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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
DOE OSTI2022

Microgrid Resilience: A Holistic and Context-Aware Resilience Metric

Microgrids present an effective solution for the coordinated deployment of various distributed energy resources and furthermore provide myriad additional benefits such as resilience, decreased carbon footprint, and reliability to energy consumers and the energy system as a whole. Boosting the resilience of distribution systems is another major benefit of microgrids. This is because they can also serve as a backup power source when the utility grid's operations are interrupted due to either high-probability low-impact events like a component failure or low-probability high-impact events - be it a natural disaster or a planned cyberattack. However, the degree to which any particular system can defend, adapt, and restore normal operation depends on various factors including the type and severity of events to which a microgrid is subjected. These factors, in turn, are dependent on the geographical location of the deployed microgrid as well as the cyber risk profile of the site where the microgrid is operating. Therefore, in this work, we attempt to capture this multi-dimensional interplay of various factors in quantifying the ability of the microgrid to be resilient in these varying aspects. This paper, thus, proposes a customized site-specific quantification of the resilience strength for the individual microgrid's capability to absorb, restore, and adapt to the changing circumstances for sustaining the critical load when a low-probability high-impact event occurs - termed as - context-aware resilience metric. We also present a case study to illustrate the key elements of our integrated analytical approach.

microgrid↗
DOE OSTI2022

Identifying Disinformation Using Rhetorical Devices in Natural Language Models

Foreign disinformation campaigns are strategically organized, extended efforts using disinformation – false or misleading information deliberately placed by an adversary – to achieve some goal. Disinformation campaigns pose severe threats to our nation’s security by misinforming decision makers and negatively influencing their actions when they are operating on limited amounts of evidence. Current efforts rely on subject matter experts to manually identify disinformation, or on computers and traditional natural language processing algorithms to identify patterns in data to calculate the probability that something is disinformation or not. While both have their merits and successes, subject matter experts are unable to keep up with the high volumes of global information and traditional natural language algorithms do not do well in identifying “why” something is disinformation or not. Our hypothesis is that we can identify disinformation by looking at the way someone speaks, in the rhetorical devices they use. We have curated and annotated a dataset designed for multiple natural language processing tasks, but specifically useful for disinformation detection algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Resilience Evaluation and Enhancement for Island City Integrated Energy Systems

Extreme natural hazards, such as hurricanes or earthquakes, have a high probability of threatening energy supply security and causing high-order contingencies to island city-integrated energy systems (IC-IESs). To better evaluate and enhance resilience, a novel approach is proposed in this work for IC-IESs. The resilience of an IC-IES is analyzed from both the system level and the component level. At the system level, the impacts of extreme natural disasters are quantified. At the component level, the importance of individual components is analyzed through pre-failure and post-failure indices. The pre-failure index identifies the system’s weak links before an energy interruption, and the post-failure index determines the optimal repair strategy to restore the service. The proposed indices are solved by the impact increment method (IIM), which significantly improves computational efficiency without much affecting result accuracy. Numerical simulation studies are conducted on the modified Barry Island IES and IES E123-G48-H32 test systems. Furthermore, the results validate the effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics

ABSTRACT There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high‐dimensionality of the data, the high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be ( γ , σ)‐stable if the probability of the same classification is at least for points sampled in a Gaussian neighborhood of the point with a given standard deviation . We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of an Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST by extending the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5,000 annual synthetic storm events in a 2,227 $km^2$ Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

58 GEOSCIENCES↗
DOE OSTI2021

Effects of 18 O Exchange on Neutron Emission Rates of Aging 238 PuO 2 LWRHUs

The Lightweight Radioisotope Heater Unit (LWRHU) provides about 1 Watt of thermal power to critical electrical and mechanical systems in the extreme temperatures of space, maintaining the operating temperature of the spacecraft’s scientific and general operating equipment. Los Alamos National Laboratory (LANL) designed and tested the LWRHU in the 1970s through early 1980s. The first production campaign at LANL was in the early 1980s in support of the Galileo mission in which over 100 LWRHUs were manufactured. In the mid-1990s, almost 200 LWRHUs were manufactured in support of the Cassini mission. After the primary Cassini manufacturing campaign, the Department of Energy (DOE) approved the fabrication of more than 10 additional LWRHUs as spares in 1998. The LWRHU assembly contains the following components: a fuel pellet, a vented capsule, a pyrolytic graphite insulator, and a fine-weave pierced fabric (FWPF) graphite aeroshell. The fuel pellet is a hot-pressed 238 PuO 2 cylindrical pellet that is sintered to create a ceramic pellet. The pellet is encapsulated in a platinum-rhodium encapsulation. The welded fueled clad is placed inside a pyrolytic graphite thermal insulation to protect the fuel from the heat produced in reentry events. The exterior layer of the LWRHU assembly is the FWPF aeroshell. Before sintering, PuO 2 granules are heated in a furnace at temperatures greater than or equal to 700°C under an enriched 16 O atmosphere in order to reduce trace quantities of 18 O. Because 18 O undergoes an (α,n) reaction, 18 O needs to be removed as much as possible to meet radiation emission requirements set by the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), as well as other agencies. The relative abundances of the oxygen isotopes are indicated by neutron emission rate (NER) measurements. LWRHUs are vented with a sintered platinum frit vent (George 1986). During extraterrestrial operation, the vent in the cladding allows helium from the decay of plutonium to escape and relieves pressure from the system while preventing the solids from escaping (Rinehart 1996, Tate 1982 & 1985). This vent is protected during manufacture by a platinum-30 rhodium cover. The capsule vent is activated by milling a 0.025-inch diameter hole through the protective cover to a nominal depth of 0.015 inches, generally immediately before the LWRHU is loaded into the aeroshell (George 1986). Prior to long-term storage, LWRHUs are vented and thus potentially allow infiltration of 18 O from the ambient atmosphere. Although NER decreases with age due to the reduction in emitted alpha particles, it might be expected that NER reduction is mitigated by the exchange of 18 O. However, this is not necessarily the case and no trend was observed relating NER to age other than the natural decay of 238 Pu (Mulford 2021). The most probable and dominant factor for determining specific NER was manufacturing discrepancies between individual units. It is important to understand the lack of observed 16 O- 18 O exchange and to compare LWRHU pellets as much as possible.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Temperature Uncertainty Modeling with Proxy Structural Data as Geostatistical Constraints for Well Siting: An Example Applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aim of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations . The data used to support the modeling are measured temperatures and structural proxies from 3D geologic modeling, the latter considered "secondary" data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally likely temperature models (i.e., realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: Vprior and entropy. Vprior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e., linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g., dilation versus distance to fault termination) should be used given the specific geothermal system. In general, Vprior from local varying mean results identify locations that are close to high values for the structural proxies: areas with highe r probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comp arison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

3D temperature modeling↗
DOE OSTI2023

Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural geology information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aims of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations. The data used to support the modelling are measured temperatures and structural proxies from 3D geologic modelling (distance to fault, distance to fault intersections and terminations, Coulomb stress change and dilation tendency), the latter considered ‘secondary’ data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally-likely temperature models (i.e. realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: V prior and entropy. V prior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because of the dipping ellipsoid shape created and that the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e. linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g. dilation v. distance to fault termination) should be used given the specific geothermal system. In general, V prior from local varying mean results identify locations that are close to high values for the structural proxies: areas with higher probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comparison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. Further, the spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Rare Event Sampling Improves Mercury Instability Statistics

Due to the chaotic nature of planetary dynamics, there is a non-zero probability that Mercury's orbit will become unstable in the future. Previous efforts have estimated the probability of this happening between 3 and 5 billion years in the future using a large number of direct numerical simulations with an N-body code, but were not able to obtain accurate estimates before 3 billion years in the future because Mercury instability events are too rare. In this paper we use a new rare-event sampling technique, Quantile Diffusion Monte Carlo (QDMC), to estimate that the probability of a Mercury instability event in the next 2 billion years is approximately 10 –4 in the REBOUND N-body code. We show that QDMC provides unbiased probability estimates at a computational cost of up to 100 times less than direct numerical simulation. QDMC is easy to implement and could be applied to many problems in planetary dynamics in which it is necessary to estimate the probability of a rare event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Data-driven Vulnerability Analysis of Networked Pipeline System

This paper introduces an attack generation framework for evaluating the vulnerability of nonlinear networked pipeline systems. The vulnerability analysis is formulated as determining the presence of feasible attack sets, defined by boundary functions representing the effectiveness and stealthiness of attack signals with respect to the objective and attack detection module. The framework utilizes three data-driven models, including two discriminative models that learn the boundary functions and a generative model that produces elements of the feasible attack set. A new loss function ensures successful attack generation with high probability.

03 NATURAL GAS↗
DOE OSTI2023

Multiple highly expressed phospho enol pyruvate carboxylase genes have divergent enzyme kinetic properties in two C4 grasses

Abstract Background and Aims Phosphoenolpyruvate (PEP) carboxylase (PEPC) catalyses the irreversible carboxylation of PEP with bicarbonate to produce oxaloacetate. This reaction powers the carbon-concentrating mechanism (CCM) in plants that perform C4 photosynthesis. This CCM is generally driven by a single PEPC gene product that is highly expressed in the cytosol of mesophyll cells. We found two C4 grasses, Panicum miliaceum and Echinochloa colona, that each have two highly expressed PEPC genes. We characterized the kinetic properties of the two most abundant PEPCs in E. colona and P. miliaceum to better understand how the enzyme’s amino acid structure influences its function. Methods Coding sequences of the two most abundant PEPC proteins in E. colona and P. miliaceum were synthesized by GenScript and were inserted into bacteria expression plasmids. Point mutations resulting in substitutions at conserved amino acid residues (e.g. N-terminal serine and residue 890) were created via site-directed PCR mutagenesis. The kinetic properties of semi-purified plant PEPCs from Escherichia coli were analysed using membrane-inlet mass spectrometry and a spectrophotometric enzyme-coupled reaction. Key Results The two most abundant P. miliaceum PEPCs (PmPPC1 and PmPPC2) have similar sequence identities (>95 %), and as a result had similar kinetic properties. The two most abundant E. colona PEPCs (EcPPC1 and EcPPC2) had identities of ~78 % and had significantly different kinetic properties. The PmPPCs and EcPPCs had different responses to allosteric inhibitors and activators, and substitutions at the conserved N-terminal serine and residue 890 resulted in significantly altered responses to allosteric regulators. Conclusions The two, significantly expressed C4Ppc genes in P. miliaceum were probably the result of genomes combining from two closely related C4Panicum species. We found natural variation in PEPC’s sensitivity to allosteric inhibition that seems to bypass the conserved 890 residue, suggesting alternative evolutionary pathways for increased malate tolerance and other kinetic properties.

DiMario, Robert J. (ORCID:0000000250566868)↗
DOE OSTI2021

Quantifying the Long‐Range Coupling of Electronic Properties in Proteins with ab initio Molecular Dynamics**

Abstract The delicate interplay of covalent and non‐covalent interactions in proteins is inherently quantum mechanical and highly dynamic in nature. To directly interrogate the evolving nature of the electronic structure of proteins, we carry out 100‐ps‐scale ab initio molecular dynamics simulations of three representative small proteins with range‐separated hybrid density functional theory. We quantify the nature and length‐scale of the coupling of residue‐specific charge probability distributions in these proteins. While some nonpolar residues exhibit expectedly narrow charge distributions, most polar and charged residues exhibit broad, multimodal distributions. Even for nonpolar residues, we observe sequence‐specific deviations corresponding to charge accumulation or depletion that would be challenging to capture in a fixed charge force field. We quantify the effect of residue‐residue interactions on charge distributions first with linear cross‐correlations. We then show how additional insight can be gained from evaluating the mutual information of charge distributions. We show that a significant number of residues couple most strongly with residues that are distant in both sequence and space over a range of secondary structures including α‐helical, β‐sheet, disulfide bridging, and lasso motifs. The mutual information analysis is necessary to capture coupling between some polar and charged residues that would be otherwise missed.

Yang, Zhongyue↗
DOE OSTI2025

Impact of developmental methylmercury exposure on avian embryonic development, hatchling growth, and survival

Abstract Methylmercury (MeHg) is a globally ubiquitous and persistent environmental toxicant that negatively affects animal behavior, health, and reproduction. In birds, MeHg is transferred from female to egg, contaminating embryos during sensitive stages of early embryonic development and growth. This toxic exposure in the prenatal environment not only induces mortality but also possible lasting impacts on physiology, health, and survival, even once hatched. The purpose of our study was to further elucidate the negative effects of MeHg exposure during avian embryonic development and explore how such exposure can impact offspring development, growth, and survival, both in ovo and posthatch. To assess this, we experimentally dosed fertile mallard and wood duck eggs with MeHg II chloride and reared developing embryos and ducklings to various endpoints. We found that embryos not only readily accumulated MeHg throughout incubation, but they also displayed varying dose-dependent disparities in body mass and morphometrics, with control individuals being larger during early and late embryonic stages of development (p < 0.05). Furthermore, hatched offspring exposed to MeHg exhibited increasingly slower growth rates between 7 to 10 and 10 to 15 days posthatch (p < 0.05), and were found to have lower survival probabilities both under controlled laboratory conditions (p < 0.005), and in the natural environment (p < 0.05). Our findings on the detrimental effects of MeHg on avian embryos and hatchlings emphasize the need for more proactive means of environmental protection and remediation to protect vulnerable avian populations and the ecosystems they inhabit.

Leaphart, James C.↗
DOE OSTI2025

Treatment of antibiotic-manufacturing wastewater enriches for Aeromonas veronii, a zoonotic antibiotic-resistant emerging pathogen

Antibiotic-manufacturing wastewater treatment plants primarily target chemical pollutants, but their processes may select for antibiotic-resistant pathogens and antibiotic resistance genes. Leveraging the combined strengths of deep metagenomic sequencing, 16S rRNA gene sequencing, quantitative polymerase chain reaction, and bacterial culturing, we investigated bacterial communities and antibiotic resistomes across eleven treatment units in a full-scale antibiotic-manufacturing wastewater treatment plant processing wastewater from a β-lactam manufacturing facility. Both bacterial communities and antibiotic resistance gene compositions varied across the treatment units, but were associated. Certain antibiotic resistance gene persisted through treatment, either carried by identical bacterial species, or linked to mobile genetic elements in different species. Despite the satisfactory performance in chemical removal, this plant continuously enriched zoonotic antibiotic-resistant Aeromonas veronii (an emerging pathogen responsible for substantial economic losses in aquaculture and human health) from influent to effluent, probably due to prolonged β-lactam selection pressure and aquatic nature of A. veronii. This enrichment resulted in a significantly higher abundance of A. veronii than other aquatic samples worldwide. Furthermore, the closest evolutionary relative to the retrieved A. veronii was an isolate obtained from the stool of a local diarrhea patient. These findings highlighted a substantial public health risk posed by antibiotic-manufacturing wastewater treatment, underlining its potential role in enriching and disseminating zoonotic antibiotic-resistant pathogens. Beyond chemical monitoring, enhanced surveillance of antibiotic-resistant pathogens and antibiotic resistance genes is needed in effluent discharge standard for antibiotic-manufacturing wastewater treatment plants.

Wang, Xingshuo↗
DOE OSTI2024

Robust VAR Capability Curve of DER with Uncertain Renewable Generation

Active distribution system with high penetration of inverter based distributed energy resources(DER), can be utilized for var-related ancillary services at the transmission side interface. In order to utilize the DER flexibility, transmission system operator must be presented the aggregated DER flexibility of distribution system. However, the uncertainty in renewable generation, questions the credibility of aggregated capability curve in practice. In this paper, we incorporate the uncertainty into aggregation process to develop capability curve while preserving the real physics (unbalance and lossy nature) of distribution system. The Resulting capability curve with the associated probability can be harnessed by the TSO for decision making for both planning and operation.

Kar, Aditya Shankar↗
DOE OSTI2023

Hydrogen Extremely Low Probability of Rupture Version 1

SAND2024-08511O The Hydrogen Extremely Low Probability of Rupture (HELPR) software toolkit provides probabilistic fracture assessments of natural gas infrastructure for transporting hydrogen. Using probabilistic sampling and sensitivity analysis—along with engineering fatigue and fracture mechanics— the software develops models that can predict structural integrity measures for natural gas pipelines under stress. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Schroeder, Benjamin↗
DOE OSTI2024

Visualisation and outlier detection for probability density function ensembles

Abstract Exploratory data analysis (EDA) for functional data—data objects where observations are entire functions—is a difficult problem that has seen significant attention in recent literature. This surge in interest is motivated by the ubiquitous nature of functional data, which are prevalent in applications across fields such as meteorology, biology, medicine and engineering. Empirical probability density functions (PDFs) can be viewed as constrained functional data objects that must integrate to one and be nonnegative. They show up in contexts such as yearly income distributions, zooplankton size structure in oceanography and in connectivity patterns in the brain, among others. While PDF data are certainly common in modern research, little attention has been given to EDA specifically for PDFs. In this paper, we extend several methods for EDA on functional data for PDFs and compare them on simulated data that exhibit different types of variation, designed to mimic that seen in real‐world applications. We then use our new methods to perform EDA on the breakthrough curves observed in gas transport simulations for underground fracture networks.

97 MATHEMATICS AND COMPUTING↗