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NASA NTRS2024

Intrastep, Stage-Value Predictors for Diagonally-Implicit Runge-Kutta Methods

To better identify the necessary attributes of good stage-value predictors (SVPs), numerous SVPs are designed for an existing: ESDIRK4(3)7L[2]SA [26] and a new: ESDIRK4(3)8L[2]SA scheme.1 Both are stifflyaccurate, stage-order two, explicit, singly-diagonally implicit Runge–Kutta (ESDIRK) schemes. Tradeoffs are studied in the parameter spaces enforcing the constraints on accuracy, linear stability, nonlinear stability and coefficient size to determine which objectives correlate with effective predictors. The SVPs are tested on three challenging external aerodynamics problems [107 − 108 degrees of freedom (DoFs)], each with a different level of stiffness. The problems include two 3D airfoils simulations and one canonical turbulence simulation. All simulations use the compressible Navier-Stokes equations (CNSE). An entropy stable spectral collocation formulation is used for discretizing the spatial terms in the equations. Simulations are performed at a wide variety of temporal error tolerances. Problems that are sufficiently stiff (e.g., lax temporal error tolerances) benefit from SVPs designed with second-order accuracy and stability properties: A-stability, and L-stability, rather than high accuracy constraints. Simulations with modest stiffness (e.g., strict error tolerances) are better suited for SVPs designed using high accuracy constraints. Designing SVPs with enhanced stability properties is tedious but worthwhile. Simulation times are reduced with optimal SVPs by as much as 100% on some stages, with combined stepwise improvements of between 50 − 100% for both methods. A comparative study is performed with the two aforementioned methods as well as four other ESDIRKs. The newly designed ESDIRK4(3)8L[2]SA with γ ≈ 1/10, proves to be the most efficient of the six tested ESDIRK schemes simulating the CNSE.

Diagonally-Implicit Runge-Kutta↗

DOE OSTI2019

Technical and Imaging Outcomes from the UK Registry of Prostate Artery Embolization (UK-ROPE) Study: Focusing on Predictors of Clinical Success

Introduction: The UK Registry of Prostate Artery Embolization (UK-ROPE) was a prospective, multicentre study comparing PAE against surgical therapies for symptomatic benign prostatic hyperplasia (BPH). A wealth of data was collected supplementary to the main study outcomes which provide a snapshot of UK PAE practice. We aimed to interpret these data in the hope of providing insight into factors which affect clinical outcome and radiation dose. Methods: 216 patients (mean age 66, mean IPSS 21.3) undergoing PAE at 20 British centres from July 2014 to January 2016 were prospectively followed up to 12 months with retrospective analysis of the data. Technical outcome was evaluated based on procedural and fluoroscopy times, skin dose and dose area product (DAP). Clinical outcome was evaluated through collection of Qmax, IPSS reduction and prostate volume reduction. Multiple analysis of variance (MANOVA) was used to assess the significance of various patients and procedural factors on clinical outcome and patient dose. Results: Significant predictors of technical outcome which affected patient skin dose included severity of CTA-detected atheroma (p < 0.001), the practitioner (p < 0.001) and use of protective coil embolization (p = 0.019). Predictors of clinical outcome included initial prostate size (dichotomized into groups > 80 ml and = <80 ml, d = 1, p = 0.0138), embolic agent (spherical particles < 300 nm performed best, p = 0.01) and number of arteries embolized (IPSS reduction of 32.9% in unilateral PAE versus 54.4% for bilateral PAE, p = 0.026). Conclusion: We have identified several important factors which are associated with improved clinical outcome and increased patient dose which we hope will facilitate optimal patient selection and encourage improved embolization technique.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Sub-cloud moist entropy curvature as a predictor for changes in the seasonal cycle of tropical precipitation

Convective Quasi-Equilibrium (CQE) may be a useful framework for understanding the precipitation minus evaporation (P-E) response to CO2-induced warming. To explore this proposition, a suite of aquaplanet simulations with a slab ocean from TRACMIP (the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project) is analyzed. A linear relationship between P-E and the curvature of sub-cloud moist entropy, a marker for the spatial distribution of sub-cloud moist energy and onset of a tropical direct overturning circulation under CQE conditions, is shown to exist across many of the TRACMIP simulations. Furthermore, this linear relationship is a skillful predictor of changes in P-E in response to CO2-induced warming. The curvature metric also shows improvement in predicting P-E changes compared to the simpler method of relating P-E directly to the sub-cloud moist entropy field or a simple ‘wet-get-wetter’ type null hypothesis, especially on seasonal and shorter timescales. Using fixed relative humidity in the curvature metric and sub-cloud moist entropy degrades their ability to predict P-E changes, implying that both temperature and relative humidity changes in the boundary layer are important for characterizing future precipitation changes. To understand why the curvature metric is a skillful predictor of hydrological changes, a moist static energy (MSE) budget analysis is performed, which shows that MSE divergence on sub-daily timescales is well parameterized as a downgradient diffusive process. Additionally, this transient MSE divergence has a similar structure to the curvature term. Assuming that the transient MSE divergence is related to convective activity, these findings suggest that the theoretical underpinning of the linear relationship between the curvature term and P-E resides in cloud processes that both remove spatial gradients of sub-cloud moist entropy and generate precipitation at sub-seasonal and shorter timescales.

aquaplanet simulations↗

DOE OSTI2026

Personal and environmental predictors of polycyclic aromatic hydrocarbon exposure identified through repeated silicone wristband sampling

This study integrates quantitative data on personal exposure to polycyclic aromatic hydrocarbons (PAHs) in 162 silicone wristbands with demographics, behavioral information, and housing characteristics to explore contributions to residential exposure in a superfund-adjacent community over the course of a year. Forty-six residents completed questionnaires and wore silicone wristbands as personal passive samplers for seven consecutive days on up to four separate occasions in alternating months between November 2022 and June 2023. It was hypothesized that individual behaviors and housing characteristics are sources of dependence and correlation between personal PAH exposures. 50 PAHs were detected at least once, 17 of which were alkylated PAHs. Exposure to PAHs of similar molecular weight was often correlated, notably between naphthalenes (2-rings) and higher molecular weight PAHs (3 or more rings). Generalized linear mixed models identified flooring type, participant age, and sampling month as important predictors of increased PAH exposure, and flooring type, and use of wood stoves or heavy machinery as predictors of increased naphthalene exposure relative to higher molecular weight PAHs. Individual chemical models based on concentration data and detection frequencies corroborated these findings across multiple PAHs. We demonstrate that personal exposure is not static and the degree of variability in personal exposure is individual. Hence, identification of influential exposure factors through repeated measures of chemical exposure and characterization of variability in personal exposure as performed in this study, is important in the development of exposure mitigation strategies.

Bonner, Emily↗

DOE OSTI2017

Temporal Evolution and Dose-Volume Histogram Predictors of Visual Acuity After Proton Beam Radiation Therapy of Uveal Melanoma

Purpose: To perform an in-depth temporal analysis of visual acuity (VA) outcomes after proton beam radiation therapy (PBRT) in a large, uniformly treated cohort of uveal melanoma (UM) patients, to determine trends in VA evolution depending on pretreatment and temporally defined posttreatment VA measurements; and to investigate the relevance of specific patient, tumor and dose-volume parameters to posttreatment vision loss. Methods and Materials: Uveal melanoma patients receiving PBRT were identified from a prospectively maintained database. Included patients (n=645) received 56 GyE in 4 fractions, had pretreatment best corrected VA (BCVA) in the affected eye of count fingers (CF) or better, with posttreatment VA assessment at specified post-PBRT time point(s). Patients were grouped according to the pretreatment BCVA into favorable (≥20/40) or unfavorable (20/50-20/400) and poor (CF) strata. Temporal analysis of BCVA changes was described, and univariate and forward stepwise multivariate logistic regression analyses were performed to identify predictors for VA loss. Results: Median VA follow-up was 53 months (range, 3-213 months). At 60-month follow up, among evaluable treated eyes with favorable pretreatment BCVA, 45% retained BCVA ≥20/40, whereas among evaluable treated eyes with initially unfavorable/poor BCVA, 21% had vision ≥20/100. Among those with a favorable initial BCVA, attaining BCVA of ≥20/40 at any posttreatment time point was associated with subsequent maintenance of excellent BCVA. Multivariate analysis identified volume of the macula receiving 28GyE (P<.0001) and optic nerve (P=.0004) as independent dose-volume histogram predictors of 48-month post-PBRT vision loss among initially favorable treated eyes. Conclusions: Approximately half of PBRT-treated UM eyes with excellent pretreatment BCVA assessed at 5 years after treatment will retain excellent long-term vision. 28GyE macula and optic nerve dose-volume histogram parameters allow for rational treatment planning optimization that may lead to improved visual outcomes. The detailed temporal analysis with intermediate as well as long-term functional prognosis, and the relationship of outcomes with clinical and treatment planning parameters, is critical for informed care of UM patients before and after PBRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Hierarchical-embedding autoencoder with a predictor as efficient architecture for learning time-evolution in multi-scale turbulent flows

We introduce a scale-aware, data-driven deep learning modeling framework for accurately predicting the time evolution of multi-scale turbulent plasma and liquid flows. The approach is motivated by the idea of scale separation. Structures of vastly different length scales emerge in these systems, and interactions between these structures occur only locally. To exploit this structure, the flow state is transformed by a hierarchical, fully convolutional autoencoder, not into a single embedding layer as in conventional convolutional surrogate models, but into a series of embedding layers. A stepwise training strategy ensures that fine-scale features are encoded on a high-resolution grid, while larger structures are represented on progressively coarser layers. The time evolution predictor advances all embedding layers in sync, capturing local interactions between features at the same scale as well as between all scales. This approach enables efficient modeling of multi-scale systems since negligible interactions between distant, small-scale structures do not need to be directly modeled. Our hierarchical-embedding autoencoder with a predictor framework is evaluated on canonical examples of multi-scale turbulence: two-dimensional Kolmogorov flow and Hasegawa–Wakatani plasma turbulence. In both cases, the proposed framework significantly improves predictive accuracy relative to conventional convolutional network architectures. A significant improvement in prediction accuracy was observed for crucial statistical characteristics of the Hasegawa–Wakatani plasma as well as for individual trajectories of the Kolmogorov flow turbulence. Importantly, the model's rollout for the Hasegawa–Wakatani problem demonstrates a four-order-of-magnitude speedup compared to traditional numerical solvers.

Khrabry, Alexander I. [Princeton Univ., NJ (United↗

DOE OSTI2020

The ASHRAE Great Energy Predictor III competition: Overview and results

In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. Furthermore, the competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

What is Important? Morphological Asymmetries are Useful Predictors of Star Formation Rates of Star-forming Galaxies in SDSS Stripe 82

The morphology and structure of galaxies reflect their star formation and assembly histories. We use the framework of mutual information (MI) to quantify the interdependence among several structural variables and to rank them according to their relevance for predicting the specific star formation rate (SSFR) by comparing the MI of the predictor variables with the SSFR and penalizing variables that are redundant. We apply this framework to study ~3700 face-on star-forming galaxies (SFGs) with varying degrees of bulge dominance and central concentration and with stellar mass $M$ * ≈ 10 9 $M$ ⊙ at redshift $z$ = 0.02–0.12. We use the Sloan Digital Sky Survey (SDSS) Stripe 82 deep $i$-band imaging data, which improve measurements of asymmetry and bulge dominance indicators. We find that star-forming galaxies are a multiparameter family. In addition to $M$ * , asymmetry emerges as the most powerful predictor of SSFR residuals of SFGs, followed by bulge prominence/concentration. Star-forming galaxies with higher asymmetry and stronger bulges have higher SSFR at a given $M$ * . In conclusion, the asymmetry reflects both irregular spiral arms and lopsidedness in seemingly isolated SFGs and structural perturbations by galaxy interactions or mergers.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1978

Analysis of a VTOL hover task with predictor displays using an optimal control model of the human operator

The influence of different types of predictor displays in a longitudinal VTOL hover task is analyzed in a theoretical study. It was assumed that pitch angle and position are presented to the pilot in separate displays namely the artificial horizon and position display. The predictive information is calculated by means of a Taylor series. From earlier experimental studies it is well known that predictor displays improve human and system performance and result in reducing human workload. In this study, an optimal control model is used to prove this effect theoretically. Several cases with differing amounts of predictive and rate information are compared.

Johannsen, G.↗

NASA NTRS1979

An analysis of the human as a predictor model

A hybrid sampled data-continuous impulse response of a preview tracker using a fast time model predictor aid is derived. The model can accept transient and nontransient imputs and is suitable for studying a human preview tracker using an internalized or externalized predictor display. The model is shown to behave reasonably in a simple example and its implications for modeling studies are discussed.

Kreifeldt, J. G.↗

NASA NTRS1986

An estimator-predictor approach to PLL loop filter design

An approach to the design of digital phase locked loops (DPLLs), using estimation theory concepts in the selection of a loop filter, is presented. The key concept is that the DPLL closed-loop transfer function is decomposed into an estimator and a predictor. The estimator provides recursive estimates of phase, frequency, and higher order derivatives, while the predictor compensates for the transport lag inherent in the loop. This decomposition results in a straightforward loop filter design procedure, enabling use of techniques from optimal and sub-optimal estimation theory. A design example for a particular choice of estimator is presented, followed by analysis of the associated bandwidth, gain margin, and steady state errors caused by unmodeled dynamics. This approach is under consideration for the design of the Deep Space Network (DSN) Advanced Receiver Carrier DPLL.

Statman, J. I.↗

NASA NTRSDate not supplied

Satellite orbit predictor

An analog aid to determine satellite coverage of Emergency Locator Transmitters Emergency Position Indicating Radio Beacon (ELT/EPIRB) distress incidence is discussed. The satellite orbit predictor is a graphical aid for determining the relationship between the satellite orbit, antenna coverage of the spacecraft and coverage of the Local User Terminal. The predictor allows the user to quickly visualize if a selected position will probably be detected and is composed of a base map and a satellite track overlay for each satellite.A table of equator crossings for each satellite is included.

Friedman, Morton l.↗

NASA NTRS1984

COSPAS-SARSAT Satellite Orbit Predictor. Volume 3

The satellite orbit predictor is a graphical aid for determining the relationship between the satellite (SARSAT or COSPAS) orbit, antenna coverage of the spacecraft and coverage of the LUTs. The predictor allows the user to quickly visualize if a selected position will probably be detected and is composed of a base map and a satellite track overlay for each satellite. Additionally, a table of equator crossings for each satellite is included.

Friedman, Morton L.↗

NASA NTRS1984

COSPAS-SARSAT Satellite Orbit Predictor, Vol 4

Friedman, Morton L.↗

NASA NTRS1983

Software development predictors, error analysis, reliability models and software metric analysis

The use of dynamic characteristics as predictors for software development was studied. It was found that there are some significant factors that could be useful as predictors. From a study on software errors and complexity, it was shown that meaningful results can be obtained which allow insight into software traits and the environment in which it is developed. Reliability models were studied. The research included the field of program testing because the validity of some reliability models depends on the answers to some unanswered questions about testing. In studying software metrics, data collected from seven software engineering laboratory (FORTRAN) projects were examined and three effort reporting accuracy checks were applied to demonstrate the need to validate a data base. Results are discussed.

Basili, Victor↗

NASA NTRS1987

COSPAS-SARSAT Satellite Orbit Predictor Volume 10

The satellite orbit predictor is a graphical aid for determining the relationship between the satellite (SARSAT or COSPAS) orbit, antenna coverage of the spacecraft and coverage of the Local User Terminals. The predictor allows the user to quickly visualize if a selected position will be detected and is composed of a base map and satellite track overlay for each satellite. A table of equator crossings for each satellite is included.

Friedman, Morton L.↗

NASA NTRS1988

Digital phase-lock loop having an estimator and predictor of error

A digital phase-lock loop (DPLL) which generates a signal with a phase that approximates the phase of a received signal with a linear estimator. The effect of a complication associated with non-zero transport delays related to DPLL mechanization is then compensated by a predictor. The estimator provides recursive estimates of phase, frequency, and higher order derivatives, while the predictor compensates for transport lag inherent in the loop.

Statman, Joseph I.↗

NASA NTRS1988

A fourth generation reliability predictor

A reliability/availability predictor computer program has been developed and is currently being beta-tested by over 30 US companies. The computer program is called the Hybrid Automated Reliability Predictor (HARP). HARP was developed to fill an important gap in reliability assessment capabilities. This gap was manifested through the use of its third-generation cousin, the Computer-Aided Reliability Estimation (CARE III) program, over a six-year development period and an additional three-year period during which CARE III has been in the public domain. The accumulated experience of the over 30 establishments now using CARE III was used in the development of the HARP program.

Bavuso, Salvatore J.↗