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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Chicago Social Interaction Model (ChiSIM)

ChiSIM is a framework for implementing agent-based models that simulate the mixing of a synthetic population. In a ChiSIM based model, each agent, that is, each person in the simulated population, resides in a place (a household, dormitory or retirement home/long term care facility, for example), and moves among other places such as workplaces, homes, clinics, and community resources. Agents typically move between places according to their domain specific activity profile, such that each agent has a profile that determines at what times throughout the day they occupy a particular location. Once in a place, an agent mixes with other agents in some model or domain-specific way. For example, an agent may expose other agents to a disease in an epidemiological model.

Ozik, Jonathan↗
DOE OSTI2021

Multi-Scale Land-Atmosphere Interactions: Modeling Convective Processes from Plants to Planet

Research accomplishments include: 1)Two case studies of the effects of heterogeneous soil moisture and surface energy budgets on organization and propagation of convective precipitation during MC3E. 2) Development and evaluation of a new approach for simulating the effect of heterogeneous soil moisture at ARM-SGP using an innovative modeling approach. 3) Investigation of the effects of spatial coupling scale using multidecade global simulations in CESM with the multiscale modeling framework. 4) Exploration of changes to future precipitation intensity resulting from two different climate change scenarios using the multiscale model. 5) Provision of the new cloud-scale coupled multiscale Earth System Model to the larger community through the CESM process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

DUNE and MINERvA Flux Studies and a Measurement of the Charged-Current Quasielastic Antineutrino Scattering Cross Section with $<E_{\nu}> \sim 6 $ GeV on a CH Target

One of the biggest open questions in physics is the prevalence of matter over anti-matter in the present universe. One of possible answers lies in the violation of charge and parity symmetry in the lepton sector that would favor matter over anti-matter so that the universe becomes dominated by matter over time. Recent results from neutrino experiments like T2K and NOvA have indicated that there is CP violation in the leptonic sector \citep{Abe:2019vii}. However, the precise measurement of CP violation will require an unprecedented level of accuracy in these oscillation experiments. One of the challenges lies with the systematic uncertainties that come with the measurements, which include uncertainties related to the neutrino flux modeling, interaction modeling and detector related systematic uncertainties.\\ The first part of this thesis will go through the challenges related to flux uncertainties. It will discuss the flux uncertainties related to hadron production in the LBN F beamli ne for the DUNE experiment. Hadron production models used in flux simulations vary widely depending on the simulation and choice of physics models used to simulate the hadron production. This thesis will explain the method of using existing hadron production data to constrain these uncertainties, a method which has been used in MINERvA experiment.\\ The second part of the thesis goes through the effect of possible flux mismodeling and MINERvA's approach to address this effect. In doing so, we discovered that the suspected mismodeling could also result from incorrect estimation of the energy scale of the muons. This project not only demonstrated the challenges of flux modeling but also led to a novel approach of using neutrino energy spectra to understand the correlation between detector and neutrino beamline parameters. \\ A high statistics measurement of the anti-neutrino scattering cross section in the charged current quasielastic (CCQE) channel is the final part of the th esis. Th e 2-body interactions in this channel have the advantage of allowing reconstruction of the interaction kinematics from the outgoing muon trajectory which can be measured very well. Double differential cross sections as a function of muon kinematics, one of the deliverables of this analysis, will help future oscillation experiments understand their data. The cross-section as a function of the four-momentum transferred from the leptonic system to the hadronic system ($Q^{2}_{QE}$) can be used to test models used in simulating antineutrino interactions and the nuclear effects that can modify the predicted cross sections. Nuclear effects arise from the complex nuclear environment and both modify the initial scattering and change the fate of the particles produced from in neutrino-nucleus interactions. Study of nuclear effects will help to understand the structure of the atomic nucleus and its impact on neutrino interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Cross-shell excited configurations in the structure of 34 Si

The cross-shell excited states of 34 Si have been investigated via β decays of the 4 − ground state and the 1 + isomeric state of 34 Al. Since the valence protons and valence neutrons occupy different major shells in the ground state as well as the intruder 1 + isomeric state of 34 Al, intruder levels of 34 Si are populated via allowed β decays. Spin assignments to such intruder levels of 34 Si were established through γ-γ angular correlation analysis for the negative-parity states. The configurations of such intruder states play crucial roles in our understanding of the N = 20 shell gap evolution. A configuration interaction model derived from the FSU Hamiltonian was utilized in order to interpret the intruder states in 34 Si. Furthermore, shell model interaction derived from a more fundamental theory with the valence space in medium similarity renormalization group method was also employed to interpret the structure of 34 Si.

20 ≤ A ≤ 38↗
DOE OSTI2021

Bridging cognitive gaps between user and model in interactive dimension reduction

Interactive machine learning (ML) systems are difficult to design because of the "Two Black Boxes" problem that exists at the interface between human and machine. Many algorithms that are used in interactive ML systems are black boxes that are presented to users, while the human cognition represents a second black box that can be difficult for the algorithm to interpret. These two black boxes create cognitive gaps between the user and the interactive ML model when a user interacts with the system. In this paper, we identify several cognitive gaps that exist in a previously-developed interactive visual analytics (VA) system, Andromeda. These cognitive gaps that we are addressing in Andromeda are representative of common problems in other VA systems. Our goal with this work is to open both black boxes and bridge these cognitive gaps by making improvements to the original Andromeda system, including designing new visual features to help people better understand how Andromeda processes and interacts with data and improving the underlying algorithm so that the Andromeda system can better understand the intent of the user during the data exploration process. We evaluate our designs through both qualitative and quantitative analysis (i.e., user study and simulation analysis), and the results confirm that the improved Andromeda system outperforms the original version significantly in a series of high-dimensional data understanding tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Prolate-Oblate Asymmetric Shape Phase Transition in the Interacting Boson Model with SU(3) Higher-Order Interactions

Prolate-oblate shape phase transition is an interesting topic in nuclear structure, which is useful for understanding the intrinsic interactions between nucleons. Recently, the interacting boson model with SU(3) higher-order interactions was proposed, in which the prolate shape and the oblate shape are not described in a mirror symmetric way. This asymmetric description seems more realistic. The level evolutions, B(E2) values, and other important indicators showing the prolate-oblate asymmetric transitions are investigated in detail, and realistic structure evolutions from 180Hf to 200Hg are compared. A key finding is that the average deformation of the prolate shape is nearly twice the one of the oblate shape. These results, together with the successful description of the B(E2) anomaly in 168,170Os, 172Pt, the γ -soft properties of 196Pt, 82Kr, and the normal states of 110Cd, support the validity of the new model.

Wang, Tao↗
DOE OSTI2021

Electron-beam energy reconstruction for neutrino oscillation measurements

Neutrinos exist in one of three types or ‘flavours’—electron, muon and tau neutrinos—and oscillate from one flavour to another when propagating through space. This phenomena is one of the few that cannot be described using the standard model of particle physics (reviewed in ref. 1), and so its experimental study can provide new insight into the nature of our Universe (reviewed in ref. 2). Neutrinos oscillate as a function of their propagation distance (L) divided by their energy (E). Therefore, experiments extract oscillation parameters by measuring their energy distribution at different locations. As accelerator-based oscillation experiments cannot directly measure E, the interpretation of these experiments relies heavily on phenomenological models of neutrino–nucleus interactions to infer E. Here we exploit the similarity of electron–nucleus and neutrino–nucleus interactions, and use electron scattering data with known beam energies to test energy reconstruction methods and interaction models. We find that even in simple interactions where no pions are detected, only a small fraction of events reconstruct to the correct incident energy. More importantly, widely used interaction models reproduce the reconstructed energy distribution only qualitatively and the quality of the reproduction varies strongly with beam energy. This shows both the need and the pathway to improve current models to meet the requirements of next-generation, high-precision experiments such as Hyper-Kamiokande (Japan) and DUNE (USA).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive

This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research.Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery

Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Lastly, we demonstrate that the cis model may be combined with Δ-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Unsupervised Learning Based Interaction Force Model for Nonspherical Particles in Incompressible Flows

This project provides a neural network-based interaction force model for gas-solid flows from low to intermediate Reynolds numbers and concentration, which can be linked to MFiX-DEM. We have constructed a database of the interaction force between the irregular-shaped particles using a spherical harmonic method and the fluid phase based on the particle-resolved direct numerical simulation (PR-DNS) with immersed boundary-based gas kinetic scheme. Unsupervised learning method, i.e., variational auto-encoder (VAE) has been applied to extract the primitive shape factors determining the drag force, lifting forces, and torque. The interaction force model has been trained and validated with a simple but effective multi-layer feed-forward neural network: multi-layer perceptron (MLP), which will be concatenated after the encoder of the previously trained VAE for geometry feature extraction for single, irregular particles. We have trained transpose convolutional neural networks with the PR-DNS data to predict the velocity and pressure gradient of the single particle systems and utilized them to calculate drag force of multi-particle systems. This model can provide high computational efficiency because it does not require collecting multiparticle system data from PR-DNS.

99 GENERAL AND MISCELLANEOUS↗