property by property basis. Reasons for differences are determined along with magnitude of impacts to cost estimating. For both residential and commercial property types, volume estimating factors, uncertainty considerations, and methods to account for each are discussed with emphasis on volume growth considerations. Cost estimating model input considerations are discussed with specific emphasis on accounting for volume uncertainty at the Feasibility Study (FS) phase of CERCLA. Underestimating or overestimating costs at the FS phase has repercussions under CERCLA that may impact project close out or completion schedule. Discussions presented will be helpful to both reviewers and preparers of cost estimates involving excavations. Additionally, project planners may be able to use the information to plan for volume growth contingencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Addressing Uncertainty in MultiSector Dynamics Research

MultiSector Dynamics (MSD) research explores the dynamics and co-evolutionary pathways of human and Earth systems and the emerging interdependent sectors of energy, water, agriculture, and transportation among others. The interactions between these sectors are central to MSD science, as they capture how processes and feedbacks across Earth, environmental, infrastructure, and societal systems shape transitions, socioeconomic risks, and the provision of services. By definition, MSD research requires deep integration across diverse scientific disciplines, ranging from the natural to the social sciences and engineering. All these disciplines apply a variety of numerical simulation models to study and understand their underlying systems of focus. The utility of these models hinges on the fidelity with which they represent the real systems and their ability to produce novel insights about systems and their interactions. The coupled human-natural systems typically represented are shaped by a multitude of interdependent human and natural processes which, when modeled, translate to highly complex, non-linear, interacting behaviors. This ever-increasing complexity massively expands the uncertainty space of a model and can be found in model inputs, processes and parameters. This is further amplified when several models are combined to answer multisectoral questions, as additional uncertainty regarding coupling relationships and interactions is introduced.

IM3, Uncertainty and Sensitivity Analysis, multise↗
NASA NTRS1993

Confidence bounds on structural reliability

Different approaches for quantifying physical, statistical, and model uncertainties associated with the distribution parameters which are aimed at determining structural reliability are described. Confidence intervals on the distribution parameters of the input random variables are estimated using four algorithms to evaluate uncertainty of the response. Design intervals are evaluated using either Monte Carlo simulation or an iterative approach. A first order approach can be used to compute a first approximation of the design interval, but its accuracy is not satisfactory. The regression approach which combines the iterative approach with Monte Carlo simulation is capable of providing good results if the performance function can be accurately represented using regression analysis. It is concluded that the design interval-based approach seems to be quite general and takes into account distribution and model uncertainties.

Mehta, S. R.↗
NASA NTRS2018

Comparisons of Two Spatial Implementations of a Crop Model Using Remotely Sensed Observations over Southeastern United States

Global food security is one of the most pressing issues of the current century, particularly for developing nations. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc. However, inaccurate input information, model related errors and the mode of implementation can also add to model uncertainties. In this study, the crop model is implemented in two separate fashions: a)gridded (GriDSSAT model) and b) using random spatial ensembles (RHEAS model). This is done in the Southeastern US to evaluate and understand the modelperformance over a region data availabilities. Once the model performance is assessed, multiple satellite based earth observation parameters such as soil moisture, vegetation index etc. can be assimilated into crop models to reduce input and model related uncertainties particularly in data limited regions. In this study, the National Agricultural Statistical Services (NASS) reported yield data at county levels are used for comparison andvalidation purposes. The GriDSSAT model estimation of corn yields in comparison with the reported NASS yields showed an overall RMSD of nearly 3720 (kg/ha) whereas RMSD for the RHEAS model implementation was 3550 (kg/ha). Overall the GriDSSAT model had negative bias of nearly 2400 kg/ha (except for 2013) while RHEAS had a slight positive bias of 400 kg/ha (approx.).

SERVIR↗
DOE OSTI2023

The Role of Snowmelt Temporal Pattern in Flood Estimation for a Small Snow‐Dominated Basin in the Sierra Nevada

Abstract Prior research confirmed the substantial bias from using precipitation‐based intensity‐duration‐frequency curves (PREC‐IDF) in design flood estimates and proposed next‐generation IDF curves (NG‐IDF) that represent both rainfall and snow processes in runoff generation. This study improves the NG‐IDF technology for a snow‐dominated test basin in the Sierra Nevada. A well‐validated physics‐based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), is used to continuously simulate snowmelt and streamflow that are used as benchmark data sets to systematically assess the NG‐IDF technology. We find that, for the studied small snow‐dominated basin, the use of standard rainfall hyetographs in the NG‐IDF technology leads to substantial underestimation of design floods. Thus, we propose probabilistic hyetographs that can represent unique patterns of events with different underlying mechanisms. For the test basin where flooding events are generated entirely by snowmelt, we develop a hyetograph that characterizes snowmelt temporal patterns, which greatly improves the performance of NG‐IDF technology in design flood estimates. In contrast to the standard rainfall hyetographs characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt hyetograph displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results also show that the uncertainty of hyetography plays an important role in design flood estimation and can have important implications for future flood projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Backlighting extended gas halos around luminous red galaxies: Kinematic Sunyaev-Zel’dovich effect from DESI Y1 and ACT data

The gas density profile around galaxies, shaped by feedback and affecting the galaxy lensing signal, is imprinted on the cosmic microwave background (CMB) by the kinematic Sunyaev-Zel’dovich effect (kSZ). We precisely measure this effect (𝑆/𝑁 ≈ 10) via velocity stacking with 825,283 spectroscopically confirmed luminous red galaxies (LRG) from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) survey, which overlap with the Atacama Cosmology Telescope (ACT) Data Release 6 temperature maps over ≥ 4,000 deg 2 . We explore the kSZ dependence with various galaxy parameters and find no significant trend with redshift but clear trends with stellar mass and absolute magnitude in 𝑔, 𝑟, and 𝑧 bands. Our analysis suggests that the gas extends beyond the dark matter halo (99.5% confidence level, i.e., probability to exceed (PTE)⁢ = 0.005). We find a tentative preference for hydrodynamical simulation models with stronger feedback that drives gas further out (Illustris 𝑧 = 0.5, PTE⁢ = 0.37) over weaker-feedback cases (IllustrisTNG 𝑧 = 0.8, PTE⁢ = 0.045), though with limited statistical significance. In all cases, a free multiplicative amplitude was fit to the simulated profiles, and further modeling work is required to firm up these conclusions. We find consistency between kSZ profiles around spectroscopic and photometric LRG, with comparable statistical power, thus increasing our confidence in the photometric analysis. Additionally, we present the first kSZ measurement around the DESI Y1 bright galaxy sample (BGS) and the emission-line galaxies (ELG) whose features match qualitative expectations. Finally, we forecast 𝑆/𝑁 ∼ 50 for future stacked kSZ measurements using data from the ACT, the DESI Y3, and the Rubin Observatory. Furthermore, these measurements will serve as an input for galaxy formation models and baryonic uncertainties in galaxy lensing.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Including 238 U(n,f)/ 235 U(n,f) and 239 Pu(n,f)/ 235 U(n,f) NIFFTE fission TPC Cross-sections into the Neutron Data Standards Database

The primary purpose of this report is to document how the 238 U/ 235 U and 239 Pu/ 235 U neutron induced fission cross-section ratios, 238 U(n,f)/ 235 U(n,f) and 239 Pu(n,f)/ 235 U(n,f), respectively, measured by the NIFFTE fission Time Projection Chamber (fissionTPC) were included in the most recent database (termed GMA) underlying Neutron Data Standards (NDS) evaluations. This report shows and discusses NDS input files, and underlying assumptions regarding the uncertainty estimate and necessary for including these data. This uncertainty estimate and the resulting files were based on information provided by fissionTPC experimentalists, R.J.Casperson, N.S. Bowden, L. Snyder and K.T. Schmitt for the 238 U ratio, and by L. Snyder for the 239 Pu ratio. The fissionTPC data were included twice, by D. Neudecker and V. Pronyaev, to counter-check results and exclude possible mistakes in their inclusion. It is shown in both evaluations that including fissionTPC 239 Pu(n,f)/ 235 U(n,f) data points to a lower evaluated 239 Pu(n,f) cross section above 10 MeV than the currently released NDS data. This raises the question whether a part of a previous dataset by Tovesson et al., that was previously rejected above 13 MeV for having low values, should be included in the NDS evaluation after all. The evaluated 238 U(n,f) cross section only changes significantly close to the threshold. The impact on the 235 U(n,f) cross section is minimal. fissionTPC data reduce evaluated uncertainties on both observables by 0–12% of the GMA evaluated uncertainties. However, the currently released NDS data contain in addition to these GMA evaluated uncertainties “Unrecognized Sources of Uncertainties” (USU) of 1.2%. It needs to be further discussed within the NDS project, whether the new fissionTPC data should also reduce USU.

238U(n,f)/235U(n,f)↗
DOE OSTI2022

Improving SGP4 Orbit Determination with New State Estimation Algorithm

The Simplified General Perturbations 4 Model (SGP4) is a well-known tool for performing satellite orbit determination. However, uncertainties and inaccuracies in the initial state inputs (required by SGP4) degrade the performance of the propagator. We present a new state estimation algorithm that allows for independent computation of these initial inputs using Unscented Kalman Filtering and GPS data from a satellite. The algorithm is tested on real flight data and demonstrates a notable performance improvement over the standard method of orbit determination using SGP4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluation of Hypotheses for the Fate of Historical Discharges from Hanford Site BC Cribs and Trenches

A preliminary review of declassified reports and historical documents from the 1950s and 1960s indicated they may contain valuable information regarding potential impacts to groundwater from liquid waste discharged to the 216-BC Cribs and Trenches during the late 1950s. Transport of contaminants in the vadose zone beneath the BC Cribs area is simulated by a vadose zone model developed for the Composite Analysis (DOE/RL-2019-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau ). Results from this model indicate there has been no substantial breakthrough of contaminants (particularly technetium-99) to groundwater from the vadose zone up to the present day. However, there is uncertainty in the anisotropy and other model input parameters as well as acknowledged sparseness of sampling data for this area. In this study, historical documents and other information were reviewed to evaluate the possibility that contaminant breakthrough to groundwater occurred during or shortly after operation of the BC Cribs sites, and whether refinements of the vadose zone model parametrization should be considered.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

47 OTHER INSTRUMENTATION↗