Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A
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54 ENVIRONMENTAL SCIENCES↗
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Results for “impact modeling”
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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Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-Learning: Preprint
Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure loss in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified in order to help transmission and distribution system operators to prepare and prevent significant losses. In this paper, we developed an approach that models the impact of extreme weather on the power grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch in order to minimize the adverse impact of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Caldera_Grid
Caldera Grid is part of Caldera software platform, a suite of collective, open-source tools that was developed to improve the state of the art in modeling the impacts of Electric Vehicle (EV) charging on the grid. Caldera Grid is a co-simulation framework implemented using Hierarchical Engine for Large scale Infrastructure Co-Simulation (HELICS). The framework facilitates the co-simulation of EV charging and Smart Charge Management (SCM) strategies in Caldera Infrastructure Charge Model (ICM) with distribution level grid models in OpenDSS. Vehicle energy needs and charge session requirements generated using Caldera Charge Decision Module (CDM) are fed in as input to Caldera Grid. The EV charging models in Caldera ICM simulates both the uncontrolled charging loads as well as loads modified by the SCM strategies to develop distributed load profiles for each grid node hosting an Electric Vehicle Supply Equipment (EVSE) – also known as chargers. These loads were simulated in OpenDSS alongside existing distribution feeder loads. Each of these models are co-simulated in a HELICS federate. The HELICS co-simulation framework facilitates communication and synchronization between the federates. By co-simulating EV loads and distribution feeder loads, Caldera Grid can assess the potential grid impacts of EV charging on distribution feeders under various grid conditions. A control strategy federate is implemented with an interface where control strategies based on feedback from EV charging status and grid conditions can be developed and implemented. The platform can also support multiple control strategies in a single co-simulation.
Sundarrajan, ManojKumar Cebol↗
Continuum contribution to charged-current absorption of low-energy $ν_e$ on $^{40}$Ar
Accurate modeling of the absorption of tens-of-MeV $ν_e$ on $^{40}$Ar is needed to enable measurements of astrophysical neutrinos using large liquid argon time projection chamber (LArTPC) detectors, such as those planned for the Deep Underground Neutrino Experiment (DUNE). We revisit the MARLEY neutrino interaction model used in present estimates of DUNE sensitivity to supernova and solar neutrino signals. Multiple theoretical refinements are pursued, especially in the unbound continuum region of nuclear excitation energy. Inclusive charged-current neutrino-argon cross sections are calculated using a hybrid strategy. Nuclear transitions to unbound states are treated using a Hartree-Fock Continuum Random Phase Approximation (HF-CRPA) model, including forbidden contributions. Allowed transitions to low-lying discrete levels are also included using indirect measurements and approximate corrections for the momentum transfer dependence. Exclusive predictions are obtained by coupling these calculations with a statistical nuclear de-excitation model. The impact on observables of interest for DUNE and similar experiments is examined in terms of both total and differential cross sections. Our refined calculations predict a lower allowed portion of the cross section relative to the prior MARLEY model. At neutrino energies appreciably below 100 MeV, the inclusion of forbidden transitions does not fully compensate for the loss of allowed strength. For a representative neutrino burst from a galactic core-collapse supernova, our results suggest that MARLEY 1.2.0 overestimates the event yield in a DUNE-like detector by approximately 20%. However, because this overestimation is more severe at backwards angles, use of the charged-current $ν_e$-$^{40}$Ar reaction for supernova pointing may be more feasible than previously expected.
Gardiner, Steven [Fermilab]↗
Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-learning
Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure damage in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified to help transmission and distribution system operators prepare for and prevent significant damage and subsequent loss of power. In this paper, we develop an approach that models the impact of extreme weather on the grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch to minimize the adverse impacts of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.
distribution system↗
Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-Learning
Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure damage in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified to help transmission and distribution system operators prepare for and prevent significant damage and subsequent loss of power. In this paper, we develop an approach that models the impact of extreme weather on the grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch to minimize the adverse impacts of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.
extreme weather↗
Water foraging with dynamic roots in E3SM; The role of roots in terrestrial ecosystem memory on intermediate timescales (Final Technical Report)
Terrestrial ecosystems can show sustained responses to stress events that may last for years after the initial perturbation. This phenomenon is referred to as legacy or memory and it emerges from a set of ecosystem processes that are poorly captured by Earth System and Land Surface Models. Consequently, these models struggle to predict the impact that extreme climate events have on surface energy, carbon and hydrological exchange once a stressor such as drought has relaxed or in response to repeated exposure to stress. In this project, we set out to test how the addition of dynamic root profiles in land surface models impact the capacity for Earth System Models, such as the Department of Energy’s E3SM, to capture realistic legacy effects. Observations have shown that vegetation shifts their root profiles during stress events to forage for water and these altered root profiles may take years to relax back to the initial state. We hypothesized that the alteration of the belowground root structures may be a source of terrestrial ecosystem legacy that is missing from models. To test this idea we undertook three core activities. (1) We developed a global-scale analysis of the impact of dynamic roots on ecosystem legacy building from a recently developed dynamic root module. (2) We developed new root dynamics in the DOE’s Energy Land Model (ELM) that allowed not only root profiles to shift their profile but also simultaneously alter carbon allocation to fine root pools. We then tested the impact of these new dynamics with intensive sensitivity analysis at four long-term AmeriFlux sites. (3) We undertook detailed isotopic analysis of tree rings from these AmeriFlux sites to assess ecophysiological and ecohydrological legacy to stress events that can be used to benchmark the sensitivity experiments with ELM. From these core activities, we report the following key findings. Firstly, on a global scale, the addition of dynamic roots led to chronically water-stressed ecosystems recovering faster to climate stress while wetter ecosystem showed enhanced legacy. This is because across ecosystems, stress events were almost universally associated with water shortages that led to the development of deeper root profiles. These deeper root profiles proved beneficial for recovery from drought stress. While the root dynamics did not universally improve the modeled representation of legacy it showed complex transient dynamics that emerge from the addition of dynamic roots. Secondly, the sensitivity analysis illustrated long term shifts in rooting depths away from the prescribed default profile suggesting that initializing of root profiles could benefit from spin-up simulations that converge on locally optimized root profiles. In addition, by enabling dynamic allocation some of the sites predicted unrealistically low allocation to roots (and high allocation to leaves). While these changes did not dramatically alter modeled gross primary productivity, they illustrated that without more sophisticated root processes in models, there is little penalty to dramatically disinvest in roots. Thirdly, the isotopic analysis of tree rings showed highly distinct legacy responses across species and sites. For example, T. canadensis showed reduced transpiration the year after stress events illustrated by sustained elevated $\delta^{18}$O. In contrast, A saccharum displayed elevated $\delta^{13}$C associated with reduced stomatal conductance in response to the previous years’ stress event. These geochemical signatures of legacy were present despite tree growth returning to normal the year after the stress. The results show the importance of species-level dynamics in legacy that are absent in modeling that assumes common traits within plant functional types. In summary, the work here established new avenues to explore root dynamics in models while illustrating how additional root processes are needed before dynamic carbon allocation can be implemented. Lastly, this project provided mentorship to a postdoctoral fellow, training for an early career scientist and multiple undergraduate students recruited from a minority serving institution.
54 ENVIRONMENTAL SCIENCES↗
Analysis of the Value Proposition of High-Efficiency, Multijunction Solar Modules for Residential Rooftop Installations
Utilizing detailed bottom-up models, we analyze the potential markets for high-efficiency photovoltaic modules in the United States. We first estimate the fraction of area-constrained residential rooftops that could benefit from increasing the availability of high-efficiency systems. We then model the impact of efficiency on installed system costs in these area-constrained scenarios. Finally, we explore the value proposition of high-efficiency, fixed-tilt micro-concentrator photovoltaic modules in a series of case studies of residential customers, modeling the levelized cost of energy, payback period, and annual bill savings. We find that efficiency can provide significant savings in installed system cost and improve value to the solar system owner. The improvement in value depends on location and the size of the rooftop, as well as the ability of a given technology to collect diffuse light and achieve low module costs.
14 SOLAR ENERGY↗
Roadmap on multiscale materials modeling
Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.
36 MATERIALS SCIENCE↗
Electrochemical-Mechanical Coupling Strongly Affects the Performance of Nanopore, Thin-Film, and Solid-State Batteries
Here, the present study focuses on the electrochemical-mechanical (ECM) coupling effects of a thin-film, solid-state battery with only stiff, ceramic materials, in contrast to prior investigations that focus on individual active material particles or aggregations of particles. We model the impacts of ECM couplings including stress-transport and stress-equilibrium potential on the full-cell performance and potential mechanical failure modes of a thin-film battery conformally deposited in a nanopore scaffold, which is an experimentally achievable device. Model results indicate electrode volume changes due to lithium insertion or removal, along with mechanical boundary conditions, result in stress gradients that alter the lithium-ion flux, reduce lithium concentration gradients, and improve cell rate capability. However, the high stress levels in the cell can also lead to mechanics-related failure such as the separation of cell layers. For the parameter set in this work, stress-transport coupling has a much greater influence on rate capability than stress-potential coupling. Optimization of thin-film batteries to harness the benefits of ECM coupling effects requires leveraging geometric design and material selection. The current work underscores the need for further theoretical and experimental investigation into ECM coupling effects in thin-film batteries to enhance their understanding and design optimization.
25 ENERGY STORAGE↗
Scenario storyline discovery for complex multi-actor human-natural systems
This poster was presented at the AGU Fall Meeting 2024. Abstract:Scenario analysis is a useful tool for assessing the impacts of future conditions or alternative strategies. However, the common practice of focusing on a small number of predetermined scenarios can limit our understanding of key uncertainties, and fail to represent diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by simulating a wide range of possible futures and system perspectives. A challenge with these approaches is that they often involve large ensemble experiments which limit interpretability and usability. We recently introduced the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced ``forensic''), a scenario discovery framework that helps users identify scenario storylines that capture key system dynamics and as well as important outcomes. In this poster presentation, we present training materials to support the generalizable application of the framework to other multi-actor systems with complex dynamics. Specifically, we will present a step-by-step methodological typology of tools and methods that can be used to generate and classify plausible states of the world on key metrics and consequential dynamics. The typology will also discuss potential implications of these choices and their applicability to different systems.
Colorado River↗
Redshift drift cosmography with ELT and SKAO measurements
ABSTRACT Mapping the expansion history of the universe is a compelling task of physical cosmology, especially in the context of the observational evidence for the recent acceleration of the universe, which demonstrates that canonical theories of cosmology and particle physics are incomplete and that there is new physics still to be discovered. Cosmography is a phenomenological approach to cosmology, where (with some caveats) physical quantities are expanded as a Taylor series in the cosmological redshift z, or analogous parameters such as the rescaled redshift y = z/(1 + z) or the logarithmic redshift x = ln (1 + z). Moreover, the redshift drift of objects following cosmological expansion provides a model-independent observable, detectable by facilities currently under construction, viz. the Extremely Large Telescope and the Square Kilometre Array Observatory (at least in its full configuration). Here, we use simulated redshift drift measurements from the two facilities to carry out an assessment of the cosmological impact and model discriminating power of redshift drift cosmography. We find that the combination of measurements from the two facilities can provide a stringent test of the Λ cold dark matter paradigm, and that overall the logarithmic-based expansions of the spectroscopic velocity drift are the most reliable ones, performing better than analogous expansions in the redshift or the rescaled redshift: the former nominally gives the smaller error bars for the cosmographic coefficients but is vulnerable to biases in the higher order terms (in other words, it is only reliable at low redshifts), while the latter always performs poorly.
Rocha, B. A. R.↗
Spatial impacts of technological innovations on the levelized cost of energy for offshore wind power plants in the United States
Recent studies predict significant decreases in the future levelized cost of energy (LCOE) of offshore wind energy, much of which is attributed to anticipated cost reductions from technological innovation. This study evaluates the spatial variability of LCOE caused by technology-induced decreases in a range of capital, operational, and financial cost categories. A spatial cost model of fixed-bottom and floating offshore wind plants is used to model the impact across thousands of potential United States sites. A specified change in an individual turbine subsystem cost produces a range of LCOE outcomes due to the varying geospatial characteristics of the considered sites and the nonlinear, interactive dependency on these input parameters; for example, a 10.8% improvement in net capacity factor can reduce LCOE by between 6% and 20% at different sites. This work expands upon the existing offshore wind literature, which typically evaluates cost sensitivities at a single site and does not consider the spatial variance in LCOE. The results suggest that the impact of technological innovations can be considerable and should be considered on a spatial as well as temporal basis when prioritizing technology innovation research or funding decisions to advance offshore wind technologies in the United States.
17 WIND ENERGY↗
The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids: Preprint
The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.
27 ARPA - Advanced Research Projects Agency-Energy↗
Improved Modeling of Waste Isolation Pilot Plant Disposal Room Porosity
Disposal rooms at the Waste Isolation Pilot Plant (WIPP) contain waste and gas, and their porosity evolves over time. This report presents several improvements to the disposal room porosity model and presents new porosity predictions for use in future WIPP Performance Assessment activities. The improvements pertain to three sub-models: the geomechanical model, the waste compaction model, and the gas pressurization model. The impacts of each major improvement were quantified and the new porosity predictions were shown to be both mesh and domain size converged. Also, sensitivity studies on the disposal room horizon, clay seam friction coefficients, and homogenized waste representation were performed to support assumptions in the disposal room porosity model. To compare the legacy and new porosity predictions, the simulation results were plotted as a response surface, where gas pressure and time are inputs and porosity is the output. The new porosity response surface is insensitive to pressures beneath lithostatic pressure and highly sensitive to pressures above lithostatic pressure. The legacy porosity response surface, on the other hand, has moderate porosity gradients over all pressures. The new porosity response surface has a stronger scientific foundation than the legacy surface and may now be used for Compliance Decision Analyses.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
A global model–measurement evaluation of particle light scattering coefficients at elevated relative humidity
The uptake of water by atmospheric aerosols has a pronounced effect on particle light scattering properties, which in turn are strongly dependent on the ambient relative humidity (RH). Earth system models need to account for the aerosol water uptake and its influence on light scattering in order to properly capture the overall radiative effects of aerosols. Here we present a comprehensive model–measurement evaluation of the particle light scattering enhancement factor f(RH), defined as the particle light scattering coefficient at elevated RH (here set to 85 %) divided by its dry value. The comparison uses simulations from 10 Earth system models and a global dataset of surface-based in situ measurements. In general, we find a large diversity in the magnitude of predicted f(RH) amongst the different models, which can not be explained by the site types. Based on our evaluation of sea salt scattering enhancement and simulated organic mass fraction, there is a strong indication that differences in the model parameterizations of hygroscopicity and model chemistry are driving at least some of the observed diversity in simulated f(RH). Additionally, a key point is that defining dry conditions is difficult from an observational point of view and, depending on the aerosol, may influence the measured f(RH). The definition of dry also impacts our model evaluation, because several models exhibit significant water uptake between RH = 0 % and 40 %. The multisite average ratio between model outputs and measurements is 1.64 when RH = 0 % is assumed as the model dry RH and 1.16 when RH = 40 % is the model dry RH value. The overestimation by the models is believed to originate from the hygroscopicity parameterizations at the lower RH range which may not implement all phenomena taking place (i.e., not fully dried particles and hysteresis effects). This will be particularly relevant when a location is dominated by a deliquescent aerosol such as sea salt. Our results emphasize the need to consider the measurement conditions in such comparisons and recognize that measurements referred to as dry may not be dry in model terms. Recommendations for future model–measurement evaluation and model improvements are provided.
54 ENVIRONMENTAL SCIENCES↗
The effect of lignin degradation products on the generation of pseudo-lignin during dilute acid pretreatment
Pseudo-lignin is an insoluble material that tends to deposit on acidic pretreated fiber surface and negatively impacts the biological conversion of biomass to valued added products. Employing a laboratory generated acidic hydrolysate solution from dilute acid pretreatment of bamboo residues, we sought to seek an improved understanding of pseudo-lignin formation from fragmented carbohydrates. In addition, we also performed experiments to investigate how lignin model compounds interact with carbohydrates en route to pseudo-lignin’s formation. The yields of pseudo-lignin generated from xylose (CXPL) were higher than those generated from glucose (CGPL). Gas chromatography-Mass spectrometry and Foloin-Ciocalteau analysis suggested that there were more aromatic compounds in hydrolyzate during CGPL’s formation. The presence of lignin phenolic model compounds had both a positive and negative effect upon quantitative yields of pseudo-lignin. Gel permeation chromatography and NMR analysis ( 31 P and 2D-HSQC NMR) of the pseudo-lignin samples generated in the presence and absence of 4-hydroxybenzoic acid (4-HA), employed as a lignin model compound, revealed drastic differences in molecular weight, changes in hydroxyl group content, and hydrocarbon bonds, suggesting significant differences in the pseudo-lignin reaction pathways when certain lignin model compounds are present. Finally, our findings demonstrate the effects of both hemicellulose carbohydrate makeup and lignin model compound impact on the formation of pseudo-lignin and its chemical properties, suggesting a potential way to minimize pseudo-lignin formation.
59 BASIC BIOLOGICAL SCIENCES↗
Reducing Model Uncertainty of Climate Change Impacts on High Latitude Carbon Assimilation
The Arctic Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO 2 . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models (TBMs) such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Furthermore, we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.
54 ENVIRONMENTAL SCIENCES↗