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DOE OSTI2015

Induced seismicity in geothermal reservoirs: A review of forecasting approaches

In order to reach Europe's 2020 and 2050 targets in terms of greenhouse gas emissions, geothermal resources will have to contribute substantially to meeting carbon-free energy needs. However, public opinion may prevent future large-scale application of deep geothermal power plants, because induced seismicity is often perceived as an unsolicited and uncontrollable side effect of geothermal development. In the last decade, significant advances were made in the development of models to forecast induced seismicity, which are either based on catalogues of induced seismicity, on the underlying physical processes, or on a hybrid philosophy. In this paper, we provide a comprehensive overview of the existing approaches applied to geothermal contexts. This overview will outline the advantages and drawbacks of the different approaches, identify the gaps in our understanding, and describe the needs for geothermal observations. Most of the forecasting approaches focus on the stimulation phase of enhanced geothermal systems which are most prone to generate seismic events. Besides the statistical models suited for real-time applications during reservoir stimulation, the physics-based models have the advantage of considering sub-surface characteristics and estimating the impact of fluid circulation on the reservoir. Thus, to mitigate induced seismicity during major hydraulic stimulations, application of hybrid methods in a decision support system seems the best available solution. So far, however, little attention has been paid to geochemical effects on the failure process and to production periods. Quantitative modelling of induced seismicity still is a challenging and complex matter. Appropriate resources remain to be invested for the scientific community to continue its research and development efforts to successfully forecast induced seismicity in geothermal fields. This is a prerequisite for making this renewable energy resource sustainable and accessible worldwide.

58 GEOSCIENCES↗

NASA NTRS2014

Revised (Mixed-Effects) Estimation for Forest Burning Emissions of Gases and Smoke, Fire/Emission Factor Typology, and Potential Remote Sensing Classification of Types for Ozone and Black-Carbon Simulation

We summarize recent progress (a) in correcting biomass burning emissions factors deduced from airborne sampling of forest fire plumes, (b) in understanding the variability in reactivity of the fresh plumes sampled in ARCTAS (2008), DC3 (2012), and SEAC4RS (2013) airborne missions, and (c) in a consequent search for remotely sensed quantities that help classify forest-fire plumes. Particle properties, chemical speciation, and smoke radiative properties are related and mutually informative, as pictures below suggest (slopes of lines of same color are similar). (a) Mixed-effects (random-effects) statistical modeling provides estimates of both emission factors and a reasonable description of carbon-burned simultaneously. Different fire plumes will have very different contributions to volatile organic carbon reactivity; this may help explain differences of free NOx(both gas- and particle-phase), and also of ozone production, that have been noted for forest-fire plumes in California. Our evaluations check or correct emission factors based on sequential measurements (e.g., the Normalized Ratio Enhancement and similar methods). We stress the dangers of methods relying on emission-ratios to CO. (b) This work confirms and extends many reports of great situational variability in emissions factors. VOCs vary in OH reactivity and NOx-binding. Reasons for variability are not only fuel composition, fuel condition, etc., but are confused somewhat by rapid transformation and mixing of emissions. We use "unmixing" (distinct from mixed-effects) statistics and compare briefly to approaches like neural nets. We focus on one particularly intense fire the notorious Yosemite Rim Fire of 2013. In some samples, NOx activity was not so suppressed by binding into nitrates as in other fires. While our fire-typing is evolving and subject to debate, the carbon-burned delta(CO2+CO) estimates that arise from mixed effects models, free of confusion by background-CO2 variation, should provide a solid base for discussion. (c) We report progress using promising links we find between emissions-related "fire types" and promising features deducible from remote observations of plumes, e.g., single scatter albedo, Angstrom exponent of scattering, Angstrom exponent of absorption, (CO column density)/(aerosol optical depth).

Remote sensing↗

DOE OSTI2018

Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate

Cyclonic atmospheric vortices of varying intensity, collectively known as low-pressure systems (LPS), travel northwest across central India and produce more than half of the precipitation received by that fertile region and its ~600 million inhabitants. Yet, future changes in LPS activity are poorly understood, due in part to inadequate representation of these storms in current climate models. Using a high-resolution atmospheric general circulation model that realistically simulates the genesis distribution of LPS, here we show that Indian monsoon LPS activity declines about 45% by the late 21st century in simulations of a business-as-usual emission scenario. The distribution of LPS genesis shifts poleward as it weakens, with oceanic genesis decreasing by ~60% and continental genesis increasing by ~10%; over land the increase in storm counts is accompanied by a shift toward lower storm wind speeds. The weakening and poleward shift of the genesis distribution in a warmer climate are confirmed and attributed, via a statistical model, to the reduction and poleward shift of low-level absolute vorticity over the monsoon region, which in turn are robust features of most coupled model projections. The poleward shift in LPS activity results in an increased frequency of extreme precipitation events over northern India.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

Finite Element Analysis of Reverberation Chambers

The primary motivating factor behind the initiation of this work was to provide a deterministic means of establishing the validity of the statistical methods that are recommended for the determination of fields that interact in -an avionics system. The application of finite element analysis to reverberation chambers is the initial step required to establish a reasonable course of inquiry in this particularly data-intensive study. The use of computational electromagnetics provides a high degree of control of the "experimental" parameters that can be utilized in a simulation of reverberating structures. As the work evolved there were four primary focus areas they are: 1. The eigenvalue problem for the source free problem. 2. The development of a complex efficient eigensolver. 3. The application of a source for the TE and TM fields for statistical characterization. 4. The examination of shielding effectiveness in a reverberating environment. One early purpose of this work was to establish the utility of finite element techniques in the development of an extended low frequency statistical model for reverberation phenomena. By employing finite element techniques, structures of arbitrary complexity can be analyzed due to the use of triangular shape functions in the spatial discretization. The effects of both frequency stirring and mechanical stirring are presented. It is suggested that for the low frequency operation the typical tuner size is inadequate to provide a sufficiently random field and that frequency stirring should be used. The results of the finite element analysis of the reverberation chamber illustrate io-W the potential utility of a 2D representation for enhancing the basic statistical characteristics of the chamber when operating in a low frequency regime. The basic field statistics are verified for frequency stirring over a wide range of frequencies. Mechanical stirring is shown to provide an effective frequency deviation.

Bunting, Charles F.↗

NASA NTRS1991

Clutter modeling of the Denver Airport and surrounding areas

To accurately simulate and evaluate an airborne Doppler radar as a wind shear detection and avoidance sensor, the ground clutter surrounding a typical airport must be quantified. To do this, an imaging airborne Synthetic Aperture Radar (SAR) was employed to investigate and map the normalized radar cross sections (NRCS) of the ground terrain surrounding the Denver Stapleton Airport during November of 1988. Images of the Stapleton ground clutter scene were obtained at a variety of aspect and elevation angles (extending to near-grazing) at both HH and VV polarizations. Presented here, in viewgraph form with commentary, are the method of data collection, the specific observations obtained of the Denver area, a summary of the quantitative analysis performed on the SAR images to date, and the statistical modeling of several of the more interesting stationary targets in the SAR database. Additionally, the accompanying moving target database, containing NRCS and velocity information, is described.

Harrah, Steven D.↗

DOE OSTI2024

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Abstract Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo- z ) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo- z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of “true” redshift posteriors that can be used for photo- z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo- z estimation. We discuss how PZFlow will be used to validate the photo- z estimation pipeline of the Dark Energy Science Collaboration, and the wider applicability of PZFlow for statistical modeling of any tabular data.

Astronomy & Astrophysics↗

DOE OSTI2013

Exploratory Nuclear Reactor Safety Analysis and Visualization via Integrated Topological and Geometric Techniques

A recent trend in the nuclear power engineering field is the implementation of heavily computational and time consuming algorithms and codes for both design and safety analysis. In particular, the new generation of system analysis codes aim to embrace several phenomena such as thermo-hydraulic, structural behavior, and system dynamics, as well as uncertainty quantification and sensitivity analyses. The use of dynamic probabilistic risk assessment (PRA) methodologies allows a systematic approach to uncertainty quantification. Dynamic methodologies in PRA account for possible coupling between triggered or stochastic events through explicit consideration of the time element in system evolution, often through the use of dynamic system models (simulators). They are usually needed when the system has more than one failure mode, control loops, and/or hardware/process/software/human interaction. Dynamic methodologies are also capable of modeling the consequences of epistemic and aleatory uncertainties. The Monte-Carlo (MC) and the Dynamic Event Tree (DET) approaches belong to this new class of dynamic PRA methodologies. The major challenges in using MC and DET methodologies (as well as other dynamic methodologies) are the heavier computational and memory requirements compared to the classical ET analysis. This is due to the fact that each branch generated can contain time evolutions of a large number of variables (about 50,000 data channels are typically present in RELAP) and a large number of scenarios can be generated from a single initiating event (possibly on the order of hundreds or even thousands). Such large amounts of information are usually very difficult to organize in order to identify the main trends in scenario evolutions and the main risk contributors for each initiating event. This report aims to improve Dynamic PRA methodologies by tackling the two challenges mentioned above using: 1) adaptive sampling techniques to reduce computational cost of the analysis and 2) topology-based methodologies to interactively visualize multidimensional data and extract risk-informed insights. Regarding item 1) we employ learning algorithms that aim to infer/predict simulation outcome and decide the coordinate in the input space of the next sample that maximize the amount of information that can be gained from it. Such methodologies can be used to both explore and exploit the input space. The later one is especially used for safety analysis scopes to focus samples along the limit surface, i.e. the boundaries in the input space between system failure and system success. Regarding item 2) we present a software tool that is designed to analyze multi-dimensional data. We model a large-scale nuclear simulation dataset as a high-dimensional scalar function defined over a discrete sample of the domain. First, we provide structural analysis of such a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. Our analysis is performed by exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1975

Magnetospheric mapping with a quantitative geomagnetic field model

Mapping the magnetosphere on a dipole geomagnetic field model by projecting field and particle observations onto the model is described. High-latitude field lines are traced between the earth's surface and their intersection with either the equatorial plane or a cross section of the geomagnetic tail, and data from low-altitude orbiting satellites are projected along field lines to the outer magnetosphere. This procedure is analyzed, and the resultant mappings are illustrated. Extension of field lines into the geomagnetic tail and low-altitude determination of the polar cap and cusp are presented. It is noted that while there is good agreement among the various data, more particle measurements are necessary to clear up statistical uncertainties and to facilitate comparison of statistical models.

Fairfield, D. H.↗

NASA NTRS2014

Exact and Approximate Probabilistic Symbolic Execution

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Symbolic Execution↗

NASA NTRSDate not supplied

Landslide Runout Analysis Using Remote Sensing for Lower and Upper Barun Lakes

Landslides are recognized as common triggers of glacial lake outburst floods in high mountain regions, but the remoteness of dangerous glacial lakes makes monitoring and disaster management very difficult. Using a very high resolution digital elevation model produced from a series of stereo satellite images, we simulate debris flows and rock avalanche runout around two very dangerous lakes in high mountain Nepal. We constrain particularly likely sources of these slope failures to simulate 79,800 potential debris flows and rock avalanches of volumes ranging from 1,000 m3 to 1,000,000 m3. This allowed us to identify zones in and around dangerous glacial lakes that are most exposed to landslide runout hazards, along with the most frequent source areas that produced landslides that may run into either of the two glacial lakes. Although our empirical-statistical model presented limitations that numerical models attempt to overcome, our probabilistic approach combined with a very high resolution DEM may shed new light on how to identify sources and impact areas of debris flows and rock avalanches that may cascade into downstream disasters.

landslides↗

NASA NTRS2013

Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

aerosol optical depth↗

NASA NTRS2025

Innovative Drug Selection, Storage, and Shelf-Life Strategies for Exploration Spaceflight

Medications have been a part of space travel dating back to the Apollo missions. A safe and effective medication formulary is essential to maintaining crew health and performance during long-duration spaceflight outside of low Earth orbit (LEO). Distance from Earth creates four key operational changes that increase medical risks, including communication, resupply, crewmember health, and evacuation. The current spaceflight pharmaceutical formulary consists of medications indicated to treat a variety of anticipated medical events and healthcare needs during spaceflight, but depends on a robust consumables resupply chain, which may be strained for a Lunar, and possibly non-existent for a Mars mission. The specific medications selections for the formulary may change to optimally align with the mission, crew compliment, and spacecraft design. Medical support at long-duration exploration missions will differ from LEO missions due to mission duration, lack of consumables resupply, prolonged exposure to space radiation, and the absence of emergency medical return capability. Loss of medication resupply limits or removes the ability to replace medications that have been exhausted or degraded, potentially exacerbating the medical risk posture. To address these anticipated risks, long-duration missions must consider use of novel medical technologies, treatment modalities, and smart medical systems that offer greater crew autonomy, such as physiologically based pharmacokinetic modeling, drug repurposing, on demand drug synthesis, or wearable drug delivery / monitoring devices. Once an ideal formulary for exploration space is determined, it is essential to establish the chemical and physical stability of each medication compound, as well as its safety by identifying its degradation profiles and products. Although few studies have been conducted to provide evidence on the physicochemical stability of pharmaceuticals during space missions, the data suggests that the spaceflight environment may promote degradation in some pharmaceuticals. Formulary drug purity and efficacy should be verified by pharmaceutical stability assessments, and can be realized non-destructively, and accessed in remote environments. Non-destructive pharmaceutical analysis and statistical modelling techniques could optimize exploration spaceflight medical care by enabling early detection of suboptimal therapeutics. Likewise, novel packaging, storage strategies, and dosage form innovations are promising countermeasures to optimize pharmaceutical shelf life, purity, and quality of exploration spaceflight medications. As we prepare for more distant exploration missions, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. Following extensive review of the literature, we will present innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.

Vernie R Daniels↗

NASA NTRS1993

Cosmic-ray modulation, merged interaction regions, and multifractals

The large-scale heliospheric magnetic field fluctuations and their effects on cosmic rays are discussed. Voyager 2 observations from 1983.0 to 1989.6 are used as input to the model of Perko and Burlaga (1992) to show that the few largest merged interaction regions (MIRs) provide essential details in the cosmic-ray intensity (CRI) profile. It is demonstrated that the largest MIRs determine the basic structure of the radial gradients of the CRI. It is shown that the magnetic field strength during the period 1983.0-1989.6 does have a multifractal structure, but that there are different multifractal structures in the three intervals 1983.0-1985.0, 1985.0-1987.5, and 1987.5-1989.6. The multifractal structure can be described by simple functions, the multifractal spectra. These analytical descriptions can be used to generate realizations of the heliospheric magnetic field with the same statistical properties as those observed by Voyager 2. Thus, the starting point is provided for realistic global statistical models of the 11-yr variation of the Galactic CRI.

Burlaga, L. F.↗

NASA NTRS1996

Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field intensity, and mean geomagnetic dipole power excursion and axial dipole reversal frequencies. We conclude that McLeod's Rule helps unify geo-paleomagnetism, correctly relates theoretically predictable statistical properties of the core geodynamo to magnetic observation, and provides a priori information required for stochastic inversion of paleo-, archeo-, and/or historical geomagnetic measurements.

Voorhies, Coerte V.↗

DOE OSTI2021

Learning-based approach to plasticity in athermal sheared amorphous packings: Improving softness

The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between the plastic dynamics and the structure of a particle’s local environment remains an open question. Previously, machine learning was used to identify a structural predictor of rearrangements called “softness.” Although softness has been shown to predict which particles will rearrange with high accuracy, the method can be difficult to implement in experiments where data are limited and the combinations of descriptors it identifies are often difficult to interpret physically. Here, we address both of these weaknesses, presenting two major improvements to the standard softness method. First, we present a natural representation of each particle’s observed mobility, allowing for the use of statistical models that are both simpler and provide greater accuracy in limited datasets. Second, we employ persistent homology as a systematic means of identifying simple, topologically informed, structural quantities that are easy to interpret and measure experimentally. We test our methods on two-dimensional athermal packings of soft spheres under quasi-static shear. We find that the same structural information that predicts small variations in the response is also predictive of where plastic events will localize. We also find that an excellent accuracy is achieved in athermal sheared packings using simply a particle’s species and the number of nearest neighbor contacts.

36 MATERIALS SCIENCE↗

NASA NTRS1997

On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

Simpson, Timothy W.↗

DOE OSTI2017

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Abstract. The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming holes (i.e., locations with decreasing daily maximum air temperatures ( T ) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θ e ) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θ e are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity ( Q ) at 850 and 500 hPa geopotential heights ( Z ), and spatiotemporally averaged soil moisture ( SM ). SM is particularly important in determining the magnitude of θ e over regions that have previously been identified as exhibiting warming holes, confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θ e . Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T , synoptic-scale meteorological conditions and SM ). This is particularly marked in regions with high variability in minimum and maximum θ e , where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θ e and the occurrence of extreme maximum θ e . Over the entire domain, the ANN with three hidden layers exhibits high accuracy in predicting maximum θ e > 347 K. The median hit rate for maximum θ e >347 K is > 0.60, while the median false alarm rate is ≈ 0.08.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Application of gas-liquid fluid dynamics techniques to the modeling of sprays

The purpose of this paper is to provide an overview of modern computational techniques for gas-liquid systems as they are found in liquid fuel atomization applications. Fuel atomization – the generation of ligaments and droplets from the bulk of liquid injection – offers several modeling challenges: a localized surface tension force, depending on the accurate evaluation of interface curvature; drastic topology changes, which complicate interface advection and introduce local length scales; and separation of phases that have substantially different physical properties. The techniques presented in this review specifically target the gas-liquid interface and couple its evolution in time with the equations of multiphase fluid dynamics. Recent algorithmic advances in this area are discussed first, including new methods that improve conservation properties and deal with very large density ratios and compressibility effects. This part is followed by a discussion on the issue of convergence in simulations with topology changes (i.e., surface pinch-off) and on the viewpoint offered by atomistic and statistical modeling. State-of the-art simulations for various types of fuel injectors are presented next, with particular attention to numerical convergence and to the inclusion of realistic inlet boundary conditions. The review is concluded by the discussion of some of the challenges that still remains in this field.

42 ENGINEERING↗