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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Approximate Bisimulation-Based Reduction of Power System Dynamic Models

In this paper we propose approximate bisimulation relations and functions for reduction of power system dynamic models in differential- algebraic (descriptor) form. The full-size dynamic model is obtained by linearization of the nonlinear transient stability model. We generalize theoretical results on approximate bisimulation relations and bisimulation functions, originally derived for a class of constrained linear systems, to linear systems in descriptor form. An algorithm for transient stability assessment is proposed and used to determine whether the power system is able to maintain the synchronism after a large disturbance. Two benchmark power systems are used to illustrate the proposed algorithm and to evaluate the applicability of approximate bisimulation relations and bisimulation functions for reduction of the power system dynamic models.

Stankovic, AM↗
DOE OSTI2019

An artificial neural network framework for reduced order modeling of transient flows

Here, this paper proposes a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state variables when the control parameter values vary. Our approach utilizes a training process from full-order scale direct numerical simulation data projected on proper orthogonal decomposition (POD) modes to achieve an artificial neural network (ANN) model with reduced memory requirements. This data-driven ANN framework allows for a nonlinear time evolution of the modal coefficients without performing a Galerkin projection. Our POD-ANN framework can thus be considered an equation-free approach for latent space dynamics evolution of nonlinear transient systems and can be applied to a wide range of physical and engineering applications. Within this framework we introduce two architectures, namely sequential network (SN) and residual network (RN), to train the trajectory of modal coefficients. We perform a systematic analysis of the performance of the proposed reduced order modeling approaches on prediction of a nonlinear wave-propagation problem governed by the viscous Burgers equation, a simplified prototype setting for transient flows. We find that the POD-ANN-RN yields stable and accurate results for test problems assessed both within inside and outside of the database range and performs significantly better than the standard intrusive Galerkin projection model. Finally, our results show that the proposed framework provides a non-intrusive alternative to the evolution of transient physics in a POD basis spanned space, and can be used as a robust predictive model order reduction tool for nonlinear dynamical systems.

42 ENGINEERING↗
DOE OSTI2015

Reduction Rates for Higher Americium Oxidation States in Nitric Acid

The stability of hexavalent americium was measured using multiple americium concentrations and nitric acid concentrations after contact with the strong oxidant sodium bismuthate. Contrary to our hypotheses Am(VI) was not reduced faster at higher americium concentrations, and the reduction was only zero-order at short time scales. Attempts to model the reduction kinetics using zero order kinetic models showed Am(VI) reduction in nitric acid is more complex than the autoreduction processes reported by others in perchloric acid. The classical zero-order reduction of Am(VI) was found here only for short times on the order of a few hours. We did show that the rate of Am(V) production was less than the rate of Am(VI) reduction, indicating that some Am(VI) undergoes two electron-reduction to Am(IV). We also monitored the Am(VI) reduction in contact with the organic diluent dodecane. A direct comparison of these results with those in the absence of the organic diluent showed the reduction rates for Am(VI) were not statistically different for both systems. Additional americium oxidations conducted in the presence of Ce(IV)/Ce(III) ions showed that Am(VI) is reduced without the typical growth of Am(V) observed in the systems sans Ce ion. This was an interesting result which suggests a potential new reduction/oxidation pathway for Am in the presence of Ce; however, these results were very preliminary, and will require additional experiments to understand the mechanism by which this occurs. Overall, these studies have shown that hexavalent americium is fundamentally stable enough in nitric acid to run a separations process. However, the complicated nature of the reduction pathways based on the system components is far from being rigorously understood.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2012

The Development of Models for Carbon Dioxide Reduction Technologies for Spacecraft Air Revitalization

Through the respiration process, humans consume oxygen (O2) while producing carbon dioxide (CO2) and water (H2O) as byproducts. For long term space exploration, CO2 concentration in the atmosphere must be managed to prevent hypercapnia. Moreover, CO2 can be used as a source of oxygen through chemical reduction serving to minimize the amount of oxygen required at launch. Reduction can be achieved through a number of techniques. NASA is currently exploring the Sabatier reaction, the Bosch reaction, and co- electrolysis of CO2 and H2O for this process. Proof-of-concept experiments and prototype units for all three processes have proven capable of returning useful commodities for space exploration. All three techniques have demonstrated the capacity to reduce CO2 in the laboratory, yet there is interest in understanding how all three techniques would perform at a system level within a spacecraft. Consequently, there is an impetus to develop predictive models for these processes that can be readily rescaled and integrated into larger system models. Such analysis tools provide the ability to evaluate each technique on a comparable basis with respect to processing rates. This manuscript describes the current models for the carbon dioxide reduction processes under parallel developmental efforts. Comparison to experimental data is provided were available for verification purposes.

Swickrath, Michael J.↗
DOE OSTI2025

Understanding the Unique Reactivity of Cu for Electrochemical CO 2 Reduction with a 3-Site Model

Cu-based catalysts for the electrochemical reduction of CO 2 and CO exhibit a perplexingly unique reactivity toward multicarbon based products compared to other studied electrocatalysts. Here, in this study, we use insights gained from a recent phenomenological 3-site microkinetic model and grand-canonical density functional theory calculations to clarify the importance of an underemphasized aspect critical to Cu’s unique reactivity: a population of so-called “reservoir” sites. Using model Cu surface motifs, we discuss how these types can be represented by undercoordinated structural defects like step edges and grain boundaries which form a network of highly anisotropic migration channels. These pathways are found to be amenable for feeding *CO over time to reactive sites like Cu adatoms more active toward C–C coupling. These results highlight an often overlooked aspect of catalyst optimization: reservoir site engineering, which exploits surface mobility and presents an equally important avenue for electrocatalyst engineering oversimply maximizing active site densities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2011

The Development of Models for Carbon Dioxide Reduction Technologies for Spacecraft Air Revitalization

Through the respiration process, humans consume oxygen (O2) while producing carbon dioxide (CO2) and water (H2O) as byproducts. For long term space exploration, CO2 concentration in the atmosphere must be managed to prevent hypercapnia. Moreover, CO2 can be used as a source of oxygen through chemical reduction serving to minimize the amount of oxygen required at launch. Reduction can be achieved through a number of techniques. The National Aeronautics and Space Administration (NASA) is currently exploring the Sabatier reaction, the Bosch reaction, and co-electrolysis of CO2 and H2O for this process. Proof-of-concept experiments and prototype units for all three processes have proven capable of returning useful commodities for space exploration. While all three techniques have demonstrated the capacity to reduce CO2 in the laboratory, there is interest in understanding how all three techniques would perform at a system-level within a spacecraft. Consequently, there is an impetus to develop predictive models for these processes that can be readily re-scaled and integrated into larger system models. Such analysis tools provide the ability to evaluate each technique on a comparable basis with respect to processing rates. This manuscript describes the current models for the carbon dioxide reduction processes under parallel developmental e orts. Comparison to experimental data is provided were available for veri cation purposes.

Swickrath, Michael J.↗
DOE OSTI2014

A revised model of ex-vivo reduction of hexavalent chromium in human and rodent gastric juices

Chronic oral exposure to hexavalent chromium (Cr-VI) in drinking water has been shown to induce tumors in the mouse gastrointestinal (GI) tract and rat oral cavity. The same is not true for trivalent chromium (Cr-III). Thus reduction of Cr-VI to Cr-III in gastric juices is considered a protective mechanism, and it has been suggested that the difference between the rate of reduction among mice, rats, and humans could explain or predict differences in sensitivity to Cr-VI. We evaluated previously published models of gastric reduction and believe that they do not fully describe the data on reduction as a function of Cr-VI concentration, time, and (in humans) pH. The previous models are parsimonious in assuming only a single reducing agent in rodents and describing pH-dependence using a simple function. We present a revised model that assumes three pools of reducing agents in rats and mice with pH-dependence based on known speciation chemistry. While the revised model uses more fitted parameters than the original model, they are adequately identifiable given the available data, and the fit of the revised model to the full range of data is shown to be significantly improved. Hence the revised model should provide better predictions of Cr-VI reduction when integrated into a corresponding PBPK model. - Highlights: • Hexavalent chromium (Cr-VI) reduction in gastric juices is a key detoxifying step. • pH-dependent Cr-VI reduction rates are explained using known chemical speciation. • Reduction in rodents appears to involve multiple pools of electron donors. • Reduction appears to continue after 60 min, although more slowly than initial rates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Herbaceous Feedstock 2018 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations, and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible, and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2018 Herbaceous SOT aligned feedstock logistic design with current biorefinery’s design capacity utilized by biochemical conversion platform. Currently biochemical conversion platform utilizes a 725,000 dry ton/year biorefiney design for the techno economic analysis. Hence, feedstock delivered cost in the 2018 Herbaceous SOT is calculated based on biorefinery’s 725,000 dry ton design capacity instead of 800, 000 dry ton capacity utilized in the 2017 Herbaceous SOT. Biomass availabilities in this SOT were updated to year 2018 data from the 2016 Billion-Ton Report (BT16) (DOE 2016a), with the exception of switchgrass, for which the 2018 Herbaceous SOT utilized the 2019 switchgrass availability data from BT16. The BT16 report (DOE 2016a) does not project switchgrass availability in 2018; the soonest switchgrass is available in the BT16 report is 2019. Therefore, availability of switchgrass for this analysis was that projected for 2019. The 2018 Herbaceous SOT incorporates same technologies utilized in the 2017 Herbaceous SOT. However, a sensitivity analysis is performed to understand the impact of variation of process parameters on those technologies on feedstock logistic cost. New R&D data that shows the variations of process parameters affecting process performance is incorporated in the 2018 SOT to measure the variations in delivered feedstock cost. The 2018 Herbaceous SOT has also provided projected delivered feedstock of 2022 design case based on near term technical target under BETO funded R&D project. Finally, updated biorefinery size of 725,000 dry ton/year was incorporated within least-cost formulation model to select optimal siting and depot scales during optimization of the least cost blend. This modification to the optimization algorithm allows the trade-off between the cost of increased supply radius and the savings from selecting biomass from higher producing counties to be assessed. Such optimization has also showed the economic benefit of decentralized depots in comparison to centralized preprocessing co-located with the biorefinery by decoupling the biorefinery and feedstock locations. The 2018 Herbaceous SOT report documents the current modeled cost of a herbaceous feedstock supply system (from harvest to the pretreatment reactor throat, including grower payment) for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2018 Herbaceous SOT predicts a modeled delivered feedstock cost of $83.67/dry ton (2016$); this is a $0.23/dry ton (2016$) decrease from the 2017 Herbaceous SOT. The modification of biorefinery’s designed capacity and increased projected biomass availability in the same supply shed contributed to this modeled cost reduction. The least-cost formulation model to optimally site and scale local distributed preprocessing depots also contributed to the cost reduction by considering county-level grower payment and distance from the biorefinery as variables in the optimization algorithm. Sensitivity analysis on various process parameters that affect delivered feedstock cost in the 2018 Herbaceous SOT shows that the delivered cost could varies from $80.45-$88.83/dry ton. The top factors that causes such variations are: effective baling rate, bale density, hammer mill throughput, interest rate and storage dry matter loss.

09 BIOMASS FUELS↗
DOE OSTI2022

Mechanistic insights into metal, nitrogen doped carbon catalysts for oxygen reduction: progress in computational modeling

We report metal and nitrogen doped carbon materials (denoted as M–N–C) synthesized through high-temperature pyrolysis have been found to exhibit activity for oxygen reduction reaction (ORR) approaching that of Pt and electrochemical stability higher than previous MN 4 -containing macrocyclic molecular catalysts. Tremendous efforts have thus been devoted to the advancement of M–N–C catalysts as an economical alternative to Pt-based catalysts for proton exchange membrane fuel cell cathodes with a focus on simultaneously improving activity and stability. To this end, novel computational modeling techniques have been developed and applied to acquire knowledge crucial for accelerating the pace of M–N–C catalyst development. In this review, recent progress in computational method development, as well as the predictions of chemical structure of active sites, reaction pathways, ORR kinetics, and catalyst stability in electrochemical environments, are critically surveyed. Moreover, the crucial role of computational modeling to elucidate the functional mechanism of M–N–C catalysts for ORR in acid media and enable rational design of M–N–C catalysts is discussed with a visionary outlook for the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Particle scale modeling of CuFeAlO 4 during reduction with CO in chemical looping applications

Particle scale models that couple reaction phenomena to changes in the solid-state chemistry of an oxygen carrier system are critical to the advancement of the chemical looping concept by allowing for a means to assess process scale up. This work presents an analysis of the reduction for a CuFeAlO 4 oxygen carrier with Carbon Monoxide (CO). The analysis was utilized to aid in the application of particle scale model representation of the carrier system. An experimentally driven study was conducted to provide an array of operational/parametric data sets for the analysis and their impact accessed. Quantification of the cubic spinel oxide phase and changes due to lattice oxygen depletion from reduction were explored to link the solid-state chemistry changes to the reaction progression. Reduction occurred in a multistep process as oxygen was depleted from the structure. Oxygen bound to Cu cations were the first to react with CO. As oxygen was depleted further phase re-orientation occurred resulting in an iron based aluminate (FeAl 2 O 4 ) with discrete metallic copper present. FeAl 2 O 4 was further depleted of oxygen to metallic Fe and alumina. The multistep process was emulated through the use of a multi-interface Grainy pellet model. To add to the utility of the model, the effects of product gas, CO 2 , on the reaction progression were incorporated into the description. In addition, the catalytic effects of the Boudouard reaction were examined and incorporated into the representation adding to the novelty of the work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Electric Power Distribution System Model Simplification Using Segment Substitution

Quasi-static time-series (QSTS) simulation is used to simulate the behavior of distribution systems over long periods of time (typically hours to years). The technique involves repeatedly solving the load-flow problem for a distribution system model and is useful for distributed energy resource (DER) planning. When a QSTS simulation has a small time step and a long duration, the computational burden of the simulation can be a barrier to integration into utility workflows. One way to relieve the computational burden is to simplify the system model. The segment substitution method of simplifying distribution system models introduced in this paper offers model bus reduction of up to 98% with a simplification error as low as 0.2% (0.002 pu voltage). In contrast to existing methods of distribution system model simplification, which rely on topological inspection and linearization, the segment substitution method uses black-box segment data and an assumed simplified topology.

Reiman, Andrew P. (ORCID:0000000302042960)↗
DOE OSTI2022

Electrochemical CO2 Reduction over Metal-/Nitrogen-Doped Graphene Single-Atom Catalysts Modeled Using the Grand-Canonical Density Functional Theory

Renewably driven, electrochemical conversion of carbon dioxide into value-added products is expected to be a critical tool in global decarbonization. However, theoretical studies based on the computational hydrogen electrode largely ignore the nonlinear effects of the applied potential on the calculated results, leading to inaccurate predictions of catalytic behavior or mechanistic pathways. Here, we use grand canonical density functional theory (GC-DFT) to model electrochemical CO2 reduction (CO2R) over metal- and nitrogen-doped graphene catalysts (MNCs) and explicitly include the effects of the applied potential. We used GC-DFT to compute the CO2 to CO reaction intermediate energies at -0.3, -0.7, and -1.2 VSHE catalyzed by MNCs each doped with 1 of the 10 3d block metals coordinated by four pyridinic nitrogen atoms. Our results predict that Sc-, Ti-, Co-, Cu-, and Zn-N4Cs effectively catalyze CO2R at moderate to large reducing potentials (-0.7 to -1.2 VSHE). ZnN4C is a particularly promising electrocatalyst for CO2R to CO both at low and moderate applied potentials based on our thermodynamic analysis. Our findings also explain the observed pH independence of CO production over FeN4C and predict that the rate-determining step of CO2R over FeN4C is not *CO2- formation but rather *CO desorption. Additionally, the GC-DFT-computed density of states analysis illustrates how the electronic states of MNCs and adsorbates change non-uniformly with applied potential, resulting in a significantly increased *CO2- stability relative to other intermediates and demonstrating that the formation of the adsorbed *CO2- anion is critical to CO2R activation. This work demonstrates how GC-DFT paves the way for physically realistic and accurate theoretical simulations of reacting electrochemical systems.

CO2 reduction↗
DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗
DOE OSTI2026

ACRRF High-Bay Dose Calculations using MCNP (Part B)

ACRR radiation outputs through vertical cavities have been documented in two reports. In Part B, the maximum dose from the unshielded central cavity, FREC-II, and NRS is calculated to inform the safety basis. The maximum dose is ≈1430 mrem per 300 MJ, or ≈40,930 rem/hr for full-power operation. A verification study completes the V&V of the modeling. Supplementary studies of variance-reduction techniques, model sensitivities, and aircraft dose above the ACRRF are included.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

58 GEOSCIENCES↗
NASA NTRS2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

Earth system dynamics↗
DOE OSTI2021

Modeling potential air temperature reductions yielded by cool roofs and urban irrigation in the Kansas City Metropolitan Area

In this work, we evaluate two mitigation strategies for urban heat island (UHI) in the Kansas City Metropolitan Area (KCMA). Using the Weather Research and Forecasting (WRF) model, we assess the potential benefits of reflective “cool” roofs and urban irrigation on air temperature in typical summer conditions between 2011 and 2015, and during six of the strongest historical heat waves from 2005 to 2016. Under the typical summer conditions, we simulate 2-m air temperature for 10 summer weeks, finding average daytime (07:00–19:00 local time) temperature reductions of 0.08 and 0.28 °C for cool roofs and urban irrigation, respectively. During the six heat-wave episodes, we find daytime temperature reductions of 0.02 and 0.26 °C for the two scenarios, similar to those under typical summer conditions. Our results suggest that urban irrigation can be more efficient than cool roofs in mitigating UHI in metropolitan regions where the majority of the land cover is comprised of areas with low urban (i.e., non-vegetated) fractions. Finally, we find the alteration of surface conditions due to enhanced roof albedos influences precipitation within the WRF simulation, in particular during the heat waves. Further research would be necessary to determine the robustness of this last finding.

54 ENVIRONMENTAL SCIENCES↗