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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Wind Energy Modeling and Simulation, Volume 2: Turbine and System

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation. Volume 2 covers turbine level aerodynamics, aeroelasticity, rotors drivetrains and electrical systems, wind turbine control, offshore foundations, system optimization, and grid modelling.

17 WIND ENERGY↗
DOE OSTI2015

How to decide between competing efficiency droop models for GaN-based light-emitting diodes

GaN-based light-emitting diodes (LEDs) exhibit a strong efficiency droop with higher current injection, which has been mainly attributed to Auger recombination and electron leakage, respectively. Thus far, the few reports on direct measurements of these two processes do not confirm their dominating influence on the droop unambiguously. Advanced numerical simulations of experimental characteristics are shown to validate one or the other explanation by variation of uncertain material parameters. We finally demonstrate how the comparative simulation of temperature effects enables a clear distinction between both models. Contrary to common assumptions, the consistently measured efficiency reduction of blue LEDs with higher ambient temperature eliminates electron leakage as primary cause of the efficiency droop in these devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Synchronous Machine Governor Upgrade

Conventional generation sources play a critical role in the stability and reliability of the electrical grid, particularly as we transition towards more renewable energy sources. To understand and accurately emulate their behavior for optimizing grid operations and ensuring seamless integration with renewable technologies, it is essential to better emulate the grid- and plant-level impacts of conventional generation sources, such as natural gas (NG) driven heat recovery steam generators (HRSGs) and combustion turbines (CTs). Therefore, a governor model is developed in a programmable logic controller (PLC) to investigate the performance of the conventional generator under various dynamic operating conditions and to identify the impact on grid stability in a controlled environment. The governor model aims to enable the hardware-in-the-loop (HIL) based emulation of these conventional generation sources using the existing 2 MVA synchronous machine/generator that is driven by a flexible 2.5 MW variable speed drive. This setup will allow us to replicate the dynamic characteristics and response behaviors of NG-driven HRSGs and CTs. The controls for the emulated conventional plants follow the industry standard and are adjustable, ensuring they accurately reflect the operational capabilities and limitations of real-world systems. These controls include load-following capabilities, ramp rates, startup and shutdown sequences, and emissions characteristics. By incorporating these adjustable controls, we aim to capture the nuanced impacts of conventional generation, such as their ability to provide ancillary services like frequency regulation, voltage support, and spinning reserve. In this report, we simulate two types of dynamic operations: grid-connected and islanding. For each dynamic operation, representative starting sequences are tested, including turbine purge, ignition, speed ramping up, generator excitation and synchronizing, and breaker close. The HIL based tests provides insights for field deployment, specifically the high-fidelity governor model provides results to predict the potential stability and reliability risk and suggest possible integration measures (e.g., generation and load balancing, tuning of governor control parameters). Ultimately, this enhanced emulation capability will be integrated into our Advanced Research on Integrated Energy Systems (ARIES), enabling us to conduct comprehensive studies on the interactions between conventional and renewable energy sources. By better understanding these interactions, we can develop strategies to optimize the overall performance and reliability of the grid. This will support the deployment of advanced grid management techniques, such as demand response, grid-forming inverters, and energy storage systems. The main contributions are summarized as follows: (1) This report introduces a PLC-based governor model for gas turbines. This model accurately simulates the dynamic behavior of conventional generation sources under various operational scenarios; (2) The model is integrated with an HIL testbed that includes a 2.5 MW variable speed drive and a 2 MVA synchronous machine. This setup enables realistic, real-time emulation of conventional power plants, particularly NG driven HRSGs and CTs; (3) The developed model is adaptable to various gas turbine configurations and allows for precise control over parameters such as MW ramp rates. This flexibility makes it a valuable tool for future research and industry collaboration; and (4) By incorporating the model into the National Renewable Energy Laboratory's Advanced Research on Integrated Energy Systems, the report lays the groundwork for future studies on interactions between conventional and renewable energy sources, enhancing the ability to develop advanced grid management strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Phase Behavior and Salt Partitioning in Polyelectrolyte Complex Coacervates

Polyelectrolyte complexes are omnipresent both in nature and in the technological world, including nucleotide condensates, biological marine adhesives, food stabilizers, encapsulants, and carriers for gene therapy. However, the true phase behavior of complexes, resulting from associative phase separation of oppositely charged polyelectrolytes, remains poorly understood. Here in this study, we rely on complementary experimental and simulation approaches to create a complete quantitative description of the phase behavior of polyelectrolyte complexes that represents a significant advance in our understanding of the underlying physics of polyelectrolyte complexation. Experiments employing multiple approaches with model polyelectrolytes oppositely charged polypeptides poly(L-lysine) and poly(D,Lglutamic acid) of matched chain lengths led to phase diagrams with compositions of the complex and the supernatant that were in excellent agreement with simulation results. Contrary to the widely accepted theory for complexation, we found preferential partitioning of salt ions into the supernatant phase. Additionally, the salt partitioning into the supernatant phase was found to initially increase and then decrease on increasing the salt concentrations, manifesting as a distinct minimum in the salt partition coefficients. These trends were shown by simulations to be strongly influenced by the excluded volume interactions in the complex phase, which were not accounted for in their entirety in earlier theories. Finally, we believe the comprehensive data we present will be conducive to the development of an accurate physical theory for polyelectrolyte complexation with predictive capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Feasibility Studies on a Hexagonal-Lattice Core for a World-Class Cold Neutron Source

Cold neutrons have kinetic energies less than 5 meV and wavelengths greater than 4 Α. They can be transported over tens of meters through super-reflecting neutron guides with minimal losses. Cold neutrons are used to investigate the structure of larger molecular systems such as biological materials, polymers and aggregates, due to their preferential interaction with light elements and general ignorance towards heavy ones. Intense beams of cold neutrons are obtained from slowing down a source of thermal energy neutrons - which may be from either a research reactor or spallation neutron source - through some cryogenic moderator such as liquid hydrogen or deuterium. The present reactor at the NIST Center for Neutron Research (NCNR) - NBSR - is such a cold neutron source (CNS) facility which provides cold neutron beams for over 2,000 scientists performing various experiments annually. Feasibility studies on a replacement reactor for the NBSR are underway at NCNR with the primary purpose of optimized cold neutron production. Low enriched uranium (LEU) fuel is required for the new reactor to conform to nuclear non-proliferation agreements. The low thermal power rate of 20 MW is taken as a priority with a fuel cycle length designated as 30 days. A horizontal split compact core with a large D{sub 2}O reflector tank is currently proposed and studied, with the expectation of achieving much better cold neutron performance than the present NIST reactor (NBSR). A preliminary core design has been completed using MCNP6 modeling and simulation. The physics performance characteristics of the proposed new core indicate that the new design is competitive with the most notable existing advanced cold neutron sources. In previous studies, only rectangular shaped fuel elements were considered because they have similar external dimensions as the NBSR fuel elements. However, from a compact core standpoint, a square lattice is not the most effective geometry for a tight configuration. Moreover, the previous studies also showed that the square lattice of the fuel elements left little room to accommodate vertical insertion blade-type control elements. As a result, all control blades had to reside in the D{sub 2}O reflector, which made the design cumbersome (in that case the control blades must penetrate the reflector tank) and neutronically uneconomical. In this summary, a preliminary feasibility study on a hexagonal core lattice is performed to render an alternative geometry for the new reactor (HEX core) to provide sufficient space for the control elements. The identical LEU fuel (U{sub 3}Si{sub 2}/Al) with U-235 enrichment 19.75% is used in the new design. The maximum thermal power and fuel management scheme remain the same as the previous investigations (three 30-day cycled batches for each batch of three fuel elements). The horizontal split compact core concept is also applied in the hexagonal lattice core design, with the objective of creating maximum thermal flux traps between the split core halves. A detailed description of the model for the hexagonal lattice core is presented in the following section. The HEX core design achieves a number of successes over the previous split-core design. Given that much of the motivation of this work was based on implementation of control blades into a concept which lacked them, it is notable that neutron economy was well-preserved in the process of achieving conservatively effective reactivity control; start-up k{sub eff} was in fact increased by 0.23% and peak thermal flux increased by ∼25%. A useful metric in determining the efficacy of resources towards creating an environment for the production of cold neutrons - dubbed the 'quality factor' (QF) - is the ratio of peak thermal neutron flux in a system to the total thermal power produced. Because HEX core's objectives are very focused in comparison to existing research reactors, it is fitting to pursue a design which out-performs all others in terms of some specific performance metric, namely this QF term. While HFIR remains the greatest cold neutron source in the world, it does so through use of HEU fuel (which will be replaced with LEU in the near future), and suffers a greater level of fast neutron contamination that is expected from the HEX design. The new core design achieves these successes due to the hexagonal lattice fuel arrangement, a 13.5% reduction in cladding mass, and a 13.1% reduction in fuel element volume. In spite of a 5.5% reduction in fuel mass, no compromises in reactor performance were noted. Provided the favorable characteristics shown of the control blade design, refueling intervals longer than the prescribed 30 days may be considered. It can be speculated that with these quantitative benefits, the overall capital and continuous costs of the HEX core design will be less than the previously proposed split-core concept.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Advanced simulation capability for environmental management, integrated tool-sets and simulator to enhance public communication - 15186

The U.S. Department of Energy (US DOE) Office of Environmental Management (EM), Office of Soil and Groundwater Remediation, is supporting multi-laboratory development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is an open source and modular computing framework that incorporates new advances and tools for predicting contaminant fate and transport in natural and engineered systems. ASCEM facilitates transparent, efficient and integrated approaches to modeling and site characterization, and provides robust and standardized assessments of performance and risk for EM cleanup and closure activities. Site subsurface simulation modeling and performance assessments are complex and involve sensitivity evaluations and uncertainty quantification to better understand the risks at a site. The ASCEM tool-set was designed in close collaboration with the sites and other potential end-users (both the public and regulators) to allow better communication and understanding of these complex modeling efforts. ASCEM's integrated tool-sets provide a user interface and tools for managing work-flows, including conceptual model development, management of data and metadata for model input, sensitivity analysis, model calibration and uncertainty analysis, model execution on diverse computational platforms, and processing of model output, including visualization. The hierarchical and modular design of ASCEM HPC simulator Amanzi easily lends itself to translating this conceptual model of a site into a numerical model that can easily be spatially and temporally discretized to run on the HPC simulator. Together this integrated work-flow allows for a seamless and integrated analysis tracking all provenance information and modeling uncertainties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

iTOUGH2-EOS1SC. Multiphase Reservoir Simulator for Water under Sub- and Supercritical Conditions. User's Guide

Supercritical fluids exist near magmatic heat sources in geothermal reservoirs, and the high enthalpy fluid is becoming more desirable for energy production with advancing technology. In geothermal modeling, the roots of the geothermal systems are normally avoided but in order to accurately predict the thermal behavior when wells are drilled close to magmatic intrusions, it is necessary to incorporate the heat sources into the modeling scheme. Modeling supercritical conditions poses a variety of challenges due to the large gradients in fluid properties near the critical zone. This work focused on using the iTOUGH2 simulator to model the extreme temperature and pressure conditions in magmatic geothermal systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Radiocesium interaction with clay minerals: Theory and simulation advances Post–Fukushima

Insights at the microscopic level of the process of radiocesium adsorption and interaction with clay mineral particles have improved substantially over the past several years, triggered by pressing social issues such as management of huge amounts of waste soil accumulated after the Fukushima Dai-ichi nuclear power plant accident. In particular, computer-based molecular modeling supported by advanced hardware and algorithms has proven to be a powerful approach. Its application can now generally encompass the full complexity of clay particle adsorption sites from basal surfaces to interlayers with inserted water molecules, to edges including fresh and weathered frayed ones. On the other hand, its methodological schemes are now varied from traditional force-field molecular dynamics on large-scale realizations composed of many thousands of atoms including water molecules to first-principles methods on smaller models in rather exacting fashion. In this article, we overview new understanding enabled by simulations across methodological variations, focusing on recent insights that connect with experimental observations, namely: 1) the energy scale for cesium adsorption on the basal surface, 2) progress in understanding the structure of clay edges, which is difficult to probe experimentally, 3) cesium adsorption properties at hydrated interlayer sites, 4) the importance of the size relationship between the ionic radius of cesium and the interlayer distance at frayed edge sites, 5) the migration of cesium into deep interlayer sites, and 6) the effects of nuclear decay of radiocesium. Key experimental observations that motivate these simulation advances are also summarized. Furthermore, some directions toward future solutions of waste soil management are discussed based on the obtained microscopic insights.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗

Abstract Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO 3 and SrTiO 3 , using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

42 ENGINEERING↗
DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗
DOE OSTI2025

Advancing Understanding of Deep Convective Anvil Clouds

In this project we use ground-based DOE ARM observations, satellite observations, and Weather Research and Forecasting (WRF) model simulations to arrive at a more complete understanding (or, new theory) of how atmospheric heating profiles in thunderstorm cores drives how quickly air rises in the storms. The rate at which air motion increases or decreases with altitude directly relates to how much cloud mass is ejected laterally into the adjacent atmosphere, which then influences the development and maintenance of “stratiform raining cloud anvils”. Our work has led to new equations connecting the heating in these thunderstorms to these stratiform clouds, with the connection modulated by the stability structure of the atmosphere (i.e., if more stable, then a more rapid change in thunderstorm heating with altitude results in more stratiform cloud, independent of whether over land or ocean, or how organized the thunderstorms are). Improving knowledge of stratiform clouds is important, since they contribute to substantial accumulated rainfall (important to agriculture and local hydrology) as well as radiation, given their large areal extents and lifetime. The radiation impacts likely impact simulated cloud feedback in all Earth System models (ESMs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advanced CO 2 Capture Solvent Systems for Dynamic Power Generation: Quarterly Research Performance Progress Report, QR4 (Q4FY24)

We developed an integrated Computational Fluid Dynamics (CFD) model to simulate the multi-physics coupled cooling process of mixed gas by cold water within a Direct Contact Cooler (DCC) equipped with a rotating packing bed (RPB). The model captures the interactions between fluid dynamics, heat transfer, mass transport, and phase transitions, while accounting for key operational variables such as RPB rotational speed and the mass flow rates of both liquid and gas. The CFD model has been validated using experimental data, specifically by comparing predicted outflow gas and liquid temperatures to measured results. Our findings demonstrate the significant effects of RPB rotational speed and mass flow rates on cooling performance, providing valuable insights for optimizing DCC efficiency in industrial applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

“Gray Zone” Simulations Using a Three-Dimensional Planetary Boundary Layer Parameterization in the Weather Research and Forecasting Model

Abstract Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δ x ) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δ x ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional mesoscale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. Significance Statement The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface–atmosphere interaction studies.

Juliano, Timothy W.↗
DOE OSTI2026

From Vehicles to Systems: Understanding Freight Transportation as a Connected Energy, Infrastructure, and Operations System

The U.S. freight system may need to handle 50% more cargo by 2050. Upgrading our freight system requires modernizing capital-intensive, long-lived assets including freight trains, ports, and terminal infrastructure. NLR is advancing freight system solutions spanning ALTRIOS, the first digital twin for the full freight rail system; ALTRIOS-LIFTS, which can create digital twins of freight terminals; INFORMES, the first national model of the intermodal freight system; MARINESim, used to simulate and optimize ocean-going vessel operations; and more. These modeling and simulation tools enable data-driven decision-making across freight modes and systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Virtual Volumetric Additive Manufacturing (VirtualVAM)

Abstract Tomographic volumetric additive manufacturing (VAM) produces arbitrary 3D geometries by exposure of a rotating volume of photopolymer resin to tomographically‐patterned illumination. This enables high speed, layer‐less printing of parts from a wide range of photopolymers not amenable to layer‐by‐layer processes. Since the entire geometry is produced at once over the course of a few seconds to minutes, molecular diffusion length scales become significant to the printing process. Understanding these molecular reaction and diffusion processes is imperative for advancing VAM to a usable technology. These processes are experimentally very difficult to monitor and measure. Herein, VirtualVAM ‐ a simulation framework for modeling the tomographic VAM process, is developed and experimentally validated. VirtualVAM simulates reaction, diffusion, and heat generation processes over the course of a print with single‐voxel resolution. From a few experimentally‐determined input parameters and a set of images for projection, VirtualVAM is able to generate a large spatio‐temporal data set for any given tomographic VAM print. Using VirtualVAM, a number of experimentally‐unattainable aspects of the VAM process are investigated such as single‐voxel conversion profiles, effect of molecular oxygen, and stopping time determination. VirtualVAM also enables the optimization of exposure patterns to further improve contrast between in‐part and out‐of‐part delivered dose.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Megahertz Rate Optical Diagnostics of Explosively Generated Soot

Detonation of a solid explosive produces a turbulent and luminous post‐detonation fireball containing condensed carbon soot. Diagnostics of soot dynamics are needed for model validation and to interpret emission signals. Diffuse back‐illumination extinction imaging (DBI‐EI) and laser‐induced incandescence (LII) are two common optical diagnostics for flame soot. This work extends both to measure time‐resolved soot dynamics from a 12 mm HMX hemisphere. DBI‐EI measured line‐of‐sight optical extinction exceeds 99% in some regions. Results are tomographically inverted to obtain a signal proportional to the soot volume fraction. Pulse‐burst LII measures are presented at 1 MHz. For the first time, LII results are combined with DBI‐EI extinction measurements to quantify and correct for signal trapping. Following this, spatially and temporally resolved DBI‐EI and LII measures are shown to be in reasonable agreement. Finally, experimental results are compared against recently developed simulations. Quantitative differences in the soot mixing dynamics are resolved. These findings motivate future model improvements and demonstrate ongoing needs for diagnostic advancements for heavily sooting environments.

diffuse back-illumination extinction imaging↗
DOE OSTI2023

Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets

Recent advances in modeling large-scale, complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational resources. Similarly, acquiring experimental datasets can prove difficult. For these systems, often computationally inexpensive, but in general inaccurate models, known as the low-fidelity models, are available. Here in this paper, we propose a bi-fidelity modeling approach for complex physical systems, where we model the discrepancy between the true system's response and a low-fidelity response in the presence of a small training dataset from the true system's response using a deep operator network, a neural network architecture suitable for approximating nonlinear operators. We apply the approach to systems that have parametric uncertainty and are partially unknown. Three numerical examples are used to show the efficacy of the proposed approach to model uncertain and partially unknown physical systems.

MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enhancing carbon storage through proactively managing fire-prone coniferous mountain forests

Mountain ecosystems typically serve as carbon (C) sinks. However, studies also suggest that they could be C sources due to climate warming, drought and insect-related mortality, wildfires, and management actions. We applied the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), a process-based dynamic vegetation-ecosystem model, to investigate the role of ecosystem management in C storage under Mediterranean climate over the 21 st century. We modified LPJ-GUESS to include implementing mechanical thinning by vegetation size classes, components, and types along with a new mechanistic fire-occurrence model that accounts for wind speed and lightning ignition. Simulations show that mechanical thinning or prescribed fire performed 5-20 years in advance of a high-severity wildfire reduced direct wildfire C emissions by 38-66 %. Our results also show that long-term management actions repeated every 5-20 years, including thinning relatively small trees (diameters up to 7 inches or ∼178 mm), can maintain stable C levels in the forest and lower dead-fuel amounts. We found that, although prescribed fire mitigated wildfire severity, ecosystem C storage from reduced wildfire emissions can be outweighed by the added emissions from the prescribed fire themselves. Thinning plus removing and sequestering the thinned biomass can ensure that forests act as net C sinks through the end of the 21 st century. However, addition of prescribed fire is needed to reduce understory and lower the projected extent of high-severity wildfire. Achieving the competing goals of reducing wildfire and making the Sierra Nevada long-term C sink can be advanced through carefully coordinated thinning, sequestration of thinned biomass, and prescribed fire.

Climate solutions↗