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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Conditioning multi-model ensembles for disease forecasting

In this study we investigate how an ensemble of disease models can be conditioned to observational data, in a bid to improve its predictive skill. We use the ensemble of influenza forecasting models gathered by the US Centers for Disease Control and Prevention (CDC) as the exemplar. This ensemble is used every year to forecast the annual influenza outbreak in the United States. The models constituting this ensemble draw on very different modeling assumptions and approximations and are a diverse collection of methods to approximate epidemiological dynamics. Currently, each models' predictions are accorded the same importance, or weight, when compiling the ensemble's forecast. We consider this equally-weighted ensemble as the baseline case which has to be improved upon. In this study, we explore whether an ensemble forecast can be improved by "conditioning" the ensemble to whatever observational data is available from the ongoing outbreak. "Conditioning" can imply according the ensemble's members different weights which evolve over time, or simply perform the forecast using the top k (equally-weighted) models. In the latter case, the composition of the "top-k-see of models evolves over time. This is called "model averaging" in statistics. We explore four methods to perform model-averaging, three of which are new. We find that the CDC ensemble responds best to the "top-k-models" approach to model-averaging. All the new MA methods perform better than the baseline equally-weighted ensemble. The four model-averaging methods treat the models as black-boxes and simply use their forecasts as inputs i.e., one does not need access to the models at all, but rather only their forecasts. The model-averaging approaches reviewed in this report thus form a general framework for model-averaging any model ensemble.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Aerodynamic Optimization of Mach 0.8 Transonic Truss-Braced Wing Aircraft using Variable Camber Continuous Trailing Edge Flap

This paper presents an aerodynamic optimization study of the Mach 0.8 Transonic Truss-Braced Wing (TTBW) aircraft with Variable Camber Continuous Trailing Edge Flap (VC-CTEF). The VCCTEF is a novel wing shaping control concept to improve aircraft aerodynamic efficiency. Drag reduction studies are conducted for two different VCCTEF configurations with 6- and 10-spanwise sections, respectively. A simple VCCTEF actuator weight model is used to account the weight penalty of the actuator in the design for the Mach 0.8 TTBW aircraft. A vortex-lattice model of the Mach 0.8 TTBW aircraft is developed with transonic small disturbance, integral boundary-layer, and wing-strut interference corrections for rapid aerodynamic performance evaluations. The VSPAERO model has been validated against the wind tunnel test data. The optimization results show that the 6-spanwise sections VCCTEF provides a relatively better solution for drag reduction when the actuator weight penalty is considered. A high-fidelity CFD solver FUN3D is used to verify the VCCTEF optimization design.

Aerodynamic optimization↗
DOE OSTI2026

Spectra-to-exposure conversion using polynomial response models for gamma-ray field characterization

Accurate measurement of exposure rate from gamma-ray spectral data remains a critical challenge during radiological emergency response operations. Conventional methods rely on pre-defined static conversion factors derived from fixed geometries and isotopic compositions, which often fail to capture real-world environmental variability. This study presents a generalized approach as a "next-step" for converting gamma-ray spectral data into exposure rate using polynomial response models. The method introduces a flexible weighting scheme based on the in-situ detector response to distributed sources, enabling a pathway towards improved correspondence between measured spectra and "ground-truth" exposure rates. Experimental data from sodium iodide NaI(Tl) detectors were used to validate the approach as, at least equivalent to the current count-to-exposure method employed in emergency response CONOPS. Results show that the polynomial weighting model is sufficiently equal to the count-to-exposure method and may help improve accuracy given its adaptability to real-world conditions.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2012

Design and Performance of Lift-Offset Rotorcraft for Short-Haul Missions

The design and performance of compound helicopters utilizing lift-offset rotors are examined, in the context of short-haul, medium-size civil and military missions. The analysis tools used are the comprehensive analysis CAMRAD II and the sizing code NDARC. Following correlation of the comprehensive analysis with existing lift-offset aircraft flight test data, the rotor performance model for the sizing code was developed, and an initial estimate was made of the rotor size and key hover and cruise flight conditions. The rotor planform and twist were optimized for those conditions, and the sizing code rotor performance model updated. Two models for estimating the blade and hub weight of lift-offset rotors are discussed. The civil and military missions are described, along with the aircraft design assumptions. The aircraft are sized for 30 passengers or 6600 lb payload, with a range of 300 nm. Civil and military aircraft designs are described for each of the rotor weight models. Disk loading and blade loading were varied to optimize the designs, based on gross weight and fuel burn. The influence of technology is shown, in terms of rotor hub drag and rotor weight.

Johnson, Wayne↗
NASA NTRS2012

Adaptive Trajectory Prediction Algorithm for Climbing Flights

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

Seperation Assurance↗
NASA NTRS2020

Ensemble Methodologies for Astronaut Cancer Risk Assessment in the face of Large Uncertainties

A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty (e.g. uncertainty in a radiation quality parameter) as well as model-form uncertainty associated with differing theoretical or empirical formalisms (e.g. combined dose-rate and radiation quality effects). Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future (including international) models, improve uncertainty quantification for underlying sub-models developed against sparse experimental data, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met or the level of knowledge doesn’t permit a specific risk or exposure limit to be developed for future space exploration missions. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible exposure limits and permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets; closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Future work, including the addition of new models and methods for statistical correlation between predictive members are discussed to define alternate ways of thinking about risk and ‘acceptable’ uncertainty with respect to NASA’s current permissible exposure limits.

space radiation↗
NASA NTRSDate not supplied

Ensemble Cancer Risk Model for Astronaut Risk Assessment

A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty (e.g. uncertainty in a radiation quality parameter) as well as model-form uncertainty associated with differing theoretical or empirical formalisms (e.g. combined dose-rate and radiation quality effects). Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future (including international) models, improve uncertainty quantification for underlying sub-models developed against sparse experimental data, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met or the level of knowledge doesn’t permit a specific risk or exposure limit to be developed for future space exploration missions. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible exposure limits and permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets; closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Future work, including the addition of new models and methods for statistical correlation between predictive members are discussed to define alternate ways of thinking about risk and ‘acceptable’ uncertainty with respect to NASA’s current permissible exposure limits.

Lisa C Simonsen↗
DOE OSTI2025

pvcracks: trained VAE model

The resulting model weights for the variational autoencoder for solar cell crack parametrization to be loaded into the python code for other to use

14 SOLAR ENERGY↗
NASA NTRS1992

Free volume model for molecular weights of polymers

A free volume model has been developed for determining molecular weights of linear polymers. It is based on the size of free volume cells in two geometries of poly(arylene ether ketone)s. Free volume cell sizes in test samples were measured using positron lifetime spectroscopy. The molecular weights computed from free volume cell sizes are in good agreement with the values measured by gel permeation chromatography, with a low angle laser light scattering photometer as the detector. The model has been further tested on two atactic polystyrene samples, where it predicted the ratio of their molecular weights with reasonable accuracy.

Singh, J. J.↗
NASA NTRS2019

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design and performance under the Revolutionary Vertical Lift Technology (RVLT) Project. These models were developed using open data sources, assuming current and advanced technology levels, and range from 650 to 7,500 shaft output horsepower (485 to 5,600 kW). Documenting the methodology, assumptions, and resulting performance realizes important benefits NASA and the aviation community. NASA concept vehicle efforts using these propulsion models can be more readily shared among the government, industry and university community as common baselines to support current and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various modeling conceptual vehicle and mission analysis environments advanced, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To perform this effort, the turboshaft engine is briefly discussed, highlighting the specific components and their expected performance characteristics over the power range and technology levels considered. Engine configurations will also be discussed as they will vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight will be reported, noting trends that are important for system studies. The effect of advanced propulsion technologies on RVLT concept vehicles are also reported. Finally, potential future propulsion modeling work will be proposed.

Snyder, Christopher A.↗
NASA NTRS2019

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design in support of the National Aeronautics and Space Administration's (NASA) Aeronautics Mission Research Directorate's (ARMD) Revolutionary Vertical Lift Technology (RVLT) Project. These models were developed using open data sources, assuming current and advanced technology levels, and range from 650 to 7,500 shaft output horsepower (485 to 5,600 kilowatts). Documenting the methodology, assumptions, and resulting performance realizes important benefits for NASA and the aviation community. NASA concept vehicle efforts using these propulsion models can more readily shared among the government, industry and university community as common baselines to support current and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various modeling conceptual vehicle and mission analysis environments advance, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To perform this effort, the turboshaft engine is briefly discussed, highlighting the specific components and their expected performance characteristics over the power range and technology levels considered. Engine configurations will also be discussed as they will vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight will be reported, noting trends that are important for system studies. The effect of advanced propulsion technologies on RVLT-concept vehicles are also reported. Finally, potential future propulsion modeling work will be proposed.

Snyder, Christopher A.↗
DOE OSTI2023

Application of multi-method-multi-model inference to radiation related solid cancer excess risks models for astronaut risk assessment

The impact of including model-averaged excess radiation risks (ER) into a measure of radiation attributed decrease of survival (RADS) for the outcome all solid cancer incidence and the impact on the uncertainties is demonstrated. It is shown that RADS applying weighted model averaged ER based on AIC weights result in smaller risk estimates with narrower 95% CI than RADS using ER based on BIC weights. Further a multi-method-multi-model inference approach is introduced that allows calculating one general RADS estimate providing a weighted average risk estimate for a lunar and a Mars mission. For males the general RADS estimate is found to be 0.42% (95% CI: 0.38%; 0.45%) and for females 0.67% (95% CI: 0.59%; 0.75%) for a lunar mission and 2.45% (95% CI: 2.23%; 2.67%) for males and 3.91% (95% CI: 3.44%; 4.39%) for females for a Mars mission considering an age at exposure of 40 years and an attained age of 65 years. It is recommended to include these types of uncertainties and to include model-averaged excess risks in astronaut risk assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2023

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project, Expanded

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design and performance under the Revolutionary Vertical Lift Technology (RVLT) Project in 2019 by Snyder in Reference 7. These models were developed using open data sources, assuming present and future technology levels, and range from 650 to 5,000 output shaft horsepower (485 to 3,730 kW). This paper expands on the previous research, extending the power ranges from 200 to 15000 output shaft horsepower (150 to 11,200 kW) and documenting the methodology, assumptions, and engine performance realizes important benefits for NASA and the aviation community. NASA concept-vehicle study efforts using these baseline propulsion models can be more readily shared among the government, industry, and university community to support present and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various conceptual vehicle and mission analysis simulations are developed, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To expand on the previous effort, the turboshaft engine is briefly discussed, highlighting the specific components, and expected performance characteristics over the updated power range and technology levels considered. Additional engine configurations will also be discussed as they vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight are updated, noting important trends for system studies. Finally, the effect of advanced propulsion technologies on public reference models including RVLT concept vehicles are reported along with the tools and software methods used to complete the analysis.

VTOL↗
DOE OSTI2015

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Experimental and Numerical Study of Coating Thickness Using Multi-slot Air Knives

Gas-jet wiping is a widely employed production technology for controlling the final zinc coating thickness on a moving substrate during continuous hot-dip galvanizing. This paper presents an experimental investigation and numerical analysis of a prototype multi-slot air knife, which offers an increase in wiping efficiency relative to the traditional single-slot jet geometry in the continuous galvanizing process. The applicability of the analytical coating weight model of Elsaadawy et al. (Metall Mater Trans B, 38:413-424, 2007) to predict the final coating weight was determined for the multi-slot geometry, where particular focus was devoted to the effect of geometric parameters. Experimental measurements under a variety of knife geometry and process conditions agreed with the coating weight predictions of the analytical model. It was also shown that the air-knife geometric parameters had a significant effect on the pressure profile and shear stress distribution applied by the air knives to the moving substrate. It was determined that the final coating thickness was significantly affected by the auxiliary jet width, D{sub a}, where lighter coating weights at higher strip velocities (up to 5.4 pct at V{sub s} = 1.5 m/s) could be achieved by using the multi-slot air-knives prototype vs. the conventional single-slot configuration.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing

This paper proposes a comprehensive framework for accelerating population balance-Monte Carlo (PBMC) simulation of particle coagulation dynamics. By combining Markov jump model, weighted majorant kernel and GPU (graphics processing unit) parallel computing, a significant gain in computational efficiency is achieved. The Markov jump model constructs a coagulation-rule matrix of differentially-weighted simulation particles, so as to capture the time evolution of particle size distribution with low statistical noise over the full size range and as far as possible to reduce the number of time loopings. Here three coagulation rules are highlighted and it is found that constructing appropriate coagulation rule provides a route to attain the compromise between accuracy and cost of PBMC methods. Further, in order to avoid double looping over all simulation particles when considering the two-particle events (typically, particle coagulation), the weighted majorant kernel is introduced to estimate the maximum coagulation rates being used for acceptance–rejection processes by single-looping over all particles, and meanwhile the mean time-step of coagulation event is estimated by summing the coagulation kernels of rejected and accepted particle pairs. The computational load of these fast differentially-weighted PBMC simulations (based on the Markov jump model) is reduced greatly to be proportional to the number of simulation particles in a zero-dimensional system (single cell). Finally, for a spatially inhomogeneous multi-dimensional (multi-cell) simulation, the proposed fast PBMC is performed in each cell, and multiple cells are parallel processed by multi-cores on a GPU that can implement the massively threaded data-parallel tasks to obtain remarkable speedup ratio (comparing with CPU computation, the speedup ratio of GPU parallel computing is as high as 200 in a case of 100 cells with 10 000 simulation particles per cell). These accelerating approaches of PBMC are demonstrated in a physically realistic Brownian coagulation case. The computational accuracy is validated with benchmark solution of discrete-sectional method. The simulation results show that the comprehensive approach can attain very favorable improvement in cost without sacrificing computational accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

Alluvial aquifers by nature are complex caused by varied depositional environments. Developing a reliable groundwater model to represent an alluvial aquifer is non-trivial. Also, relying on a single best calibrated model may not be sufficient because of an inadequate choice of model parameter values. To better understand groundwater dynamics and improve model prediction reliability, this study presents a Bayesian multi-model uncertainty quantification (BMMUQ) framework to account for model parameter uncertainty in complex alluvial groundwater modeling. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer (MRAA), Northeast Louisiana. An aquifer architecture was first constructed using 7,259 well logs in the MRAA area which covers three fluvial deposits (alluvium, braided-stream terrace, and braided-stream terrace-loess). A 12-layer MODFLOW model was then developed to address the alluvial aquifer complexity and well calibrated through a genetic algorithm. This study quantified model parameter uncertainty in hydraulic conductivity and specific storage of sand facies. Bayesian model averaging (BMA) with the Expectation Maximization (EM) algorithm was adopted to derive posterior model weights and head variances of 50 alternative conceptual groundwater flow models, and thereby obtains BMA ensemble model predictions instead of only relying on the best calibrated conceptual model. Overall, results show that an estimated around 950 million m3 of groundwater storage loss occurs in 2015 with respect to the beginning of 2004, due to high groundwater demand for irrigation in the MRAA area. Explicitly quantifying model uncertainty can produce more reliable groundwater level predictions from BMA ensemble model. The presented groundwater modeling framework improves our understanding of the MRAA and provides a valuable tool to assist agricultural water management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Modeling Turboshaft Engines for the Revolutionary Vertical Lift Technology Project, Expanded

Turboshaft engine performance and weight models were developed to support conceptual propulsion and vehicle mission design and performance under the Revolutionary Vertical Lift Technology (RVLT) Project in 2019 by Snyder in Ref. 1. These models were developed using open data sources, assuming present and future technology levels, and range from 650 to 5,000 output shaft horsepower (485 to 3,730 kW). This paper expands on the previous research, extending the power ranges from 200 to 15000 output shaft horsepower (150 to 11,200 kW) and documenting the methodology, assumptions, and engine performance realizes important benefits for NASA and the aviation community. NASA concept vehicle study efforts using these baseline propulsion models can be more readily shared among the government, industry, and university community to support present and future work. Assessing the benefits of advanced technologies and new configurations can be facilitated using these models, which helps guide technology investment. As the various modeling conceptual vehicle and mission analysis environments advance, these models can be used directly for broader systems analysis studies, including optimization within the propulsion model itself. To expand on the previous effort, the turboshaft engine is briefly discussed, highlighting the specific components, and expected performance characteristics over the updated power range and technology levels considered. Additional engine configurations will also be discussed as they vary based on power output and assumed technology level. Engine performance, such as airflow, power output and weight will be updated, noting trends that are important for system studies. Finally, the effect of advanced propulsion technologies on RVLT concept vehicles are also reported along with the tools and software methods used to complete the analysis.

VTOL↗