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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Allometric scaling laws linking biomass and rooting depth vary across ontogeny and functional groups in tropical dry forest lianas and trees

There are two theories about how allocation of metabolic products occurs. The allometric biomass partitioning theory (APT) suggests that all plants follow common allometric scaling rules. The optimal partitioning theory (OPT) predicts that plants allocate more biomass to the organ capturing the most limiting resource. In this work, whole-plant harvests of mature and juvenile tropical deciduous trees, evergreen trees, and lianas and model simulations were used to address the following knowledge gaps: (1) Do mature lianas comply with the APT scaling laws or do they invest less biomass in stems compared to trees? (2) Do juveniles follow the same allocation patterns as mature individuals? (3) Is either leaf phenology or life form a predictor of rooting depth? It was found that: (1) mature lianas followed the same allometric scaling laws as trees; (2) juveniles and mature individuals do not follow the same allocation patterns; and (3) mature lianas had shallowest coarse roots and evergreen trees had the deepest. It was demonstrated that: (1) mature lianas invested proportionally similar biomass to stems as trees and not less, as expected; (2) lianas were not deeper-rooted than trees as had been previously proposed; and (3) evergreen trees had the deepest roots, which is necessary to maintain canopy during simulated dry seasons.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

EVI-Equity

EVI-Equity (Electric Vehicle Infrastructure for Equity) is a $200k project, started around in June of 2021, with a funding from the Vehicle Technologies Office (VTO). The motivation was to create a new analytical capability that can enable us to quantify and investigate equitable access to and distribution of existing and future deployment of PEVs and EVSEs in neighborhoods, cities, states, and the nation. EVI-Equity is a bottom-up equity-focused analysis model, built upon individual (synthetic) households, aggregated by census block groups. It consists of four core components - community engagement, environmental profiling, household expenditures, and network design. Although there are some commonalities, EVI-Equity is not a vehicle choice model, charging simulation model, or transportation demand model. EVI-Equity is rather a cross-cutting analysis tool, dedicated for evaluating equitable EV adoption and EVSE deployment, encompassing and bridging a wide variety of related tools, models, and frameworks. Some of the results indicate the importance of used vehicle market for low-income households. The presentation also highlights similarities and variations as to preferred public charging locations. For example, regardless of household income, retail spots are the most preferred location for public charging, followed by curbside/street. However, the results also imply that the importance of workplace charging may vary with income - the lower the income, the less important. Environmental profiling results, with an example of ground-level ozone in Atlanta area, show that the contrast between the haves and the have nots of plug-in electric vehicles depends on location. The assessment of household expenditures illustrates the economic impact of home charging access on an individual household level - the lower the income, the greater the impact is. Lastly, Denver metro area and the state of South Dakota are used to showcase the impact of different network design of charging infrastructure, as well as alternative (vs. baseline/existing) electric vehicle adoption pattern.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2026

Data for Zheng et al. (2025), "AquaMEND: Reconciling multiple impacts of salinization on soil carbon biogeochemistry"

Soil salinization, exacerbated by climate change, poses a global threat to coastal ecosystems and soil function. Salinity affects soil carbon cycling by directly impacting microbial activity and indirectly altering soil physicochemical properties, but current models inadequately represent these complexities. This dataset contains the observational and modeling data from Zheng et al. (2025), which described a process-based modeling framework that couples soil solution chemistry with microbial carbon cycling reactions to study the impacts of soil salinization. This conceptual model is implemented numerically into the open-source geochemical program PHREEQC 3.0 (Parkhurst and Appelo, 2013). This dataset consists of: - Figure2_AquaMEND_salinity_buffer: Contains model simulation outputs to assess the impact of three different cation exchange and surface complexation processes on salinity buffering (Fig. 2 from Zheng et al. 2025). - Figure3_Salinity_function: Contains salinity function fitting for literature data (Fig. 3 from Zheng et al. 2025). - Figure4_AquaMEND_microbial_mechanisms: Contains model simulation outputs for testing various microbial process-based hypotheses related to soil salinization, including microbial mortality, carbon use efficiency (CUE), extracellular enzyme activity, and other microbial mechanisms (Fig. 4 from Zheng et al. 2025). - Figure5_AquaMEND_Redox: Contains on model simulation outputs to evaluate shifts among key redox processes, such as aerobic respiration, sulfate reduction, and methanogenesis (Fig.5 from Zheng et al. 2025). - Figure6_AquaMEND_sorption: Contains on model simulation outputs for investigating the effects of salinity on dissolved organic matter (DOM) sorption and desorption processes (Fig. 6 from Zheng et al. 2025). - Figure7_AquaMEND_process_couple: Contains on model simulation outputs for exploring coupled biotic-abiotic processes and their interactions (Fig. 7 from Zheng et al. 2025). - data: Includes datasets used to develop salinity response functions and evaluate salinity buffering capacity. Datasets for MEND model calibration. - database: Contains the `.dat` file required by PHREEQC for model execution. - README.md: A Markdown plain text file describing the computational tools and directories. Files are a mixture of plain text CSV (comma-separated value) and plain text *.dat files written by the model; no special software is required to read them.

EARTH SCIENCE > AGRICULTURE > SOILS > SOIL SALINIT↗
DOE OSTI2020

Terry Turbopump Expanded Operating Band Modeling and Simulation Efforts in Fiscal Year 2020 - Progress Report

The Terry Turbine Expanded Operating Band Project is currently conducting testing at Texas A&M University as part of a revised experimental program meant to supplant previous full-scale testing plans under the headings of Milestone 5 and Milestone 6. In consultation with Sandia National Laboratories technical staff and with modeling and simulation support from the same, the hybrid Milestone 5&6 plan is moving forward with experiments aimed at addressing knowledge gaps regarding scale, working fluid, and turbopump self-regulation. Modeling and simulation efforts at Sandia National Laboratories in FY20 fell under the broad umbrella of Milestone 7 and consisted exclusively of MELCOR-related tasks aimed at: 1) Constructing/improving input models of Texas A&M University experiments, 2) Constructing a generic boiling water reactor input model according to best practices with systems-level Teny turbine capabilities, and 3) Adding code capability in order to leverage experimental data/findings, address bugs, and improve general code robustness Project impacts of the Covid-19 pandemic have fortunately been minimal thus far but are mentioned as necessary when discussing the hybrid Milestone 5&6 progress as well as the corresponding Milestone 7 modeling and simulation progress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Enhancing Modeling and Simulation for Effective Protection Strategies

This report was created by Sandia National Laboratories (SNL) to document the principles and methodology of performance data collection and integration with modeling and simulation tools to better facilitate the performance evaluation of physical protection systems (PPS). Results and conclusions from the use of modeling and simulation tools are only as good as the data employed by the tools when conducting analysis. Acquiring the performance testing data necessary to ensure effective evaluation can be a complex and sometimes daunting process. It is the desire of the organization to provide guidance that eases the burdens associated with pursuit of these objectives. This document draws heavily upon longstanding principles of systems engineering that have been developed and employed by SNL in the discipline of security since the 1970s. The scope of this document is constrained to the testing of system components and integration of data that is applicable within the context of PPS performance analysis using two tools that have been developed by SNL, PathTrace© and Scribe3D©. For guidance related to testing and evaluation more broadly, the manuals and reports referenced by this document can be consulted.

42 ENGINEERING↗
DOE OSTI2023

Efficient Optimization of Energy Recovery From Geothermal Reservoirs With Recurrent Neural Network Predictive Models

Improving the long-term energy production performance of geothermal reservoirs can be accomplished by optimizing field development and management plans. Reliable prediction models, however, are needed to evaluate and optimize the performance of the underlying reservoirs under various operation and development strategies. In traditional frameworks, physics-based simulation models are used to predict the energy production performance of geothermal reservoirs. However, detailed simulation models are not trivial to construct, require a reliable description of the reservoir conditions and properties, and entail high computational complexity. Data-driven predictive models can offer an efficient alternative for use in optimization workflows. This paper presents an optimization framework for net power generation in geothermal reservoirs using a variant of the recurrent neural network (RNN) as a data-driven predictive model. The RNN architecture is developed and trained to replace the simulation model for computationally efficient prediction of the objective function and its gradients with respect to the well control variables. The net power generation performance of the field is optimized by automatically adjusting the mass flow rate of production and injection wells over 12 years, using a gradient-based local search algorithm. Two field-scale examples are presented to investigate the performance of the developed data-driven prediction and optimization framework. Furthermore, the prediction and optimization results from the RNN model are evaluated through comparison with the results obtained by using a numerical simulation model of a real geothermal reservoir.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Modeling and Simulation to Support Current and Advanced Reactors : Thermal Hydraulics

Presentation on thermohydraulic modeling and simulation capabilities in support of current and advanced reactors. The presentation includes information on the capabilities of RELAP5-3D, MOOSE and the experimental testbed for this purpose. This presentation is intended for a Taiwan delegation that will be visiting INL in August 2024.

42 ENGINEERING↗
DOE OSTI2019

Reduction of a detailed soot model for simulations of pyrolysing solid fuels

Here, we introduce a computationally inexpensive soot formation model for use in simulating large solid-complex fuel systems. The proposed model evaluates three variables: number density of tars, bulk mass density of soot particles, and particle number density. Each of these variables are influenced by soot formation phenomena, and this model evaluates the most common of these phenomena: soot nucleation, surface reactions, and coagulation. Two separate simulations were carried out using this model and results were compared against simulations using a more detailed and computationally expensive model. Findings are promising and show a meaningful reduction in model complexity and computational cost while still giving comparable results.

58 GEOSCIENCES↗
DOE OSTI2020

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗
DOE OSTI2020

Diagnosis of convective organization and cold pools using ARM datasets and evaluation of a unified convection parameterization (UNICON)

Tropical thunderstorms often cluster together. Studies have suggested that the degree to which the tropical thunderstorms are clustered impacts Earth's energy balance and water cycle, as well as extreme precipitation events. However, the processes controlling the spatial distribution of the thunderstorms are poorly understood and are not properly represented in most computer models for weather and climate prediction. Under the goals of better understanding how convection organizes at the mesoscale and advancing the representation of mesoscale convective organization in global climate models, we i) objectively quantified the degrees of convective organization and diagnosed cold pool processes using ARM field campaign observations, ii) examined the organization processes in storm-resolving model simulations, and iii) evaluated the impacts of mesoscale convective organization in global model simulations. The project yielded a firm reference against which the global model representation of mesoscale convective organization and cold pools can be evaluated against and shed new light into the role of parameterized convective organization in the global model simulation of the basic state and variability. Our results revealed two distinct phases of convective clustering during the two-day rain episodes (Cheng et al. 2018) and a new mechanism through which vertical wind shear in the low-troposphere can aid convective organization over tropical oceans (Cheng et al. 2020). It was demonstrated that the interactive representation of cold pools and mesoscale convective organization is key for global models to successfully simulate both the mean state and intraseasonal variability in the tropics (Ahn et al. 2019; 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dynamic modeling and simulation of pressure swing adsorption processes using toPSAil

Pressure swing adsorption (PSA) has attracted significant recent interest for chemical process intensification due to its potential for high energy efficiency and amenability to small, modular designs. However, the lack of simulation tools that are readily available, transparent, and trusted, has been identified as a serious impediment to widespread adoption of PSA, as well as to further research on PSA modeling, numerical solution, optimization, and control. This paper presents a complete framework for dynamic modeling and simulation of PSA processes and its implementation in an open-source simulator called toPSAil. Further, the presentation is tutorial and includes many modeling and implementation details often overlooked in existing literature. Novel methods are presented for handling flow reversals and implementing various pressure–flow relationships, along with controlled boundary conditions. Finally, the code contains several innovations designed to improve efficiency and reduce the extensive trial-and-error tuning often required to produce a working PSA cycle.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

EGS Collab: Modeling and Simulation Working Group Teleconference Series (129-130)

This submission contains the presentation slides and recordings from EGS Collab Modeling and Simulation Working Group (MSWG) teleconferences number 129 through 130. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

A New Efficient Quantitative Multi-component Phase Field: Lattice Boltzmann Model for Simulating Ti6Al4V Solidified Dendrite Under Forced Flow

Ti6Al4V is a widely used, multi-component alloy in additive manufacturing, during which the fluid flow in the molten pool significantly affects the solidified dendrites. To predict and further control the microstructure, modeling and simulating the microstructure evolution play a critical role. In this study, a newly developed, efficient, quantitative multi-component phase-field (PF) model is coupled with a lattice Boltzmann (LB) model to simulate Ti6Al4V solidified dendrite evolution under fluid flow. The accuracy and convergence behavior of the model is validated by the Gibbs–Thomson relation at the dendrite tip. Single and multiple two-dimensional (2D) equiaxed dendrite evolution cases under forced flow were simulated. Results show that the dendrite pattern is influenced remarkably by the fluid flow. Underlying mechanisms of the asymmetrical evolution are revealed by discussing the interaction among the flow, composition distribution and dendrite morphology, quantitatively. The dendrite kinetics are also derived, which ascertains the relationship between tip velocity and undercooling and inlet velocity and is the foundation for larger-scale simulation. We believe that the coupled quantitative multi-component PF–LB framework employed in this study helps in investigating the solidified dendrite morphology evolution in a deep and quantitate manner.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Improved pore network models to simulate single-phase flow in porous media by coupling with lattice Boltzmann method

In this paper, different pore network models to simulate single-phase flow in porous media are built and their accuracy is evaluated. In addition to the conventional pore network model (CPNM) which consists of regular pore bodies and throat bonds, three improved pore network models (IPNMs) are developed allowing to better describing the real pore and throat geometry. The first improved pore network model (IPNM1) replaces the regular throat bond with a throat bond showing the real throat cross section. The second improvement (IPNM2) uses a series of sub-throat bonds with varying cross sections to better describe the real throat geometry, which is firstly proposed in this paper. The third model (IPNM3) extracts the real pore-throat-pore geometry without simplification. The conductance of fluid flow through these more realistic throat bonds is calculated by the lattice Boltzmann method (LBM). The accuracy and computational efficiency of the different pore network models are evaluated taking the LBM simulation over the whole porous medium as reference solution. The global permeability and detailed pressure distributions in the pores for the different pore network models are validated. The results show that the accuracy of the pore network model increases from CPNM to IPNM3, but at the expense of increasing computational cost. This study suggests that IPNM3 can replace a whole-domain LBM simulation with similar accuracy but much lower computational cost. As a first-order approximation the newly proposed IPNM2 is suggested as good compromise between accuracy and computational cost.

42 ENGINEERING↗
DOE OSTI2022

A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model

Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper and simpler ways to describe the dynamics of these systems but are inexact and introduce additional approximation errors. In order to overcome the computational difficulties of the full complex models, on one hand, and the limitations of surrogate models, on the other, this work proposes a new accelerated time-stepping strategy that combines information from both. This approach is based on the multirate infinitesimal general-structure additive Runge--Kutta framework. The inexpensive surrogate model is integrated with a small time step to guide the solution trajectory, and the full model is treated with a large time step to occasionally correct for the surrogate model error and ensure convergence. Here, we provide a theoretical error analysis, and several numerical experiments, to show that this approach can be significantly more efficient than using only the full or only the surrogate model for the integration.

Surrogate models↗
DOE OSTI2022

EGS Collab: Modeling and Simulation Working Group Teleconference Series (99-128)

This submission contains the presentation slides and recordings from EGS Collab Modeling and Simulation Working Group (MSWG) teleconferences number 99 through 128. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.

15 GEOTHERMAL ENERGY↗