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At least 379 records · Page 21

Impacts of sulfonic acids on fungal manganese oxide production

Microbial manganese (Mn) oxidation plays a critical role in Mn(III/IV) oxide formation in modern day environmental systems. These oxidation processes and resulting biominerals are sensitive to aqueous conditions, such as pH and dissolved constituent concentrations. With Mn and sulfur (S) biogeochemical cycling closely linked in many environmental systems, and dissolved organic sulfur comprising a substantial pool of total sulfur in several environments, the impact of dissolved organic sulfur compounds on Mn redox processes is important to consider. Sulfonic acids, environmentally ubiquitous organosulfur compounds, play substantial roles in S cycling in many natural and contaminated systems. Research to assess the effects of these abundant sulfonic acids on Mn biogeochemical cycling, microbial Mn oxidation processes, and Mn biominerals is needed for understanding and predicting the impact of coupled S and Mn biogeochemical cycles, particularly in environments with dynamic redox gradients or in anthropogenically contaminated systems. Further, with research on microbial and abiotic Mn oxidation processes often using aminosulfonic acids to control pH, understanding the impact of such sulfonic acids on microbial Mn oxidation processes is critical. Several recent studies found that commonly used zwitterionic N substituted aminosulfonic acids, known as Good’s buffers, such as HEPES and MES, can alter abiotic birnessite sheet structures. Here we investigate the impact of two sulfonic acids with broad applications to natural and contaminated sites as well as laboratory settings (HEPES and MES) on fungal Mn oxidation relative to a carbonate buffer and a buffer-free control by three Ascomycete fungi known to oxidize Mn(II): Stagonospora sp. SRC1lsM3a, Paraphaeosphaeria sporulosa AP3s5–JAC2a, and Plectosphaerella cucumerina DS2psM2a2. Structural analyses of the products show that sulfonic acids promote Mn oxidation by P. cucumerina, producing hexagonally symmetric phyllomanganates analogous to hexagonal birnessite or c–disordered H + birnessite [(Ca,Na,K)(Mn 4+ ,Mn 3+ ,$\square$)O 2 nH 2 O], with solid–associated Mn(II) bound to vacancy sites and biomass, while in their absence almost all Mn remains as either aqueous Mn(II) or solid–associated Mn(II) bound to biomass. In contrast, sulfonic acids exert the opposite effect on Mn oxidation by P. sporulosa, with their presence suppressing Mn(II) oxidation to Mn(IV), likely leading to the formation of mycogenic bixbyite (Mn 3+ 2 O 3 ) while the buffer–free control forms a poorly crystalline phyllo- or tectomanganate. Meanwhile, all treatments exert a minimal effect on Mn(II) uptake from solution and Mn oxidation with Stagonospora sp., with all experimental systems and controls forming poorly crystalline, hexagonally symmetric phyllomanganates. The fact that the sulfonic acids here studied exert similar effects on Mn oxidation, but substantially different effects for each fungus, suggests they affect Mn oxidation via mechanistically similar pathways that are likely dependent on interactions with fungal exudates (which vary from species to species) or specific fungal Mn oxidation processes. Interestingly, for all fungi, MES increases Mn(III) in the resulting biominerals, while the carbonate buffer consistently decreases Mn(III). Further, these results clearly demonstrate that sulfonic acids not only alter Mn oxide structures, as has been previously noted in abiotic studies, but can interfere with Mn oxidation reactions themselves, highlighting the incredible sensitivity of both Mn oxide structures and the Mn oxidation process to the aqueous environment.

58 GEOSCIENCES↗

The impact of cold electrons and cold ions in magnetospheric physics

A review of the impact of the cold-ion and cold-electron populations in the Earth’s magnetosphere is presented in this paper. The cold populations are defined by total energy less than approximately 100 eV, i.e. in the energy range which is strongly affected by spacecraft charging and that often dominates the total plasma density. We also include the warm plasma cloak in the review, since it overlaps partially with the cold energy range and is a population that is still not well understood. The known impacts of cold ions and cold electrons that are discussed are: the source of magnetospheric plasma, solar-wind/magnetosphere coupling, magnetotail reconnection and substorms, Kelvin-Helmholtz instabilities at the magnetopause, chorus, hiss, electromagnetic-ion-cyclotron and ultra-low-frequency wave-particle interactions, aurora structuring and spacecraft charging. Other possible impacts are associated with refilling on open-drift trajectories, the remnant layer and plasmapause disruption. A discussion of the difficulty of cold-plasma measurements and the need for new measurement techniques that measure the full cold-ion and cold-electron distribution functions is also presented. There remain a lot of unknowns about the cold-ion and cold-electron populations, associated with their origin, properties, drivers and impacts. These populations will need to be fully understood before the magnetosphere-ionosphere system can be fully understood.

79 ASTRONOMY AND ASTROPHYSICS↗

Geochemical and microbial characteristics of seepage water and mineral precipitates in a radwaste disposal facility impacted by seawater intrusion and high alkalinity

The construction of an underground facility can dramatically change the quality, flow direction, and level of groundwater. It may also impact subsurface microbial composition and activity. Groundwater quality was monitored over eight years in two observational wells near an underground disposal facility on the east coast of South Korea. The results showed dramatic increases in dissolved ions such as O 2 , Na, Ca, Mg, and SO 4 during facility construction. Seepage water samples downgradient from the silos and tunnels, and precipitates deposited along the seepage water flow path were collected to determine the impact inside the disposal facility. X-ray analysis (powder X-ray diffraction (pXRD) and X-ray absorption fine structure (XAFS)) were used to characterize the mineral precipitates. Microbial community composition was determined by 16S rRNA gene sequencing. The seepage water composition was of two types: Ca-Cl and Ca-Na-HCO 3 . The ratio of Cl and δ 18 O showed that the Ca-Cl type seepage water was influenced by groundwater mixed with seawater ranging from 2.7% to 15.1%. Various sulfate-reducing bacteria were identified in the Ca-Cl type seepage water, exhibiting relatively high sulfate content from seawater intrusion. Samples from the Ca-Na-HCO 3 type seepage water had an extremely high pH (>10) and abundance of Hydrogenophaga. The precipitates observed along the flow path of the seepage water included calcite, ferrihydrite, green rust, and siderite, depending on seepage water chemistry and microbial activity. This study suggests that the construction of underground structures creates distinct, localized geochemical conditions (e.g., high alkalinity, high salinity, and oxic conditions), which may impact microbial communities. These biogeochemical changes may have undesirable large-scale impacts such as water pump clogging. Overall, an understanding of the process and long-term monitoring are essential to assess the safety of underground facilities.

54 ENVIRONMENTAL SCIENCES↗

Laser ablation for structuring Li-ion electrodes for fast charging and its impact on material properties, rate capability, Li plating, and wetting

Laser ablation is a scalable technique for decreasing the effective tortuosity of electrodes by selectively removing material with high precision. Applied to ≈ 110 um thick electrode coatings, this work focuses on understanding the impact of laser ablation on electrode material properties at the beginning of life and synergistic impacts of ablated channels on cell performance throughout their cycle life. Post laser ablation, local changes in chemistry, crystallography, and morphology of the laser-impacted electrode regions are investigated. It is shown that femtosecond pulsed laser ablation can achieve high-rate material removal with minor material damage locally at the interface of the impacted zones. The capacity achieved during a 6C (10 min) constant-current constant-voltage charge to 4.2 V improved from 1 mAh cm -2 for the non-ablated electrodes to almost 2 mAh cm -2 for the ablated electrodes. This benefit is attributed to a synergistic effect of enhanced wetting and decreased electrode tortuosity. The benefit was maintained for over 120 cycles, and upon disassembly decreased Li-plating on the graphite anode was observed. Finally, multi-physics modeling in conjunction with wetting analyses showed that laser ablating either one of the electrodes led to substantial improvements in wetting and rate capability, indicating that substantial performance benefits can be achieved by ablating only the graphite anode as apposed to both electrodes.

25 ENERGY STORAGE↗

Sensitivity analysis for characterizing the impact of HNGD model on the prediction of hydrogen redistribution in Zircaloy cladding using BISON code

Hydrogen in zirconium cladding is able to precipitate into zirconium hydrides which impacts cladding integrity. The Hydride Nucleation-Growth-Dissolution (HNGD) model in the BISON code accounts for the precipitation and dissolution kinetics of hydride in Zircaloy material. This paper presents global sensitivity analyses of the HNGD model aiming to enhance our understanding of the hydride precipitation phenomena by quantifying the variance that key parameters have on the prediction of hydrogen behavior under various environmental conditions. Here, model predictions are compared to experimental data obtained under two different conditions: 1) with uniformly precharged specimens subjected to a linear thermal gradient, and 2) specimens precharged with a cathodically applied hydride rim at one end of the sample and subjected to an asymmetric thermal gradient. The Sobol sensitivity analysis identifies the key parameters in the HNGD model for both types of specimens. For linear temperature cases, the heat of transport dominates the accuracy of predictions when no precipitation occurs at the cold end, while Terminal Solid Solubility for Dissolution (TSSD) is the most important parameter when precipitation occurs. A large variation in the predicted hydrogen concentration profiles is found in the range of high TSSD due to the occurrence of precipitation. For asymmetric temperature cases, the solubility coefficient gives the largest impact on the predicted hydrogen distribution, as it determines the amount of solute hydrogen dissolved from the initially applied hydride rim. A large discrepancy in hydrogen distribution between simulations and experiments exists with the asymmetric specimens because BISON simulations fail to predict the precipitation of hydride at the cooler end. Comparative studies using former and updated models verifies the significant impact of the hydride growth mechanism on predicted hydrogen concentration profiles. In particular, when hydride initially exists, changes in TSSD generate a large variation in the predicted amount of precipitation by hydride growth, giving large uncertainty in predicting the hydrogen distribution over the sample length. The outputs characterize the significant impact of the hydride growth mechanism in the HNGD model on predicting hydrogen behavior, and improve the understanding of the precipitation of hydride in Zircaloy cladding within a range of expected environmental conditions. The analyses indicate work is still needed to improve the hydride solvus models in the BISON code to accurately predict experimentally observed hydride concentrations and distributions.

36 MATERIALS SCIENCE↗

Impact of ferromagnetic electrode length and thickness on Magnetic Tunnel Junction-Based Molecular Spintronic Devices (MTJMSD)

A knowledge gap exists about the impact of variation in length and thickness of ferromagnetic(FM) electrodes on molecular spintronics devices’ ability to manifest molecular coupling impact. Magnetic Tunnel Junction-Based Molecular Spintronic Devices (MTJMSDs) are potential candidates for inventing highly correlated materials and devices and investigating the fundamental science of organic spintronics. This paper reports our experi- mental observations providing the dramatic impact of variation in ferromagnetic electrode length and thickness on paramagnetic molecule-based MTJMSD. Room temperature transport studies were performed to investigate the effect of FM electrode thickness. On the other hand, magnetic force microscopy measurements were con- ducted to understand the effect of FM electrode length extending beyond the molecular junction area, i.e., the site where paramagnetic molecules bridged between two FM. In the strong molecular coupling regime, transport study suggested thickness variation caused ~1000 to million-fold differences in junction conductivity. MFM study revealed near-zero magnetic contrast for pillar-shaped MTJMSD without any extended FM electrode. However, MFM images showed a multitude of microscopic magnetic phases on cross junction shaped MTJMSD where FM electrodes extended beyond the junction area. To understand the intriguing experimental results, we conducted an in-depth theoretical study using Monte Carlo Simulation (MCS) approach. MCS study utilized a Heisenberg atomic model of cross junction shaped MTJMSD to gain insights about room temperature transport and MFM experimental observations of microscopic MTJMSD. To make this study applicable for a wide variety of MTJMSDs, we systematically studied the effect of variation in molecular coupling strength between magnetic molecules and ferromagnetic (FM) electrodes of various dimensions. Here, our theoretical results suggest 25 atoms thick FM electrode show very weak molecular coupling impact and of the same order of experimentally studied FM electrode dimensions showing weak effect. Also, MCS results agree with experimentally observed multiple magnetic phases on lengthy FM electrodes of an MTJMSD.

42 ENGINEERING↗

Measurement of the impact-parameter dependent azimuthal anisotropy in coherent $ρ$ 0 photoproduction in Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

This Letter presents the first measurement of the impact-parameter dependent angular anisotropy in the decay of coherently photoproduced $ρ$ 0 mesons. The $ρ$ 0 mesons are reconstructed through their decay into pion pairs. The measured anisotropy corresponds to the amplitude of the cos(2φ) modulation, where φ is the angle between the two vectors formed by the sum and the difference of the transverse momenta of the pions, respectively. The measurement was performed by the ALICE Collaboration at the LHC using data from ultraperipheral Pb–Pb collisions at a center-of-mass energy of $\sqrt{s_{NN}}$ = 5.02 TeV per nucleon pair. Different impact-parameter regions are selected by classifying the events in nuclear-breakup classes. The amplitude of the cos(2φ) modulation is found to increase by about one order of magnitude from large to small impact parameters. Theoretical calculations describe the measured cos(2φ) anisotropy and its impact-parameter dependence as the result of a quantum interference effect at the femtometer scale, arising from the ambiguity regarding which of the nuclei is the photon source in the interaction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Validating simulated mountain wave impacts on hub-height wind speed using SoDAR observations

The ascent of stably stratified air over a mountain barrier can trigger the generation of mountain waves. Mountain waves occur frequently over the Columbia River Gorge in western North America and can impact wind power generation over the area. Therefore, predicting the details of mountain waves events (e.g., dominant wavelength, timing, and duration) can be very valuable for the wind energy community. In this study, the ability of the Weather Research and Forecasting (WRF) model to simulate mountain waves and their impact on hub-height wind speed is investigated. Our results suggest that the WRF model has moderate skill in simulating observed mountain wave. Further, given WRF predictions of wavelength range and wave period, the Fast Fourier Transform can calculate the simulated mountain wave impact on hub-height wind speed. The resulting wind speeds agree well with SoDAR observations in terms of both magnitude and pattern. Finally, for the simulated cases, WRF consistently predicts impacts of significant mountain wave events about an hour earlier than the actual observations. Overall, in this work, the sensitivities as well as uncertainties associated with our methodology are discussed in detail.

17 WIND ENERGY↗

Costs of mitigating the environmental impacts of hydropower projects in the United States

Developers are required to mitigate the environmental impacts of hydropower projects in the United States (U.S.) but there is very little accessible information on the associated costs. This work compiles and analyses a dataset of environmental impacts mitigation costs for 182 hydropower projects based on documents obtained from the U.S. Federal Energy Regulatory Commission (FERC). The analysis shows that environmental costs vary wildly across hydropower project classes and mitigation measures, even when normalized by plant capacity. Capital costs per kilowatt are generally higher for relicensed conventional hydropower plants, and lower for new and relicensed pumped storage hydropower plants, relative to other project classes. Smaller plants tend to spend a higher share of total project costs on mitigating environmental impacts than larger plants, and measures related to Aquatic Species, Project Operations, and Recreation have the highest capital cost per kilowatt. These findings mean that environmental costs could become a key decision variable for new hydropower developments in the U.S. due to the small scale of most of the remaining potential and the increasing stringency of environmental requirements. Therefore, technologies to reduce the environmental impacts and costs of hydropower would be important for future project developments.

54 ENVIRONMENTAL SCIENCES↗

Techno-economic-environmental impacts of industrial energy assessment: Sustainable industrial motor systems of small and medium-sized enterprises

Energy assessments can provide an effective way of identifying and implementing energy efficiency measures to save energy costs and avoid emissions throughout manufacturing facilities. Consequently, it generates significant economic and environmental benefits to localities, states, and the nation. Quantifying these benefits requires a systematic techno-economic-environmental framework for capturing the interactions. This article employs methodologies to improve the energy efficiency of small and medium-size industries through their sustainable industrial motor systems. Motor systems offer large opportunities to enhance energy efficiency through adopting advanced technologies and better-informed operations. Case studies presented illuminate the potential savings and impacts from implementing sustainable motor systems and the importance of energy assessments. Here, the integrated macro-economic analysis quantifies the regional sustainability impacts of implementing the industrial energy efficiency offered by an energy auditing program in Ohio over a ten-year period. Results show that implementing all the center’s motor recommendations have directly saved $\$702$ M in energy costs, avoided 2.7 million metric tons of carbon dioxide emissions, and created 3,445 jobs, resulting in a total annual economic impact of $\$788$ M stemming from direct, indirect, and induced regional economic impacts. It provides policy implications for encouraging sustainable industrial motor systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Technology Impact and Resource Assessment of Existing and Planned U.S. Biofuel Production: Life Cycle Water Consumption, Water Stress, Land Use, and Criteria Air Pollutants

Biofuels have the potential to strengthen the U.S. energy supply, enhance energy security, and promote economic development. As the United States continues to expand biofuel production, quantifying resource requirements and location-specific constraints is crucial for planning, siting, and technology development to support long-term viability. Accordingly, this work assesses the life cycle resource consumption (water consumption, and land use), water stress and criteria air pollutants associated with expanded U.S. biofuel production over the 2020–2035 period, based on producers’ plans. We perform a bottom-up technology impact assessment and resource assessment by integrating facility-level production statistics with Argonne’s Research and Development (R&D) GREET model and county-level water-stress characterization factors from Available Water Remaining for the United States (AWARE-US) model. Results suggest that by 2035, biofuels could meaningfully contribute to U.S. energy demand, driven primarily by first-generation and waste-based feedstocks with plans for substantial capacity expansion, although cellulosic and e-fuel technologies remain limited. However, growth must be managed to minimize impacts on water resources and land use. These impacts vary by fuel type and facility location, specifically, projected expansion increases water consumption and can elevate water-stress impacts in certain regions like Nebraska, Kansas, Colorado, Idaho, California and North Texas. Direct land use also increases overall, particularly for first-generation feedstocks such as corn and soybeans. These findings underscore the need for continued technological improvements and innovative strategies to manage resource demands as the industry scales and to support complementary deployment within the evolving U.S. energy system.

09 BIOMASS FUELS↗

Exploring the Meteorological Impacts of Surface and Rooftop Heat Mitigation Strategies Over a Tropical City

Different heat mitigation technologies have been developed to improve the thermal environment in cities. However, the regional impacts of such technologies, especially in the context of a tropical city, remain unclear. The deployment of heat mitigation technologies at city-scale can change the radiation balance, advective flow, and energy balance between urban areas and the overlying atmosphere. We used the mesoscale Weather Research and Forecasting model coupled with a physically based single-layer urban canopy model to assess the impacts of five different heat mitigation technologies on surface energy balance, standard surface meteorological fields, and planetary boundary layer (PBL) dynamics for premonsoon typical hot summer days over a tropical coastal city in the month of April in 2018, 2019, and 2020. Results indicate that the regional impacts of cool materials (CMs), super-cool broadband radiative coolers, green roofs (GRs), vegetation fraction change, and a combination of CMs and GRs (i.e., “Cool city (CC)”) on the lower atmosphere are different at diurnal scale. Results showed that super-cool materials have the maximum potential of ambient temperature reduction of 1.6°C during peak hour (14:00 LT) compared to other technologies in the study. During the daytime hours, the PBL height was considerably lower than the reference scenario with no implementation of strategies by 700 m for super-cool materials and 500 m for both CMs and CC cases; however, the green roofing system underwent nominal changes over the urban area. During the nighttime hours, the PBL height increased by CMs and the CC strategies compared to the reference scenario, but minimal changes were evident for super-cool materials. The changes of temperature on the vertical profile of the heat mitigation implemented city reveal a stable PBL over the urban domain and a reduction of the vertical mixing associated with a pollution dome. This would lead to crossover phenomena above the PBL due to the decrease in vertical wind speed. Therefore, assessing the coupled regional impact of urban heat mitigation over the lower atmosphere at city-scale is urgent for sustainable urban planning.

54 ENVIRONMENTAL SCIENCES↗

Impact of Orographic Drag Schemes on East Asia Rainfall

Current generation of climate models often has significant biases in mountainous regions where the gradient of elevation is steep, and the terrain is complex. Potential reasons for these biases include under‐representation of orographic drag process in climate models. In this study, we assess the impact of orographic drag on East Asia rainfall by comparing the impact of a new orographic drag scheme that considers 3D orographic anisotropy (3D‐oro) with a 2‐D scheme in a general circulation model. Two sets of simulations (medium‐range and seasonal forecast) are carried out for the comparison and validation against observation. It is shown that through local/remote forcing of the drag in the mountainous regions, the 3D‐oro alleviates part of the excessive rainfall in west Tibetan Plateau and parts of insufficient rainfall in Southeast China by about 25%∼50% in the January/winter forecasts; it alleviates about 25%∼50% of the rainfall bias in part of south Tibetan Plateau and of East Asia in the July/summer forecasts. The results suggest the importance of improved orographic drag process and its impact in climate modeling for those regions that are prone to significant impact of hydroclimate events.

orographic anisotropy↗

Impact of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on the US Puget Sound Regional Hydroclimate

El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) are two major modes of climate variability with global hydroclimate impacts. However, their impacts often depend on the local climate and geography, resulting in large regional differences. In this study, we examined the connection of ENSO and MJO to the hydroclimate conditions and extremes in the Puget Sound (PS) basin located in the US Pacific Northwest coast. The results indicate that ENSO significantly modulates the cold season temperature and temperature-mediated hydrologic processes. El Niño cold seasons feature less snow accumulation and intensified surface runoff, even if the precipitation amount is similar to La Niña cold seasons. Therefore, El Niño causes more snow drought (in the form of compound dry and warm snow drought) and shifts the surface runoff seasonality by reducing runoff in the subsequent warm season. MJO phases 6–7 trigger more extreme precipitation, temperature, snowmelt, and runoff in the PS region at 0–9–day lags, and such connections are robust regardless of how the ENSO signals are removed. Meanwhile, MJO modulates large-scale extreme weather systems (e.g., atmospheric rivers) with significant enhancement during phases 6–7. ENSO impacts have intensified in the 2001–2020 period, whereas MJO impacts showed some phase shift in this period. This study reveals ENSO and MJO phases 6–7 as useful predictors of the PS hydroclimate anomalies/extremes at seasonal and daily scales, respectively. Utilizing these findings holds the potential to improve regional water resources prediction and management.

ENSO↗

Machine Learning Calibration of Groundwater Table Depth in ELM: Impact on Land Surface Hydrology and Land‐Atmosphere Fluxes

Accurate representation of groundwater table depth (GWTD) is crucial for simulating hydrological cycling in Earth system models (ESM). Nevertheless, there is a notable gap in the literature regarding the validation of GWTD simulations in ESMs and their subsequent impact on downstream hydrological components. This study explores the calibration of parameterization of global GWTD using machine learning within the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Despite achieving significant gains in simulating GWTD through calibration, offline ELM simulations unexpectedly show that these improvements do not translate to substantial enhancements in model performance for other key hydrological variables, including soil moisture (SM), runoff, groundwater contribution to runoff or base flow index (BFI), and evapotranspiration and its partitioning. The performance in SM and runoff was even degraded in some regions, while BFI was mostly overestimated. Although there is significant improvement in GWTD within the critical range of 1–5 m, where groundwater traditionally influences land surface energy fluxes, these improvements occurred mostly in humid areas where the impact of GWTD on surface processes is minimal. Although the impacts of model calibration are generally small in offline ELM simulations, coupled land-atmosphere simulations exhibit much stronger responses to GWTD calibration, highlighting the role of land-atmosphere feedbacks in Earth system modeling. These findings underscore the need for integrated calibration strategies that simultaneously optimize multiple hydrological variables. However, if a single-variable approach is necessary, it is crucial to establish clear priorities for calibration, identifying the most critical variables that have the greatest impact on overall model performance.

Fang, Yilin [Pacific Northwest National Laboratory↗

A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

36 MATERIALS SCIENCE↗

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗

Nature-based solutions in mountain catchments reduce impact of anthropogenic climate change on drought streamflow

Quantifying how well Nature-based Solutions can offset anthropogenic climate change impacts is important for adaptation planning, but has rarely been done. Here we show that a widely-applied Nature-based Solution in South Africa – invasive alien tree clearing – reduces the impact of anthropogenic climate change on drought streamflow. Using a multi-model joint-attribution of climate and landscape-vegetation states during the 2015–2017 Cape Town “Day Zero” drought, we find that anthropogenic climate change reduced streamflow by 12–29% relative to a counterfactual world with anthropogenic emissions removed. This impact on streamflow was larger than corresponding reductions in rainfall (7–15%) and reference evapotranspiration (1.7–2%). Clearing invasive alien trees could have ameliorated streamflow reductions by 3–16% points for moderate invasions levels. Preventing further invasive alien tree spread avoided potential additional reductions of 10–27% points. Total clearing could not have offset the anthropogenic climate change impact completely. Invasive alien tree clearing is an important form of catchment restoration for managing changing hydroclimatic risk, but will need to be combined with other adaptation options as climate change accelerates.

54 ENVIRONMENTAL SCIENCES↗