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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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All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2018

Phase Transitions in Edge-Weighted Exponential Random Graphs: Near-Degeneracy and Universality

Conventionally used exponential random graphs cannot directly model weighted networks as the underlying probability space consists of simple graphs only. Since many substantively important networks are weighted, this limitation is especially problematic. We extend the existing exponential framework by proposing a generic common distribution for the edge weights. Minimal assumptions are placed on the distribution, that is, it is non-degenerate and supported on the unit interval. By doing so, we recognize the essential properties associated with near-degeneracy and universality in edge-weighted exponential random graphs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Skill and independence weighting for multi-model assessments

We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.

58 GEOSCIENCES↗
DOE OSTI2016

Optimizing the Costs of Solid Sorbent-Based CO 2 Capture Process Through Heat Integration

The focus of this project was the ADAsorb™ CO 2 Capture Process, a temperature-swing adsorption process that incorporates a three-stage fluidized bed as the adsorber and a single-stage fluidized bed as the regenerator. ADAsorb™ system was designed, fabricated, and tested under DOE award DEFE0004343. Two amine-based sorbents were evaluated in conjunction with the ADAsorb™ process: “BN”, an ion-exchange resin; and “OJ”, a metal organic framework (MOF) sorbent. Two cross heat exchanger designs were evaluated for use between the adsorber and regenerator: moving bed and fluidized bed. The fluidized bed approach was rejected fairly early in the project because the additional electrical load to power blowers or fans to overcome the pressure drop required for fluidization was estimated to be nominally three times the electrical power that could be generated from the steam saved through the use of the cross heat exchanger. The Energy Research Center at Lehigh University built and utilized a process model of the ADAsorb™ capture process and integrated this model into an existing model of a supercritical PC power plant. The Lehigh models verified that, for the ADAsorb™ system, the largest contributor to parasitic power was lost electrical generation, which was primarily electric power which the host plant could not generate due to the extraction of low pressure (LP) steam for sorbent heating, followed by power for the CO 2 compressor and the blower or fan power required to fluidize the adsorber and regenerator. Sorbent characteristics such as the impacts of moisture uptake, optimized adsorption and regeneration temperature, and sensitivity to changes in pressure were also included in the modeling study. Results indicate that sorbents which adsorb more than 1-2% moisture by weight are unlikely to be cost competitive unless they have an extremely high CO 2 working capacity that well exceeds 15% by weight. Modeling also revealed that reductions in adsorber pressure drop could negatively affect the CO 2 adsorption characteristics for sorbents with certain isobar adsorption characteristics like sorbent BN. Thus, reductions in pressure drop do not provide the efficiency benefits expected. A techno-economic assessment conducted during the project revealed that without heat integration, the a metal organic framework (MOF) sorbent used in conjunction with the ADAsorb™ process provided the opportunity for improved performance over the benchmark MEA process. While the addition of a cross heat exchanger and heat integration was found to significantly improve net unit heat rate, the additional equipment costs required to realize these improvements almost always outweighed the improvement in performance. The exception to this was for a supported amine sorbent and the addition of a moving bed cross heat exchanger alone or in conjunction with waste heat from the compressor used for supplemental regenerator heating. Perhaps one of the most important points to be drawn from the work conducted during this project is the significant influence of sorbent characteristics alone on the projected COE and LCOE associated with the ADAsorb™ process, and the implications associated with future improvements to solid sorbent CO 2 capture. The results from this project suggest that solid sorbent CO 2 capture will continue to see performance gains and lower system costs as further sorbent improvements are realized.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A particle-conserved weighted-average oscillator model for trapped particles in long-time nonlinear Landau damping

A normalized weighting function model for the average period of particles trapped in the potential wells of waves undergoing nonlinear Landau damping is considered herein. The model preserves the conservation properties of the trapped particles and relates them to the evolution of the wave envelope. It is valid when there are more shallowly trapped particles than deeply trapped particles, such as in long-time nonlinear Landau damping, as well as stabilities due to trapped particles at long times. The model is also useful for estimating the validity of the often invoked assumption that most trapped particles in nonlinear waves are deeply trapped.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27509-DND

The information about composition of aerosols has a great importance for monitoring and understanding of climate and environment dynamics. Such information can be obtained using situ measurements or chemical transport models. However, in situ sampling has limited spatial and temporal coverage, while estimations have large uncertainties. The present work enables the monitoring of aerosol chemical species from space-borne observations, providing observationally-based results with spatial and temporal coverage. Following the ideas of Schuster et al.(2005, 2009, 2016), we retrieve chemical composition directly from satellite observations without intermediate retrieval of the refractive index (in contrast with Schuster's approach). This approach is expected to reduce the influence of modeling uncertainties, and to provide additional constraints in situations where remote sensing observations do not have enough spectral sensitivity to refractive index. One of principal difficulties is the identification of an adequate model for linking refractive index to chemical composition. Therefore, the initial effort of this work has focused on identifying an optimal physico-chemistry to refractive index conversion model. With that purpose, we first tested the retrieval approach using a simplified volume-weighting model and updated by the Maxwell Garnett mixing model. This concept was incorporated into the GRASP algorithm designed to retrieve an extended set of atmospheric parameters from remote sensing observations. Then a series of sensitivity tests using synthetic data of POLDER/PARASOL were conducted, and followed by inversion of real PARASOL observations over Africa. The sensitivity tests showed that these two models allow the retrieval to distinguish amongst the assumed chemical species. Results obtained from real PARASOL data demonstrated good agreement with the optical characteristics provided by AERONET. In addition, the obtained patterns of chemical component distribution agreed with known physical expectations. We also compared the chemical composition retrievals to GOCART model simulations and obtained good consistencies.

Lei Li↗
DOE OSTI2021

Evaluation of the Large-Scale and Regional Climatic Response Across North Africa to Natural Variability in Oceanic Modes and Terrestrial Vegetation Among the CMIP5 Models

Hydrologic variability is a serious threat to the poverty-stricken regions of North Africa. Meanwhile, the scientific community struggles to attribute these extreme climatic episodes to specific oceanic and land drivers. Prior modeling studies have not assessed simulated feedbacks over North Africa against an established observational benchmark, so their results are considered as untested, model-specific findings. The Coupled Model Intercomparison Project Phase Five (CMIP5) archive represents the state-of-the-art in climate projections, with most CMIP5 models now containing interactive vegetation phenology; however, there have been no studies to date of their simulated vegetation feedbacks. The capability of CMIP5 models at accurately simulating the forcing of both sea-surface temperature (SST) and regional leaf area index (LAI) anomalies on North African climate needs to be a key consideration in assessing the models’ overall credibility and determining appropriate model weighting for developing climate projections. The Generalized Equilibrium Feedback Assessment (GEFA) is a promising statistical method, which can be applied to either model output or observations, for isolating the local and remote impacts of individual oceanic or terrestrial forcings on regional climate. We performed a combined observational and modeling assessment of land-ocean-atmosphere interactions across the distinct ecological and moisture gradients of North Africa. We evaluated and demonstrated the reliability of the GEFA statistical method over North Africa using the Community Earth System Model (CESM), applied GEFA to observational data to quantify the observed forcing of ocean basin SST anomalies and regional LAI anomalies on North African climate, evaluated the CMIP5 models’ performance in terms of representing these key observed feedbacks, and formulated CMIP5 feedback performance metrics for weighting North African climate projections. Our study represented the first attempt to separate the observed roles of oceanic and vegetation feedbacks across North Africa, the first systematic assessment and intercomparison of land-ocean-atmosphere feedbacks in CMIP5, and the first exploration of vegetation feedbacks among CMIP5 models. The following work was accomplished. (1) The team generated 14 scientific publications and 30 presentations based on the DOE-funded research. (2) A stepwise version of the GEFA statistical method was developed in which unimportant forcings were dropped to increase the reliability of the results. (3) SGEFA was successfully validated through experiments with CESM in terms of its ability to isolate the atmospheric responses to individual oceanic or land forcings. (4) Observational evidence was revealed for the Sahel’s positive vegetation-rainfall feedback on the seasonal to interannual time scale, and it was attributed to a moisture recycling mechanism rather than an albedo mechanism. (5) An approach with SGEFA was developed in which the individual contributions of soil moisture versus vegetation forcings could be separated, revealing that the former forcing outweighed the latter for sub-Saharan Africa. (6) Tropical ocean temperatures were found to be key regulators of pan-tropical vegetation variability, especially for arid and semi-arid regions, including sub-Saharan Africa. (7) The CMIP5 models largely underestimated the importance of land feedbacks across the Sahel. (8) The general consensus among CMIP5 models indicates, for the late 21st century, a diminished seasonal predictability of sub-Saharan African regional climate and an elevated role of the land surface compared to oceanic drivers in regulating regional climate variability. (9) Land-ocean-atmosphere interactions were demonstrated to be key contributors to the seasonal predictability of African wildfire activity. (10) The El Djouf was determined to be of greater importance than the Bodélé depression in terms of providing trans-Atlantic dust transport to the Americas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multimodel ensemble predictions of precipitation using bayesian neural networks

Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models. BNN infers spatiotemporally varying model weights and biases through the calibration against observations. This ensemble scheme of BNN sufficiently leverages individual model skill for accurate predictions as well as provides interpretability about which models contribute more to the ensemble prediction at which locations and times to inform model development. Additionally, BNN accurately quantifies epistemic uncertainty to avoid overconfident projections. We demonstrate BNN’s superior prediction performance to three state-of-the-art ensemble approaches and discuss its interpretability and uncertainty quantification.

Fan, Patrick↗
DOE OSTI2022

Multimodel Ensemble Predictions of Precipitation using Bayesian Neural Networks

Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models. BNN infers spatiotemporally varying model weights and biases through the calibration against observations. This ensemble scheme of BNN sufficiently leverages individual model skill for accurate predictions as well as provides interpretability about which models contribute more to the ensemble prediction at which locations and times to inform model development. Additionally, BNN accurately quantifies epistemic uncertainty to avoid overconfident projections. We demonstrate BNN’s superior prediction performance to three state-of-the-art ensemble approaches and discuss its interpretability and uncertainty quantification.

Fan, Patrick↗
DOE OSTI2015

The optically unbiased GRB host (TOUGH) survey. VII. The host galaxy luminosity function: Probing the relationship between GRBs and star formation to redshift ∼6

Gamma-ray bursts (GRBs) offer a route to characterizing star-forming galaxies and quantifying high-z star formation that is distinct from the approach of traditional galaxy surveys: GRB selection is independent of dust and probes even the faintest galaxies which can evade detection in flux-limited surveys. However, the exact relation between the GRB rate and the star formation rate (SFR) throughout all redshifts is controversial. The Optically Unbiased GRB Host (TOUGH) survey includes observations of all GRB hosts (69) in an optically unbiased sample of Swift GRBs; we utilize these to constrain the evolution of the UV GRB-host-galaxy luminosity function (LF) between z = 0 and z = 4.5, and compare this with LFs derived from both Lyman-break galaxy (LBG) surveys and simulation modeling. At all redshifts we find the GRB hosts to be most consistent with an LF derived from SFR weighted models incorporating GRB production via both metallicity-dependent and independent channels with a relatively high level of bias toward low metallicity hosts. In the range 1<z<3 an SFR weighted LBG derived (i.e., non-metallicity biased) LF is also a reasonable fit to the data. Between z ∼ 3 and z ∼ 6, we observe an apparent lack of UV bright hosts in comparison with LBGs, though the significance of this shortfall is limited by nine hosts of unknown redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

Noise Control in Propeller-Driven Aircraft

Analytical model predicts noise levels inside propeller-driven aircraft during cruise at mach 0.8. Double wall sidewalls minimize interior noise and weight. Model applied to three aircraft with fuselages of different size (wide-body, narrow-body, and small-diameter) to determine noise reductions required to achieve A-weighted sound level not to exceed 80 dB.

Rennison, D. C.↗
DOE OSTI2019

Inferring Atmospheric Release Characteristics in a Large Computer Experiment using Bayesian Adaptive Splines

An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of the release (location, amount and duration) and meteorological conditions is an active research area highly relevant for emergency and long-term response to these releases. As a result, researchers have developed particle dispersion simulators to provide plume path predictions that incorporate release characteristics and meteorological conditions. However, since release characteristics and meteorological conditions are often unknown, the inverse problem is of great interest, that is, based on all the observations of the plume so far, what can be inferred about the release characteristics? This is the question we seek to answer using plume observations from a controlled release at the Diablo Canyon Nuclear Power Plant in Central California. With access to a large number of evaluations of a computationally expensive particle dispersion simulator that includes continuous and categorical inputs and spatio-temporal output, building a fast statistical surrogate model (or emulator) presents many statistical challenges, but is an essential tool for inverse modeling and sensitivity analysis. We achieve accurate emulation using Bayesian adaptive splines to model weights on empirical orthogonal functions. Here, we use this emulator as well as appropriately identifiable simulator discrepancy and observational error models to calibrate the simulator, thus finding a posterior distribution of characteristics of the release. Since the release was controlled, these characteristics are known, making it possible to compare our findings to the truth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Segmentation Model Distillation [Poster]

The process of training object detection (OD) or image segmentation model requires both a substantial amount of data and technical knowledge, which often creates challenges in applying these types of models to their full potential. In order to streamline the process of developing these models, we propose a new pipeline where a foundation model assists in the dataset generation. Then this resulting dataset is used to fine-tune a fast light-weight model to perform the custom segmentation or OD. This resulting model is also fit for real-time image segmentation, such as in a video stream.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

The dust coma of Comet P/Giacobini-Zinner in the infrared

The paper presents 1-20-micron photometry of P/Giacobini-Zinner obtained at the NASA Infrared Telescope Facility, during 1985 June-September (r = 1.57-1.03 AU). A broad, weak 10-micron silicate emission feature was detected on August 26.6; a similar weak emission feature could have been hidden in the broadband photometry on other dates. The total scattering and emitting cross section of dust in the inner coma was similar to that in other short-period comets, but a factor of 10 (r = 1.56 AU) to 100 (r = 1.03 AU) lower than the amount of dust in Comet Halley. The thermal emission continuum can be fit with models weighted toward either small or large absorbing grains. The dust production rate near perihelion was about 100,000 g/s (small-grain model) to about 1,000,000 g/s (large-grain model). The corresponding dust/gas mass ratio on August 26 was about 0.1-1. A silicate-rich heterogeneous grain model with an excess of large particles is compatible with the observed spectrum of Giacobini-Zinner on August 26. Thus, weak or absent silicate emission does not necessarily imply an absence of silicates in the dust, although the abundance of silicate particles not greater than 1 micron radius must have been lower than in Comet Halley.

Hanner, M. S.↗
DOE OSTI2015

Propensity-Weighted Comparison of Long-Term Risk of Urinary Adverse Events in Elderly Women Treated For Cervical Cancer

Purpose: Cervical cancer treatment is associated with a risk of urinary adverse events (UAEs) such as ureteral stricture and vesicovaginal fistula. We sought to measure the long-term UAE risk after surgery and radiation therapy (RT), with confounding controlled through propensity-weighted models. Methods and Materials: From the Surveillance, Epidemiology, and End Results (SEER)-Medicare database, we identified women ≥66 years old with nonmetastatic cervical cancer treated with simple surgery (SS), radical hysterectomy (RH), external beam RT plus brachytherapy (EBRT+BT), or RT+surgery. We matched them to noncancer controls 1:3. Differences in demographic and cancer characteristics were balanced by propensity weighting. Grade 3 to 4 UAEs were identified by diagnosis codes plus treatment codes. Cumulative incidence was measured using Kaplan-Meier methods. The hazard associated with different cancer treatments was compared using Cox models. Results: UAEs occurred in 272 of 1808 cases (17%) and 222 of 5424 (4%) controls; most (62%) were ureteral strictures. The raw cumulative incidence of UAEs was highest in advanced cancers. UAEs occurred in 31% of patients after EBRT+BT, 25% of patients after RT+surgery, and 15% of patients after RH; however, after propensity weighting, the incidence was similar. In adjusted Cox models (reference = controls), the UAE risk was highest after RT+surgery (hazard ratio [HR], 5.07; 95% confidence interval [CI], 2.32-11.07), followed by EBRT+BT (HR, 3.33; 95% CI, 1.45-7.65), RH (HR, 3.65; 95% CI, 1.41-9.46) and SS (HR, 0.99; 95% CI, 0.32-3.01). The higher risk after RT+surgery versus EBRT+BT was statistically significant, whereas, EBRT+BT and RH were not significantly different from each other. Conclusions: UAEs are common after cervical cancer treatment, particularly in patients with advanced cancers. UAEs are more common after RT, but these women tend to have the advanced cancers. After propensity weighting, the risk after RT was similar to that after surgery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2015

Visual Attention Allocation Between Robotic Arm and Environmental Process Control: Validating the STOM Task Switching Model

Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version. The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An un-weighted model based on attributes of difficulty, interest and salience accounted for 96 percent of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task.

STOM attention allocation model validation↗
DOE OSTI2026

The Double-edged Sword of Data-driven Super-Resolution: Adversarial Super-resolution Models

Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. However, these SR models introduce a previously unexplored attack surface into imaging pipelines. In this paper, we present AdvSR, a framework demonstrating that adversarial behavior can be embedded directly into SR model weights during training, requiring no access to inputs at inference time. Unlike prior attacks that perturb inputs or rely on backdoor triggers, AdvSR operates entirely at the model level. By jointly optimizing for reconstruction quality and targeted adversarial outcomes, AdvSR produces models that appear benign under standard image quality metrics while inducing downstream misclassification. We evaluate AdvSR on three SR architectures (SRCNN, EDSR, SwinIR) paired with a YOLOv11 classifier and demonstrate that AdvSR models can achieve high attack success rates with minimal quality degradation. These findings highlight a new model-level threat for imaging pipelines, with implications for how practitioners source and validate models in safety-critical applications.

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗
DOE OSTI2017

Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR

Abstract The purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes or model selection as opposed to simple model averaging. The three driest climate regions (snow, polar, and arid) exhibit high correlations and low RMSE values when compared against PERSIANN-CDR estimates, with the exceptions of the cold regions showing an inability to capture the 95th and 99th percentile annual total precipitation characteristics. A comprehensive assessment of each model’s performance in each continent–climate zone defined group is provided as a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.

Nguyen, Phu↗