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All sources NASA NTRS DOE OSTI

DOE OSTI2025

High resolution characterization of soil dissolved organic matter with FTICR-MS (Fourier-transform ion cyclotron resonance mass spectrometry) from soil samples in control and warming plots in Blodgett Forest, CA (2014 and 2018)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A quasi-static particle-in-cell algorithm based on an azimuthal Fourier decomposition for highly efficient simulations of plasma-based acceleration: QPAD

The three-dimensional (3D) quasi-static particle-in-cell (PIC) algorithm is a very efficient method for modeling short-pulse laser or relativistic charged particle beam–plasma interactions. In this algorithm, the plasma response, i.e., plasma wave wake, to a non-evolving laser or particle beam is calculated using a set of Maxwell’s equations based on the quasi-static approximate equations that exclude radiation. The plasma fields are then used to advance the laser or beam forward using a large time step. The algorithm is many orders of magnitude faster than a 3D fully explicit relativistic electromagnetic PIC algorithm. It has been shown to be capable to accurately model the evolution of lasers and particle beams in a variety of scenarios. Additionally, at the same time, an algorithm in which the fields, currents and Maxwell equations are decomposed into azimuthal harmonics has been shown to reduce the algorithmic complexity of a 3D explicit PIC algorithm to that of a 2D algorithm when the expansion is truncated while maintaining accuracy for problems with near azimuthal symmetry. This hybrid algorithm uses a PIC description in r–z and a gridless description in . We describe a novel method that combines the quasi-static and hybrid PIC methods. This algorithm expands the fields, charge and current density into azimuthal harmonics. A set of the quasi-static field equations is derived for each harmonic. The complex amplitudes of the fields are then solved using the finite difference method. The beam and plasma particles are advanced in Cartesian coordinates using the total fields. Details on how this algorithm was implemented using a similar workflow to an existing quasi-static code, QuickPIC, are presented. The new code is called QPAD for QuickPIC with Azimuthal Decomposition. Benchmarks and comparisons between a fully 3D explicit PIC code (OSIRIS), a full 3D quasi-static code (QuickPIC), and the new quasi-static PIC code with azimuthal decomposition (QPAD) are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

A Novel Partitioned Approach for Reduced Order Model—Finite Element Model (ROM-FEM) and ROM-ROM Coupling

Partitioned methods allow one to build a simulation capability for coupled problems by reusing existing single-component codes. In so doing, partitioned methods can shorten code development and validation times for multiphysics and multiscale applications. In this work, we consider a scenario in which one or more of the “codes” being coupled are projection-based reduced order models (ROMs), introduced to lower the computational cost associated with a particular component. We simulate this scenario by considering a model interface problem that is discretized independently on two non-overlapping subdomains. Here we then formulate a partitioned scheme for this problem that allows the coupling between a ROM “code” for one of the subdomains with a finite element model (FEM) or ROM “code” for the other subdomain. The ROM “codes” are constructed by performing proper orthogonal decomposition (POD) on a snapshot ensemble to obtain a low-dimensional reduced order basis, followed by a Galerkin projection onto this basis. The ROM and/or FEM “codes” on each subdomain are then coupled using a Lagrange multiplier representing the interface flux. To partition the resulting monolithic problem, we first eliminate the flux through a dual Schur complement. Application of an explicit time integration scheme to the transformed monolithic problem decouples the subdomain equations, allowing their independent solution for the next time step. We show numerical results that demonstrate the proposed method’s efficacy in achieving both ROM-FEM and ROM-ROM coupling.

de Castro, Amy↗

DOE OSTI2024

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. Furthermore, the proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

13 HYDRO ENERGY↗

DOE OSTI2022

Complete Initial Evaluation of Novel Complexants for Tc Holdback for Simplified Single Cycle Separations.

Management of technetium at the back end of nuclear fuel cycle is complicated by its unique physico-chemical character, but must be addressed to due to the environmental, storage and reprocessing challenges of this fission product. In a single-cycle scenario, the recovery and purification of uranium is particularly challenging due to the tendency of a pertechnetate anion to follow uranyl cation. This report summarizes initial studies directed towards finding the solution for technetium management. Two options for controlling technetium’s chemistry in solvent extraction scenarios were studied. Molecular recognition of pertechnetate by aqueous complexants based on guanidinium moieties was compared to redox manipulation of technetium using dihydrazide reagents. Solvent extraction, potentiometric and nuclear magnetic resonance spectroscopy studies were performed. The influence of guanidinium reagents on the liquid-liquid partitioning of technetium is less pronounced, relative to dihydrazides. Strong impact exerted by dihydrazides originates from the technetium-catalyzed decomposition of such compounds which reduces technetium to a tetravalent, non-extractable state. Although this route of technetium management is very effective the destruction of dihydrazides is undesirable. Guanidinium complexants show a more tempered influence on technetium. The effect on the liquid-liquid partitioning of pertechnetate is evident and likely guided by anion recognition due to ion-pair and hydrogen bond formation. Guanidiniums are stable in presence of technetium as evidenced by nuclear magnetic resonance studies. The steady state chemistry of guanidiniums identify this class of aqueous complexants as solid candidates for structure-function pertechnetate recognition studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Interactive Mesh Generation and Comparison Tools for Nuclear Energy Advanced Modeling & Simulation Phase I. Final Report

The promise of computer simulations for nuclear reactor designs is the opportunity to speed up the design process while also improving safety, providing more detail and higher fidelity, reducing cost, minimizing schedule risk, and avoiding many other potential problems; because simulations have the potential to be much less expensive and time-consuming than traditional development – where scale models and physical tests require construction and long lead times – it is possible to consider more alternatives and complete more comprehensive assessments before the design is finalized and constructed. As more design scenarios are studied by simulation, the speed that simulations promise is only available as long as bottlenecks are addressed for these scenarios. A frequent bottleneck is the amount of human labor required to describe the shape of the objects being designed and decompose the shape into simple elements, such as hexahedra, that are small enough to capture the physical phenomena of interest without being so small that even fast computers cannot perform the simulation in a reasonable amount of time. Recently, techniques for creating all-hexahedral decompositions have been developed, but they require some human input. Many simulation codes prefer all-hexahedral decompositions, so we proposed to evaluate the feasibility of these techniques on nuclear reactor geometries to see whether they required too much human input to be commercially viable. Our study concluded that they can be made viable with some additional software tools to reduce the amount of user input required.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Comparative Techno-economic Analysis and Life Cycle Assessment of Producing High-Value Chemicals and Fuels from Waste Plastic via Conventional Pyrolysis and Thermal Oxo-degradation

The rapid rise in global plastic production in recent decades has resulted in the massive generation of plastic waste. Over 75% of the plastic waste generated in the United States was sent to landfills, with a meager 8.7% recycled. Plastics are valuable feedstocks for platform chemicals and fuels. Chemical upcycling of waste high-density polyethylene (HDPE) is gaining more attention as a potentially feasible and environmentally friendly plastic waste management technology. Conventional pyrolysis (CPY) and thermal oxo-degradation (TOD) are two chemical upcycling technologies actively researched for decomposing waste HDPE into valuable chemicals and fuels. However, there are few studies on the techno-economic analysis (TEA) and life cycle assessment (LCA) of these technologies for converting waste HDPE to valuable products. This study conducts a comparative TEA and LCA study of the thermochemical decomposition of waste HDPE to produce gaseous (ethylene and propylene) and liquid (naphtha, diesel, and wax) products by CPY and TOD. The study elucidates and compares the impact of hydrocracking longer chain hydrocarbons to produce more valuable products on the TEA and LCA. The TEA showed that the fixed capital investment could range from $\$32.5$ million for TOD without hydrocracking to $\$244$ million for CPY with hydrocracking scenarios. Annual revenues range from $\$28.1$ million to $\$71.5$ million in favor of scenarios with hydrocracking. However, the net present value ranges from $\$1.4$ million to $\$265.8$ million in favor of scenarios without hydrocracking. Sensitivity analysis showed that fixed capital cost, facility capacity, and product prices have the biggest impact on the process economics of the facilities, while utilities and waste transportation to refineries have the biggest impact on environmental impacts. Here, the LCA showed that primary products from scenarios without hydrocracking can be more environmentally friendly than virgin products from petroleum processes. However, TOD and CPY with hydrocracking primary products have more emissions than those of virgin products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Quantitative Analysis of Origin of Lithium Inventory Loss and Interface Evolution over Extended Fast Charge Aging in Li Ion Batteries

During the extreme fast charging (XFC) of lithium-ion batteries, lithium inventory loss (LLI) and reaction mechanisms at the anode/electrolyte interface are crucial factors in performance and safety. Determining the causes of LLI and quantifying them remain an essential challenge. We present mechanistic research on the evolution and interactions of aging mechanisms at the anode/electrolyte interface. We used NMC 532 /graphite pouch cells charged at rates of 1, 6, and 9 C up to 1000 cycles for our investigation. The cell components were characterized after cycling using electrochemical measurements, inductively coupled plasma optical emission spectroscopy, 7 Li solid-state nuclear magnetic resonance spectroscopy, and high-performance liquid chromatography/mass spectrometry. The results indicate that cells charged at 1 C exhibit no Li plating, and the increase of SEI thickness is the dominant source of the Li loss. In contrast, Li loss in cells charged at 9 C is related to the formation of the metallic plating layers (42%) the SEI layer (38.1%) and irreversible intercalation into the bulk graphite (19%). XPS analysis suggests that the charging rate has little influence on the evolution of SEI composition. The interactions between competing aging mechanisms were evaluated by a correlation analysis. In conclusion, the quantitative method established in this work provides a comprehensive analytical framework for understanding the synergistic coupling of anodic degradation mechanisms, forecasting SEI failure scenarios, and assessing the XFC lithium-ion battery capacity fade.

25 ENERGY STORAGE↗

DOE OSTI2024

Informative and non-informative decomposition of turbulent flow fields

Not all the information in a turbulent field is relevant for understanding particular regions or variables in the flow. Here, we present a method for decomposing a source field into its informative Φ I (x, t) and residual Φ R (x, t) components relative to another target field. The method is referred to as informative and non-informative decomposition (IND). All the necessary information for physical understanding, reduced-order modelling and control of the target variable is contained in Φ I (x, t), whereas Φ R (x, t) offers no substantial utility in these contexts. The decomposition is formulated as an optimisation problem that seeks to maximise the time-lagged mutual information of the informative component with the target variable while minimising the mutual information with the residual component. The method is applied to extract the informative and residual components of the velocity field in a turbulent channel flow, using the wall shear stress as the target variable. We demonstrate the utility of IND in three scenarios: (i) physical insight into the effect of the velocity fluctuations on the wall shear stress; (ii) prediction of the wall shear stress using velocities far from the wall; and (iii) development of control strategies for drag reduction in a turbulent channel flow using opposition control. In case (i), IND reveals that the informative velocity related to wall shear stress consists of wall-attached high- and low-velocity streaks, collocated with regions of vertical motions and weak spanwise velocity. This informative structure is embedded within a larger-scale streak–roll structure of residual velocity, which bears no information about the wall shear stress. In case (ii), the best-performing model for predicting wall shear stress is a convolutional neural network that uses the informative component of the velocity as input, while the residual velocity component provides no predictive capabilities. Finally, in case (iii), we demonstrate that the informative component of the wall-normal velocity is closely linked to the observability of the target variable and holds the essential information needed to develop successful control strategies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Risk-averse optimization for resilience enhancement of complex engineering systems under uncertainties

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system and therefore improving the system’s resilience is an important yet challenging task. Here, to ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, the CVaR risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique i.e. modified Lagrangian dual decomposition, is also employed to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

42 ENGINEERING↗

DOE OSTI2026

Random 3D Interpenetrating Electrode Design for Energy Storage Applications

We introduce the concept of random interpenetrating electrode structures, inspired by spinodal decomposition, as a transformative approach for energy storage applications. While ordered architectures, such as double gyroids and Kelvin cell structures, have long been favored for their structural uniformity and surface area utilization, we demonstrate that random structures offer distinct advantages including shorter diffusion pathways, higher packing density, and enhanced reaction kinetics, particularly at low temperatures. Using a combination of computational and electrochemical analysis, we show that random architecture outperforms their ordered counterparts in scenarios where diffusion dominates. This work represents the first systematic exploration of random structures in energy storage, highlighting their potential to overcome limitations of traditional designs. However, challenges such as structural reproducibility and scalability remain, necessitating further investigation into fabrication techniques and material selection. By establishing a framework to optimize random electrode architectures, we provide critical insights into the interplay between geometry, diffusion, and reaction kinetics. This study not only introduces a new design principle for 3D electrodes but also opens pathways for next-generation energy storage systems that demand both high performance and adaptability to a range of operating conditions.

Materials science↗

NASA NTRS1997

An Algorithm for Integrated Subsystem Embodiment and System Synthesis

Consider the statement,'A system has two coupled subsystems, one of which dominates the design process. Each subsystem consists of discrete and continuous variables, and is solved using sequential analysis and solution.' To address this type of statement in the design of complex systems, three steps are required, namely, the embodiment of the statement in terms of entities on a computer, the mathematical formulation of subsystem models, and the resulting solution and system synthesis. In complex system decomposition, the subsystems are not isolated, self-supporting entities. Information such as constraints, goals, and design variables may be shared between entities. But many times in engineering problems, full communication and cooperation does not exist, information is incomplete, or one subsystem may dominate the design. Additionally, these engineering problems give rise to mathematical models involving nonlinear functions of both discrete and continuous design variables. In this dissertation an algorithm is developed to handle these types of scenarios for the domain-independent integration of subsystem embodiment, coordination, and system synthesis using constructs from Decision-Based Design, Game Theory, and Multidisciplinary Design Optimization. Implementation of the concept in this dissertation involves testing of the hypotheses using example problems and a motivating case study involving the design of a subsonic passenger aircraft.

Lewis, Kemper↗

NASA NTRS2006

Role of Climate Change in Global Predictions of Future Tropospheric Ozone and Aerosols

A unified tropospheric chemistry-aerosol model within the Goddard Institute for Space Studies general circulation model II is applied to simulate an equilibrium CO2-forced climate in the year 2100 to examine the effects of climate change on global distributions of tropospheric ozone and sulfate, nitrate, ammonium, black carbon, primary organic carbon, secondary organic carbon, sea salt, and mineral dust aerosols. The year 2100 CO2 concentration as well as the anthropogenic emissions of ozone precursors and aerosols/aerosol precursors are based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) A2. Year 2100 global O3 and aerosol burdens predicted with changes in both climate and emissions are generally 5-20% lower than those simulated with changes in emissions alone; as exceptions, the nitrate burden is 38% lower, and the secondary organic aerosol burden is 17% higher. Although the CO2-driven climate change alone is predicted to reduce the global O3 concentrations over or near populated and biomass burning areas because of slower transport, enhanced biogenic hydrocarbon emissions, decomposition of peroxyacetyl nitrate at higher temperatures, and the increase of O3 production by increased water vapor at high NOx levels. The warmer climate influences aerosol burdens by increasing aerosol wet deposition, altering climate-sensitive emissions, and shifting aerosol thermodynamic equilibrium. Climate change affects the estimates of the year 2100 direct radiative forcing as a result of the climate-induced changes in burdens and different climatological conditions; with full gas-aerosol coupling and accounting for ozone and direct radiative forcings by the O2, sulfate, nitrate, black carbon, and organic carbon are predicted to be +0.93, -0.72, -1.0, +1.26, and -0.56 W m(exp -2), respectively, using present-day climate and year 2100 emissions, while they are predicted to be +0.76, -0.72, 0.74, +0.97, and -0.58 W m(exp -2), respectively, with year 2100 climate and emissions.

Liao, Hong↗

DOE OSTI2025

Metagenome-assembled genomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of LBNL (Lawrence Berkeley National Laboratory) TES (Terrestrial Ecosystem Science) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from soil depth profiles collected from 2014 to 2021 from three paired control and warming plots. We collected soil samples across a range of depth profiles (spanning surface to 90 cm deep) from three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. 101 soil metagenomes were sequenced at JGI (Joint Genome Institute) and UCSF (University of California San Francisco) Center for Advanced Technology and can be found under the JGI (Joint Genome Institute) GOLD (Genomes Online Database) Sequencing project Gs0151586 and NCBI (National Center for Biotechnology Information) Projects PRJNA1225762 and PRJEB39497. Metagenomes were assembled using JGI (Joint Genome Institute) Metagenome Workflow (10.1128/mSystems.00804-20). For each metagenome, the assembled contigs were binned into genomes using 3 binning algorithms (cocacola, metabat, and maxbin) and the resulting bins were consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>50%) and contamination (<25%), and dereplicated at 99% ANI (average nucleotide identity) using dRep (https://github.com/MrOlm/drep).The dataset includes a zip file of 2321 MAG (Metagenome Assembled Genome) fasta files, the accession numbers for the underlying metagenomes, and a csv file with MAG (Metagenome Assembled Genome) quality metrics and taxonomic classification (GTDB -Genome Taxonomy Database-RS220). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. A sample metadata file (samples.csv) that contains site information has also been included.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Simulating Global Terrestrial Carbon and Nitrogen Biogeochemical Cycles With Implicit and Explicit Representations of Soil Microbial Activity

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Reduce Revenue Versus Increase Expenditure: Fires and Plant Invasion Drive Soil Carbon Loss With Different Mechanisms in a Mediterranean Shrubland

Fires and plant invasions threaten Mediterranean ecosystems substantially, particularly in the context of changing climate. Our study utilized a data-model integration approach to assess the response of soil organic carbon (SOC) to fires and plant invasion under three Shared Socio-Economic Pathway (SSP) scenarios (SSP1-26, SSP2-45, and SSP5-85). We parameterized the CLM-Microbe model and then investigated the individual and interactive impacts of fires and plant invasion on soil C by comparing factorial simulations of initialization (fire/no wildfire in 2021), fire module on/off, and with and without plant invasion during 2023–2100 in a Mediterranean ecosystem. The simulations indicated a marked C loss due to the 2021 wildfire, projected fires, and plant invasion across all future climate scenarios. Specifically, the 2021 wildfire, projected fires, and plant invasion reduced the SOC (0–30 cm) by 0.12, 0.26, and 0.15 kg C m −2 under SSP1-26, 0.12, 0.30, and 0.12 kg C m −2 under SSP2-45, and 0.12, 0.24, and 0.13 kg C m −2 under SSP5-85, respectively. However, fires and plant invasion decreased SOC through distinct mechanisms. The effects of the 2021 wildfire occurred due to its negative legacy on soil microbial community and, thus, litter accumulation, suppressing the formation of soil carbon via decomposition. Influences of projected fires happen via consuming fuel and suppressing carbon input to soils. In contrast, the impacts of plant invasions were due to enhanced microbial respiration, leading to C loss. In conclusion, these findings emphasize the need for tailored C sequestration strategies considering the disparate effects of fires and plant invasions in the Mediterranean climate.

microbe↗

NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗