Bounds and self-consistent estimates for elastic constants of polycrystals of hcp solid He 4
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Berryman, James G.↗
SEARCH · Engineering Papers
Results for “estimation”
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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Inverter Reliability Estimation for Advanced Inverter Functionality
In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous modes, which will produce new stresses on the grid-interfacing power electronics components, such as DC/AC inverters. In previous work, multiple DER devices were instrumented to evaluate additional component stress under multiple reactive power setpoints. We utilize quasi-static time-series simulations to determine voltage-reactive power mode (volt-var) mission profile of inverters in an active power system. Mission profiles and loss estimates are then combined to estimate the reduction of the useful life of inverters from different reactive power profiles. It was found that the average lifetime reduction was approximately 0.15% for an inverter between standard unity power factor operation and the IEEE 1547 default volt-var curve based on thermal damage due to switching in the power transistors. For an inverter with an expected 20-year lifetime, the 1547 volt-var curve would reduce the expected life of the device by 12 days. This framework for determining an inverter's useful life from experimental and modeling data can be applied to any failure mechanism and advanced inverter operation.
component degradation↗
Addressing Angular Single-Event Effects in the Estimation of On-Orbit Error Rates
Our study describes complications introduced by angular direct ionization events on space error rate predictions. In particular, prevalence of multiple-cell upsets and a breakdown in the application of effective linear energy transfer in modern-scale devices can skew error rates approximated from currently available estimation models. Moreover, this paper highlights the importance of angular testing and proposes a methodology to extend existing error estimation tools to properly consider angular strikes in modern-scale devices. Finally, these techniques are illustrated with test data provided from a modern 28 nm SRAM-based device.
42 ENGINEERING↗
SU-E-T-364: Estimating the Minimum Number of Patients Required to Estimate the Required Planning Target Volume Margins for Prostate Glands
Purpose: To establish a minimum number of patients required to obtain statistically accurate Planning Target Volume (PTV) margins for prostate Intensity Modulated Radiation Therapy (IMRT). Methods: A total of 320 prostate patients, consisting of a total number of 9311 daily setups, were analyzed. These patients had gone under IMRT treatments. Daily localization was done using the skin marks and the proper shifts were determined by the CBCT to match the prostate gland. The Van Herk formalism is used to obtain the margins using the systematic and random setup variations. The total patient population was divided into different grouping sizes varying from 1 group of 320 patients to 64 groups of 5 patients. Each grouping was used to determine the average PTV margin and its associated standard deviation. Results: Analyzing all 320 patients lead to an average Superior-Inferior margin of 1.15 cm. The grouping with 10 patients per group (32 groups) resulted to an average PTV margin between 0.6–1.7 cm with the mean value of 1.09 cm and a standard deviation (STD) of 0.30 cm. As the number of patients in groups increases the mean value of average margin between groups tends to converge to the true average PTV of 1.15 cm and STD decreases. For groups of 20, 64, and 160 patients a Superior-Inferior margin of 1.12, 1.14, and 1.16 cm with STD of 0.22, 0.11, and 0.01 cm were found, respectively. Similar tendency was observed for Left-Right and Anterior-Posterior margins. Conclusion: The estimation of the required margin for PTV strongly depends on the number of patients studied. According to this study at least ∼60 patients are needed to calculate a statistically acceptable PTV margin for a criterion of STD < 0.1 cm. Numbers greater than ∼60 patients do little to increase the accuracy of the PTV margin estimation.
60 APPLIED LIFE SCIENCES↗
A computational fluid dynamics model to estimate local quantities in firebrand char oxidation
Firebrand burning is a complex phenomenon that is influenced by several parameters which are difficult to fully explore experimentally. Computational fluid dynamics models capable of predicting local quantities are essential for accurate prediction of char oxidation in firebrands. This article presents a computational fluid dynamics model to estimate firebrand mass loss, diameter change, and surface temperature during char oxidation. The model was validated using previously conducted wind tunnel experiments. These experiments were conducted for firebrands of two different aspect ratios, which were arranged in three different configurations (single, horizontal array, and vertical array), and for four different wind speeds (0.5, 1, 1.5, and 2 m/s). The computational fluid dynamics results were compared with a previous 1 D model. In all the test cases, the computational fluid dynamics model predicted the physical phenomena with significantly improved accuracy compared to a 1 D model. The char oxidation model presented in this article can be coupled with other models to study firebrand generation and trajectory, biomass pyrolysis, fluidized bed reactors, and coal combustion.
Engineering↗
Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing
Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.
AI/ML↗
A Robust Mass Estimator for Dark Matter Subhalo Perturbations in Strong Gravitational Lenses
A few dark matter substructures have recently been detected in strong gravitational lenses through their perturbations of highly magnified images. We derive a characteristic scale for lensing perturbations and show that they are significantly larger than the perturber’s Einstein radius. We show that the perturber’s projected mass enclosed within this radius, scaled by the log-slope of the host galaxy’s density profile, can be robustly inferred even if the inferred density profile and tidal radius of the perturber are biased. We demonstrate the validity of our analytic derivation using several gravitational lens simulations where the tidal radii and the inner log-slopes of the density profile of the perturbing subhalo are allowed to vary. By modeling these simulated data, we find that our mass estimator, which we call the effective subhalo lensing mass, is accurate to within about 10% or smaller in each case, whereas the inferred total subhalo mass can potentially be biased by nearly an order of magnitude. We therefore recommend that the effective subhalo lensing mass be reported in future lensing reconstructions, as this will allow for a more accurate comparison with the results of dark matter simulations.
79 ASTRONOMY AND ASTROPHYSICS↗
On-Road Validation of a Simplified Model for Estimating Real-World Fuel Economy
On-road fuel economy is known to vary significantly between individual trips in real-world driving conditions. This work introduces a methodology for rapidly simulating a specific vehicle's fuel economy over the wide range of real-world conditions experienced across the country. On-road test data collected using a highly instrumented vehicle is used to refine and validate this modeling approach. Here, model accuracy relative to on-road data collection is relevant to the estimation of 'off-cycle credits' that compensate for real-world fuel economy benefits that are not observed during certification testing on a chassis dynamometer.
33 ADVANCED PROPULSION SYSTEMS↗
Estimation of the Fuel Efficiency Potential of Six Gasoline Blendstocks Identified by the U.S. Department of Energy’s Co-Optimization of Fuels and Engines Program
Six blendstocks identified by the Co-Optimization of Fuels & Engines Program were used to prepare fuel blends using a fixed blendstock for oxygenate blending and a target RON of 97. The blendstocks included ethanol, n-propanol, isopropanol, isobutanol, diisobutylene, and a bioreformate surrogate. The blends were analyzed and used to establish interaction factors for a non-linear molar blending model that was used to predict RON and MON of volumetric blends of the blendstocks up to 35 vol%. Projections of efficiency increase, volumetric fuel economy increase, and tailpipe CO2 emissions decrease were produced using two different estimation techniques to evaluate the potential benefits of the blendstocks. Ethanol was projected to provide the greatest benefits in efficiency and tailpipe CO2 emissions, but at intermediate levels of volumetric fuel economy increase over a smaller range of blends than other blendstocks. A bioreformate surrogate blendstock was projected to provide the greatest increase in volumetric fuel economy and the lowest increase in efficiency. Tailpipe CO2 emissions for blends of the bioreformate surrogate were higher at all blend levels compared to the baseline E10 fuel.
02 PETROLEUM↗
Sequential estimation of observation error variances in a trajectory estimation problem.
Sequential trajectory estimation improved by implementing simple running estimates of observation error variances
TRAJECTORY ANALYSIS↗
Sequential estimation of observation error variances in a trajectory estimation problem.
Sequential trajectory estimation improved by implementing simple running estimates of observation error variances
BAYESIAN STATISTICS↗
Reliability estimation procedures and CARE: The Computer-Aided Reliability Estimation Program
Ultrareliable fault-tolerant onboard digital systems for spacecraft intended for long mission life exploration of the outer planets are under development. The design of systems involving self-repair and fault-tolerance leads to the companion problem of quantifying and evaluating the survival probability of the system for the mission under consideration and the constraints imposed upon the system. Methods have been developed to (1) model self-repair and fault-tolerant organizations; (2) compute survival probability, mean life, and many other reliability predictive functions with respect to various systems and mission parameters; (3) perform sensitivity analysis of the system with respect to mission parameters; and (4) quantitatively compare competitive fault-tolerant systems. Various measures of comparison are offered. To automate the procedures of reliability mathematical modeling and evaluation, the CARE (computer-aided reliability estimation) program was developed. CARE is an interactive program residing on the UNIVAC 1108 system, which makes the above calculations and facilitates report preparation by providing output in tabular form, graphical 2-dimensional plots, and 3-dimensional projections. The reliability estimation of fault-tolerant organization by means of the CARE program is described.
Mathur, F. P.↗
Space Station Program (Modular) Cost Estimates Document. Volume II. Subsystem Estimates
Subsystem estimates for the modular space station development contain the cost and schedule data as well as appendices with task descriptions and cost estimate data forms.
Source record↗
Cost estimating methodology and techniques for preparing industrial-engineering type manhour and material-based cost estimates
Various methods of cost estimating are described. The method and benefits are presented of industrial engineering type manhour and material based cost estimates.
Stewart, R. D.↗
Digital combining-weight estimation for broadband sources using maximum-likelihood estimates
An algorithm described for estimating the optimum combining weights for the Ka-band (33.7-GHz) array feed compensation system is compared with the maximum-likelihood estimate. This provides some improvement in performance, with an increase in computational complexity. However, the maximum-likelihood algorithm is simple enough to allow implementation on a PC-based combining system.
Rodemich, E. R.↗
A High-Speed, Real-Time Visualization and State Estimation Platform for Monitoring and Control of Electric Distribution Systems: Implementation and Field Results
Continued deployment of renewable and distributed energy resources is fundamentally changing the way that electric distribution systems are controlled and operated; more sophisticated active system control and greater situational awareness are needed. Real-time measurements and distribution system state estimation (DSSE) techniques enable more sophisticated system control and, when combined with visualization applications, greater situational awareness. This paper presents a novel demonstration of a high-speed, real-time DSSE platform and related control and visualization functionalities, implemented using existing open-source software and distribution system monitoring hardware. Live scrolling strip charts of meter data and intuitive annotated map visualizations of the entire state (obtained via DSSE) of a real-world distribution circuit are shown. The DSSE implementation is validated to demonstrate provision of accurate voltage data. This platform allows for enhanced control and situational awareness using only a minimum quantity of distribution system measurement units and modest data and software infrastructure.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement
This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional weighted inner product space. We demonstrate the benefit of working on a weighted space by establishing operator-norm bounds for finite element and graph-based discretizations of Matérn-type priors and deconvolution forward models. For linear-Gaussian inverse problems, we develop a general theory to characterize the error in the approximation to the posterior. We also embed the computational framework into ensemble Kalman methods and MAP estimators for nonlinear inverse problems. Furthermore, our operator-norm bounds for prior discretizations guarantee the scalability and accuracy of these algorithms under mesh refinement.
Bayesian inverse problem↗