A reactive transport modeling perspective on the dynamics of interface-coupled dissolution-precipitation
Not Available
58 GEOSCIENCES↗
SEARCH · Engineering Papers
Results for “Probabilistic models”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Using ultrasonic attenuation in cortical bone to infer distributions on pore size
Here, in this work we infer the underlying distribution on pore radius in human cortical bone samples using ultrasonic attenuation data. We first discuss how to formulate polydisperse attenuation models using a probabilistic approach and the Waterman Truell model for scattering attenuation. We then compare the Independent Scattering Approximation and the higher-order Waterman Truell models’ forward predictions for total attenuation in polydisperse samples. Following this, we formulate an inverse problem under the Prohorov Metric Framework coupled with variational regularization to stabilize this inverse problem. We then use experimental attenuation data taken from human cadaver samples and solve inverse problems resulting in nonparametric estimates of the probability density function on pore radius. We compare these estimates to the “true” microstructure of the bone samples determined via microCT imaging. We find that our methodology allows us to reliably estimate the underlying microstructure of the bone from attenuation data.
97 MATHEMATICS AND COMPUTING↗
Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes
Hydrothermal liquefaction (HTL) has potential for converting abundant wet organic wastes into renewable fuels. Because HTL consists of a complex reaction network, deterministic, physics-based prediction of its biocrude yield is prohibitively difficult. Data-driven methods provide an alternative to the physics-based approach; however, rigorous testing must be performed to ensure the accuracy of predictions made by data-driven methods. To this end, a data set was assembled consisting of 570 data points appearing in the open literature. The data set was divided into training, validation, and test sub-sets and used for evaluating different machine learning regression approaches to predict biocrude yield. Among the tested algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost) predicted biocrude yields in a test set that had not been used for training with the greatest accuracy, with root mean square errors (RMSE) of 8.34 and 8.57, respectively. Further refinement of the Random Forest model reduced its RMSE to 8.07. In comparison, predictions of a series of literature models resulted in RMSE ranging from 9.16 in the most accurate case to 27.6 in the least accurate; most literature models yielded RMSE values > 10. Using biocrude yield predictions from the most accurate Random Forest model and a probabilistic economic analysis found that the model accuracy is sufficient to prioritize allocation of resources based on projected minimum fuel selling price. In our report the models and analysis represent a major advance in the ability to use readily available data to predict biocrude yields on new feedstocks that have not previously been studied.
42 ENGINEERING↗
Anthropogenic Impacts on Atmospheric Carbonyl Sulfide Since the 19th Century Inferred From Polar Firn Air and Ice Core Measurements
Carbonyl sulfide (COS) was measured in firn air collected during seven different field campaigns carried out at four different sites in Greenland and Antarctica between 2001 and 2015. A Bayesian probabilistic statistical model is used to conduct multisite inversions and to reconstruct separate atmospheric histories for Greenland and Antarctica. The firn air inversions cover most of the 20th century over Greenland and extend back to the 19th century over Antarctica. The derived atmospheric histories are consistent with independent surface air time series data from the corresponding sites and the Antarctic ice core COS records during periods of overlap. Atmospheric COS levels began to increase over preindustrial levels starting in the 19th century, and the increase continued for much of the 20th century. Atmospheric COS peaked at higher than present-day levels around 1975 CE over Greenland and around 1987 CE over Antarctica. An atmosphere/surface ocean box model is used to investigate the possible causes of observed variability. The results suggest that changes in the magnitude and location of anthropogenic sources have had a strong influence on the observed atmospheric COS variability.
54 ENVIRONMENTAL SCIENCES↗
Fatty acid foams for nonselective physical removal of microplastics from aqueous solutions
Microplastics (MPs) are pervasive environmental contaminants whose removal from water remains a major challenge due to their small size, chemical diversity, and dynamic surface properties arising from environmental aging/weathering. Here, we present a concept of foam-based separation method that physically traps MPs in the foam phase using microtubular assemblies of 12-hydroxystearic acid. These foams are stabilized by anisotropic fatty acid microtubules formed in the presence of ethanolamine, which jam within the foam channels and suppress fluid drainage thereby enhancing MP retention and foam stability. MPs of different sizes, polymer compositions (including polystyrene, polypropylene, polyethylene terephthalate, and polytetrafluoroethylene), and weathered states were retained in the foam phase without requiring chemical modification or relying on chemical interactions between the fatty acid and MPs. Thermally induced transition of the fatty acid microtubules into nanomicelles above the characteristic phase transition temperature (∼35 °C) enables controlled foam collapse and recovery of trapped MPs. The cumulative removal efficiency can exceed 85% through multiple foaming cycles, matching predictions from a probabilistic retention model. This work shows that foams can provide a simple platform to trap MPs, thus providing a new physical-removal strategy that does not rely on the particles’ chemistry.
Guillot, Kennedy A. [Louisiana State Univ., Baton ↗
Cyber‐Resilient Distributed Energy Resource Control Algorithms for Smart Distribution Grids
ABSTRACT This paper focuses on the development of cyber‐resilient gradient‐based optimisation algorithms and theoretical proof for grid‐interactive distributed energy resource (DER) control to enable two grid services of virtual power plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. Firstly, the combined DER dispatch and voltage regulation as a real‐time gradient‐based optimisation problem is recapped. Thereafter, we consider a probabilistic traffic model to characterise packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control centre that execute this control algorithm in a coordinated manner. Then, a strategy combining delay thresholds and message update rules is proposed to immunity the asynchrony resulting from the communications traffic and it avoids possible numerical instabilities and sensitivities of the power tracking and voltage regulation capabilities, resulting as cyber‐resilient DER control algorithms. Additionally, their convergence is theoretically proved. Effectiveness of proposed cyber‐resilient algorithms has been validated on the IEEE 37‐bus system in terms of convergence, VPP tracking and voltage regulation performance for smart distribution systems with high penetration of DERs.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Radioisotope Identification with List-Mode Gamma-Ray Data
This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with spectra containing similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and qualitative distribution analysis. Additionally, we propose a probabilistic classification model that can utilize spectral data, temporal data, or both to determine if the incorporation of temporal information improves radioisotope identification. Our findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated to develop more robust and optimal methods for utilizing this temporal information in applications requiring radioisotope identification.
List-mode data↗
Cyber Physical Grid-Interactive Distributed Energy Resources Control for VPP Dispatch and Regulation
This paper presents a cyber-physical algorithm for grid-interactive Distributed Energy Resource (DER) control to enable two features of Virtual Power Plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. We first formulate the DER dispatch problem as a real-time, iterative, and grid-interactive DER control problem. Thereafter, we consider a probabilistic traffic model to characterize packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control center that coordinately execute this dispatch algorithm. Finally, a strategy combining delay threshold and modified message update rules is proposed to immune the asynchrony resulting from the communications network traffic and it avoids possible numerical instabilities and sensitivities of the tracking and regulation capabilities of this DER control algorithm. By implementing the proposed cyber-physical algorithm on the modified IEEE 37-node system, our preliminary results exhibit that the uncertainties of the underlying communications infrastructure must be considered for the VPP tracking and regulation capabilities of any DER in a generic Cyber-Physical System (CPS), because the delayed voltage measurements in the uplink/bi-link cases result in the off-track in VPP dispatch and jittery in voltage regulation.
cyber-physical algorithm↗
Forecasting Day-Ahead Solar Irradiance for Puerto Rico Using the WRF Model and NSRDB
Accurately predicting solar energy resources is a major challenge in integrating photovoltaics generation on the electric grid. Numerical weather prediction has been recognized by the solar energy community as a major approach to provide solar resource forecasts at various locations and for a variety of timescales. In this study, as a part of the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100), we develop day-head solar irradiance forecast data using the Weather Research and Forecasting (WRF) model at 3 km and hourly/5-minute. The global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts simulated from the WRF model are postprocessed by a simple optimization method using satellite-derived gridded observations from the National Solar Radiation Data Base (NSRDB) to reduce error and bias of the solar irradiance forecasts covering 2018-2020. The NSRDB contributes to improving the GHI and DNI forecasts and also offers the opportunity for an in-depth analysis to evaluate their accuracy over a wide range of Puerto Rico regions. Preliminary results show overall improvements of GHI forecasts up to 37% (DNI: 15%) for mean absolute error and 97% (DNI: 76%) for mean bias error by applying a postprocessing technique to WRF model output.
data models↗
Probabilistic Resilience-Oriented Assessment Approach for Transmission Networks Under Wildfires
The rising threat of wildfires poses significant challenges to power transmission networks, particularly in areas prone to such disasters. Traditional approaches for wildfire risk assessment neglect some potential wildfire scenarios. Here, this paper introduces a probabilistic resilience-oriented assessment approach for power transmission networks to address this gap. Initially, a probabilistic wildfire model is developed to capture uncertainties in ignition, intensity, and fire spread. Next, a spatiotemporal fragility model is constructed to assess the impact of wildfires on transmission corridors, incorporating Thermal Aging (TA) and Dynamic Thermal Rate (DTR) change. Finally, a comprehensive resilience metric is defined to evaluate system performance, leveraging the fragility model to determine component and system-level resilience. The approach employs a combinatorial enumeration method to generate potential wildfire scenarios, enhanced by an impact-increment-based state enumeration (IISE) method for computational efficiency. The proposed method provides critical insights for identifying system vulnerabilities and developing robust strategies to protect transmission networks from wildfires. The efficacy of this approach is validated through extensive scenarios of the RTS-GMLC system across Southern California, Nevada and Arizona.
Vahedi, Soroush [Univ. of Connecticut, Storrs, CT ↗
General Bayesian Inference over the Stiefel Manifold via the Givens Transform
We introduce here an approach based on the Givens representation for posterior inference in statistical models with orthogonal matrix parameters, such as factor models and probabilistic principal component analysis (PPCA). We show how the Givens representation can be used to develop practical methods for transforming densities over the Stiefel manifold into densities over subsets of Euclidean space. We demonstrate how to deal with issues arising from the topology of the Stiefel manifold and how to inexpensively compute the change-of-measure terms. We introduce an auxiliary parameter approach that limits the impact of topological issues. We provide both analysis of our methods and numerical examples demonstrating the effectiveness of the approach. We also discuss how our Givens representation can be used to define general classes of distributions over the space of orthogonal matrices. We then give demonstrations on several examples showing how the Givens approach performs in practice in comparison with other methods.
97 MATHEMATICS AND COMPUTING↗
Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network
Advances in nuclear power technologies require enhanced capabilities for operator advice and autonomous control. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for structures, systems, and components (SSCs) of interest, for which the primary goal is to aid plant personnel in deducing the probabilistic performance status of the monitored SSCs and in detecting impending faults/failure. The task of conditional failure probability estimation is a bidirectional inference problem and shall be logically tackled by the Bayesian network (BN) approach. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers the capability of reasoning under uncertainty and graphical representation emulating the physical behavior of the target SSC. This paper provides a systematic overview of the BN technique and the software tools for handling implementation of BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgement can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. Our focus is upon failure-prone and risk-important balance of plant assets, especially cases having strong operator involvement. An exemplary case study on the failure of a motor-driven centrifugal pump is also conducted to demonstrate the usefulness and technical feasibility of the proposed artificial reasoning system using an expert system shell.
Zhao, Xingang↗
In-Situ Inference: Bringing Advanced Data Science Into Exascale Simulations
As simulations generate ever-increasing amounts of data, there are correspondingly richer opportunities for analysis and scientific discovery - discoveries that will be missed if most of the data must be discarded before it is analyzed. Because future exascale architectures will be increasingly storage-limited, it will not be possible to save the vast majority of simulation data for later analysis, requiring analysis to occur “in-situ” within the simulation. However, existing in-situ data analysis frameworks provide little or no support for one of the most sophisticated forms of data science: probabilistic statistical modeling or uncertainty quantification (UQ), and the accompanying challenge of inference - fitting those statistical models to massive simulation output. Our goal is to develop the fundamental statistical algorithms and computer science needed to perform statistical inference in-situ (in HPC simulations) to the full stream of data those simulations generate.
97 MATHEMATICS AND COMPUTING↗
Industry Level Integrated Fire Modeling Using Fire Risk Investigation in 3D (FRI3D)
The software Fire Risk Investigation in 3D (FRI3D) has been developed over the last 2 years to integrate 3D spatial modeling with existing fire probabilistic risk assessment (PRA) models and fire simulation codes. The goal of this research and development is to automate many of the fire analysis manual tasks to reduce industry efforts in the initial fire modeling and operational costs for the model maintenance and evaluations required during normal plant operations. The tasks for Fiscal Year (FY) 2021 include first testing the FRI3D modeling capabilities by importing an industry fire model into FRI3D and making a 3D model of a complex/high-risk significant area. (For this work, the switchgear room was chosen.) Then, the second task of FY 2021 is to develop a dynamic fire PRA process that can help optimize traditional fire PRA models. The switchgear room model will be used for the dynamic fire PRA work. This report describes the work and insights learned when using FRI3D software to model both a Nuclear Regulatory Report (NUREG) example models and a full industry switchgear room.
97 MATHEMATICS AND COMPUTING↗
Data-Driven Decision Control for Complex Systems (DnC2S) (Final Technical Report - Award DE-SC0021321)
This final technical report summarizes the key research outcomes achieved by the University of California at Santa Barbara Team of the “Data-Driven Decision Control for Complex Systems (DnC2S)” project under award number DE-SC0021321.
97 MATHEMATICS AND COMPUTING↗
How to Obtain the Redshift Distribution from Probabilistic Redshift Estimates
Abstract A reliable estimate of the redshift distribution n ( z ) is crucial for using weak gravitational lensing and large-scale structures of galaxy catalogs to study cosmology. Spectroscopic redshifts for the dim and numerous galaxies of next-generation weak-lensing surveys are expected to be unavailable, making photometric redshift (photo- z ) probability density functions (PDFs) the next best alternative for comprehensively encapsulating the nontrivial systematics affecting photo- z point estimation. The established stacked estimator of n ( z ) avoids reducing photo- z PDFs to point estimates but yields a systematically biased estimate of n ( z ) that worsens with a decreasing signal-to-noise ratio, the very regime where photo- z PDFs are most necessary. We introduce Cosmological Hierarchical Inference with Probabilistic Photometric Redshifts ( CHIPPR ), a statistically rigorous probabilistic graphical model of redshift-dependent photometry that correctly propagates the redshift uncertainty information beyond the best-fit estimator of n ( z ) produced by traditional procedures and is provably the only self-consistent way to recover n ( z ) from photo- z PDFs. We present the chippr prototype code, noting that the mathematically justifiable approach incurs computational cost. The CHIPPR approach is applicable to any one-point statistic of any random variable, provided the prior probability density used to produce the posteriors is explicitly known; if the prior is implicit, as may be the case for popular photo- z techniques, then the resulting posterior PDFs cannot be used for scientific inference. We therefore recommend that the photo- z community focus on developing methodologies that enable the recovery of photo- z likelihoods with support over all redshifts, either directly or via a known prior probability density.
79 ASTRONOMY AND ASTROPHYSICS↗
Proper Motions, Orbits, and Tidal Influences of Milky Way Dwarf Spheroidal Galaxies
We combine Gaia early data release 3 astrometry with accurate photometry and utilize a probabilistic mixture model to measure the systemic proper motion of 52 dwarf spheroidal (dSph) satellite galaxies of the Milky Way (MW). For the 46 dSphs with literature line-of-sight velocities we compute orbits in both a MW and a combined MW + Large Magellanic Cloud (LMC) potential and identify Car II, Car III, Hor I, Hyi I, Phx II, and Ret II as likely LMC satellites. 40% of our dSph sample has a >25% change in pericenter and/or apocenter with the MW + LMC potential. For these orbits, we use a Monte Carlo sample for the observational uncertainties for each dSph and the uncertainties in the MW and LMC potentials. We predict that Ant II, Boo III, Cra II, Gru II, and Tuc III should be tidally disrupting by comparing each dSph's average density relative to the MW density at its pericenter. dSphs with large ellipticity (CVn I, Her, Tuc V, UMa I, UMa II, UMi, Wil 1) show a preference for their orbital direction to align with their major axis even for dSphs with large pericenters. We compare the dSph radial orbital phase to subhalos in MW-like N-body simulations and infer that there is not an excess of satellites near their pericenter. With projections of future Gaia data releases, we find that dSph's orbital precision will be limited by uncertainties in the distance and/or MW potential rather than in proper motion precision. Finally, we provide our membership catalogs to enable community follow-up.
79 ASTRONOMY AND ASTROPHYSICS↗