Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model exploration”

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.

At least 1,243 records · Page 69

Conceptual and Mathematical Foundation for the FE/NETL CO 2 Prophet Model for Simulating CO 2 Enhanced Oil Recovery, Version 2

The United States (U.S.) Department of Energy (DOE) Office of Fossil Energy (FE) at the National Energy Technology Laboratory (NETL) has developed the FE/NETL CO 2 Prophet Model, which is Version 2 of CO 2 Prophet. This document describes the mathematical foundation for the FE/NETL CO 2 Prophet Model. CO 2 Prophet was originally developed in the 1990s by Texaco Exploration and Production and Technology Department for DOE. The FE/NETL CO 2 Prophet Model is an oil reservoir simulator that is suitable for simulating water floods and supercritical carbon dioxide (CO 2 ) enhanced oil recovery (EOR). The FE/NETL CO 2 Prophet Model uses a number of assumptions to simplify the equations describing the flow of oil, water (or brine), and CO 2 in the oil reservoir.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Particle Theory and Cosmology

This project covered theoretical studies in particle physics, particle astrophysics and cosmology, aiming to bridge theoretical models with observable phenomena. The central focus was on exploring innovative mechanisms that could simultaneously address several outstanding puzzles in these areas, including the nature of dark matter, the muon g-2 anomaly, the existence of topologically stable monopoles, the generation of observable gravitational waves from early universe phenomena and high energy cosmic rays. One of the major achievements of this project was the development of models that predict new physics accessible through current and forthcoming experimental setups, both in particle colliders and astrophysical observations. These models have been instrumental in proposing verifiable predictions concerning supersymmetric extensions, the dynamics of cosmic strings and monopoles, and the intricate processes underpinning baryogenesis and reheating post-inflation. In tackling the dark matter conundrum, the project proposed several candidates within extended frameworks, such as light Z' models, pseudo-Goldstone dark matter, and scenarios integrating dark matter with inflationary cosmology. Each model outlined pathways for detection through direct, indirect, and collider search strategies, marking significant strides in the hunt for dark matter. Another cornerstone of the project was the in-depth analysis of inflationary models compliant with the Trans-Planckian Censorship Conjecture, highlighting the compatibility of axion dark matter within such frameworks. This not only provided a coherent picture of early universe cosmology but also delineated clear experimental signatures. The exploration of grand unified theories yielded insights into the potential discovery of monopoles and novel particle configurations at energy scales accessible to current and future colliders. This endeavor expanded the predictive power of these theories, particularly in the context of proton decay and the properties of Higgs-portal dark matter. Throughout the project, significant emphasis was placed on ensuring the theoretical developments were grounded in experimental testability. This led to a series of publications across prestigious journals, each contributing to the vibrant discourse at the intersection of particle physics and cosmology. In summary, this project has elucidated pathways beyond the Standard Model that are ripe for exploration through both ongoing and upcoming experimental efforts. The comprehensive approach adopted herein not only enhances our understanding of the fundamental forces and constituents of the universe but also propels the field towards new frontiers in high-energy physics and cosmology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

New technology for an ancient fish: A lamprey life cycle modeling tool with an R Shiny application

Lampreys (Petromyzontiformes) are an ancient group of fishes with complex life histories. We created a life cycle model that includes an R Shiny interactive web application interface to simulate abundance by life stage. This will allow scientists and managers to connect available demographic information in a framework that can be applied to questions regarding lamprey biology and conservation. We used Pacific lamprey ( Entosphenus tridentatus ) as a case study to highlight the utility of this model. We applied a global sensitivity analysis to explore the importance of individual life stage parameters to overall population size, and to better understand the implications of existing gaps in knowledge. We also provided example analyses of selected management scenarios (dam passage, fish translocations, and hatchery additions) influencing Pacific lamprey in fresh water. These applications illustrate how the model can be applied to inform conservation efforts. This tool will provide new capabilities for users to explore their own questions about lamprey biology and conservation. Simulations can hone hypotheses and predictions, which can then be empirically tested in the real world.

Gomes, Dylan G. E. (ORCID:0000000226423728)↗

Concept Lens: Visual Comparison and Evaluation of Generative Model Manipulations

Generative models are becoming a transformative technology for the creation and editing of images. However, it remains challenging to harness these models for precise image manipulation. These challenges often manifest as inconsistency in the editing process, where both the type and amount of semantic change, depend on the image being manipulated. Moreover, there exist many methods for computing image manipulations, whose development is hindered by the matter of inconsistency. This paper aims to address these challenges by improving how we evaluate, compare, and explore the space of manipulations offered by a generative model. We present Concept Lens, a visual interface that is designed to aid users in understanding semantic concepts carried in image manipulations, and how these manipulations vary over generated images. Given the large space of possible images produced by a generative model, Concept Lens is designed to support the exploration of both generated images, and their manipulations, at multiple levels of detail. To this end, the layout of Concept Lens is informed by two hierarchies: a hierarchical organization of (1) original images, grouped by their similarities, and (2) image manipulations, where manipulations that induce similar changes are grouped together. This layout allows one to discover the types of images that consistently respond to a group of manipulations, and vice versa, manipulations that consistently respond to a group of codes. We show the benefits of this design across multiple use cases, specifically, studying the quality of manipulations for a single method, and offering a means of comparing different methods.

clustering↗

Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County

Exploring climate-induced impacts on building energy consumption can provide valuable insights for sustainable energy planning and environmental management in the face of a changing climate. By utilizing future weather data statistically downscaled from the Intergovernmental Panel on Climate Change (IPCC) General Circulation Models (GCMs) from 2020-2100, this paper presents a broadening industry-consensus approach for generating future Typical Meteorological Year (fTMY) weather files through a combination of statistical downscaling and high-performance computing that generalizes across decades, multiple locations for a region, and varying climate models. Furthermore, these fTMY files have been generated for 3,128 US counties for capturing potential weather on a 20-year basis.

Building↗

Ice-shelf freshwater triggers for the Filchner–Ronne Ice Shelf melt tipping point in a global ocean–sea-ice model

Some ocean modeling studies have identified a potential tipping point from a low to a high basal melt regime beneath the Filchner–Ronne Ice Shelf (FRIS), Antarctica, with significant implications for subsequent Antarctic ice sheet mass loss. To date, investigation of the climate drivers and impacts of this possible event have been limited because ice-shelf cavities and ice-shelf melting are only now starting to be included in global climate models. Using a global ocean–sea-ice configuration of the Energy Exascale Earth System Model (E3SM) that represents both ocean circulations and melting within ice-shelf cavities, we explore freshwater triggers (iceberg melt and ice-shelf basal melt) of a transition to a high-melt regime at FRIS in a low-resolution (30 km in the Southern Ocean) global ocean–sea-ice model. We find that a realistic spatial distribution of iceberg melt fluxes is necessary to prevent the FRIS melt regime change from unrealistically occurring under historical-reanalysis-based atmospheric forcing. Further, improvement of the default parameterization for mesoscale eddy mixing significantly reduces a large regional fresh bias and weak Antarctic Slope Front structure, both of which precondition the model to melt regime change. Using two different stable model con figurations, we explore the sensitivity of FRIS melt regime change to regional ice-sheet freshwater fluxes. Through a series of sensitivity experiments prescribing incrementally increasing melt rates from the smaller, neighboring ice shelves in the eastern Weddell Sea, we demonstrate the potential for an ice-shelf melt “domino effect” should the upstream ice shelves experience increased melt rates. The experiments also reveal that modest ice-shelf melt biases in a model, especially at coarse ocean resolution where narrow continental shelf dynamics are not well resolved, can lead to an unrealistic melt regime change at downstream ice shelves. Thus, we find that remote connections between melt fluxes at different ice shelves are sensitive to baseline model conditions. Our results highlight both the potential and the peril of simulating prognostic Antarctic ice-shelf melt rates in a low-resolution global model.

58 GEOSCIENCES↗

Dynamic crushing of metal lattice metamaterials: Shock mode diagrams and transition to topology-independent compaction regime

Additively manufactured lattice metamaterials offer design versatility in strength and energy absorption and provide an additional degree of freedom through the selection of the lattice topology. Under quasistatic loading, the unit cell structure can strongly affect the stiffness, yield, and post-yield behavior, but whether and to what degree the effect of lattice topology persists into dynamic loading scenarios, up to the compaction shock regime, has not been established. LLNL’ s ALE3D hydrocode was used to perform a computational investigation of dynamic loading in multiple lattice types, including the gyroid, octet, Schwarz D, and rhombic dodecahedron, under impact velocities from 0.25 to 2.25 km/s. Shock Hugoniots for each lattice topology are generated and compared, suggesting that above a critical velocity, distinctions between architectures may not persevere and compacted lattices behave similarly. Here, to investigate the transition between topology-dependent quasistatic compression and the topology-independent regime above the critical velocity, a one-dimensional elastic-linear hardening plasticity-densified solid (E-LHP-DS) shock model for lattice materials was developed that relies upon confined compression to link the quasistatic and shock mechanics. Unlike similar works, the model does not assume rigid behavior prior to yield or locking behavior at densification, allowing a richer exploration of lattice mechanics. With only six parameters, the analytical model simultaneously fit quasistatic confined compression simulations for relative densities 0.1 $≤ \bar{ρ} ≤$ 0.9 and predicted dynamic compaction behavior to traverse several distinct shock modes, each defined by a critical impact speed (equivalently, critical stresses). Comparing the numerical results to the one-dimensional E-LHP-DS shock model predictions suggests that the topology-independence under strong shocks is linked to the onset of densification, which can be predicted based on quasistatic confined compression results.

Cellular material↗

Allometric Scaling of Hyporheic Respiration Across Basins in the Pacific Northwest United States

Abstract Hyporheic zones regulate biogeochemical processes in streams and rivers, but high spatiotemporal heterogeneity makes it difficult to predict how these processes scale from individual reaches to river basins. Recent work applying allometric scaling (i.e., power‐law relationships between size and function) to river networks provides a new paradigm for understanding cumulative hyporheic biogeochemical processes. We used previously published model predictions of reach‐scale hyporheic aerobic respiration to explore patterns in allometric scaling across two climatically divergent basins with differing characteristics in the Pacific Northwest, United States. In the model, hydrologic exchange fluxes (HEFs) regulate hyporheic respiration, so we examined how HEFs might influence allometric scaling of respiration. We found consistent scaling behaviors where HEFs were either very low or very high, but differences between basins when HEFs were moderate. Our findings provide initial model‐generated hypotheses for factors influencing allometric scaling of hyporheic respiration. These hypotheses can be used to optimize new data generation efforts aimed at developing predictive understanding of allometries that can, in turn, be used to scale biogeochemical dynamics across watersheds.

59 BASIC BIOLOGICAL SCIENCES↗

Demonstration of new fracture criteria based on micro-structure and compare to empirical model and measurements

The US nuclear industry is currently exploring extending the peak rod average burnup limits above the current regulatory limit of 62 GWd/tU. A potential concern for fuel exceeding the burnup limit is the fragmentation during a temperature transient such as a loss-of-coolant accident (LOCA). In the event of cladding failure, the fragmented fuel could relocate and disperse within the reactor. The potential impact of fuel fragmentation, relocation and dispersal (FFRD) on licensing assumptions for burnup extension has focused a great amount of efforts and research on this topic.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Robust cosmological inference from non-linear scales with k -th nearest neighbour statistics

ABSTRACT We present the methodology for deriving accurate and reliable cosmological constraints from non-linear scales ($\lt 50\, h^{-1}$ Mpc) with k-th nearest neighbour (kNN) statistics. We detail our methods for choosing robust minimum scale cuts and validating galaxy–halo connection models. Using cross-validation, we identify the galaxy–halo model that ensures both good fits and unbiased predictions across diverse summary statistics. We demonstrate that we can model kNNs effectively down to transverse scales of $r_{\rm p}\sim 3\, h^{-1}$ Mpc and achieve precise and unbiased constraints on the matter density and clustering amplitude, leading to a 2 per cent constraint on σ8. Our simulation-based model pipeline is resilient to varied model systematics, spanning simulation codes, halo finding, and cosmology priors. We demonstrate the effectiveness of this approach through an application to the Beyond-2p mock challenge. We propose further explorations to test more complex galaxy–halo connection models and tackle potential observational systematics.

79 ASTRONOMY AND ASTROPHYSICS↗

Unraveling emission line galaxy conformity at z ∼ 1 with DESI early data

Emission line galaxies (ELGs) are now the preeminent tracers of large-scale structure at z > 0.8 due to their high density and strong emission lines, which enable accurate redshift measurements. However, relatively little is known about ELG evolution and the ELG–halo connection, exposing us to potential modelling systematics in cosmology inference using these sources. In this paper, we use a variety of observations and simulated galaxy models to propose a physical picture of ELGs and improve ELG–halo connection modelling in a halo occupation distribution framework. We investigate Dark Energy Spectroscopic Instrument (DESI)-selected ELGs in COSMOS data, and infer that ELGs are rapidly star-forming galaxies with a large fraction exhibiting disturbed morphology, implying that many of them are likely to be merger-driven starbursts. We further postulate that the tidal interactions from mergers lead to correlated star formation in central–satellite ELG pairs, a phenomenon dubbed ‘conformity’. We argue for the need to include conformity in the ELG–halo connection using galaxy models such as IllustrisTNG, and by combining observations such as the DESI ELG autocorrelation, ELG cross-correlation with luminous red galaxies, and ELG–cluster cross-correlation. We also explore the origin of conformity using the UniverseMachine model and elucidate the difference between conformity and the well-known galaxy assembly bias effect.

79 ASTRONOMY AND ASTROPHYSICS↗

Indirect searches at CMS

In these proceedings, we present several new measurements of Standard Model (SM) processes, in the Higgs sector and beyond, that push the precision frontier forward at CMS. Results are presented in the context of a framework parameterizing deviations in Higgs boson couplings, as well as in the context of SM Effective Field Theory, where new analyses targeting Higgs, top, and multi-boson processes probe an increasingly diverse set of operators. Within these frameworks, CMS is efficiently exploring a large space of new physics models. No significant deviation from SM expectations is found.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Mean field model parameterization to recover coverage-dependent kinetics

Lateral interactions between adsorbates introduce coverage dependence into adsorption energies and activation barriers of surface reactions. Lattice-based kinetic Monte Carlo (kMC) simulations can capture these interactions quantitatively but are laborious to parameterize and solve. Mean field models are more tractable, but protocols to construct and parameterize them are unclear. Here we explore the ability of a coverage-aware mean-field model to map to a lattice-kMC model of a generic two-step reaction network, including quasi-equilibrated adsorption and rate-limiting dissociation steps. We derive expressions for mean-field and coverage-dependent adsorption energies and dissociation barriers and parameterize against lattice-kMC predictions. We show that the parameterized mean-field rates correlate with ground truth lattice-kMC results across a wide range of reaction conditions and identify regions where the mean field fails. The mean field model similarly captures kMC-derived rate-order, Arrhenius and Sabatier plots at a greatly reduced computational cost. Further, the results provide guidance for parameterizing mean-field models, benchmarked against explicit lattice-based approaches for incorporating the influence of coverage effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Discovering nuclear models from symbolic machine learning

Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.

Nuclear structure↗

Dipolar Bose-Hubbard model

We study a simple model of interacting bosons on a d-dimensional cubic lattice whose dynamics conserves both total boson number and total boson dipole moment. This model provides a simple framework in which several remarkable consequences of dipole conservation can be explored. As a function of chemical potential and hopping strength, the model can be tuned between gapped Mott insulating phases and various types of gapless condensates. The condensed phase realized at large hopping strengths, which we dub a Bose-Einstein insulator, is particularly interesting: despite having a Bose condensate, it is insulating, and despite being an insulator, it is compressible.

36 MATERIALS SCIENCE↗

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗

The Lack of a QBO-MJO Connection in Climate Models With a Nudged Stratosphere

The observed stratospheric quasi-biennial oscillation (QBO) and the tropospheric Madden-Julian oscillation (MJO) are strongly connected in boreal winter, with stronger MJO activity when lower-stratospheric winds are easterly. However, the current generation of climate models with internally generated representations of the QBO and MJO do not simulate the observed QBO-MJO connection, for reasons that remain unclear. Furthermore, this study builds on prior work exploring the QBO-MJO link in climate models whose stratospheric winds are relaxed toward reanalysis, reducing stratospheric biases in the model and imposing a realistic QBO. A series of ensemble experiments are performed using four state-of-the-art climate models capable of representing the MJO over the period 1980–2015, each with similar nudging in the stratosphere. In these four models, nudging leads to a good representation of QBO wind and temperature signals, however no model simulates the observed QBO-MJO relationship. Biases in MJO vertical structure and cloud-radiative feedbacks are investigated, but no conclusive model bias or mechanism is identified that explains the lack of a QBO-MJO connection.

54 ENVIRONMENTAL SCIENCES↗