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Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US
Abstract Burned areas in the western US have increased ten‐fold since 1980s, which are attributable to multiple factors, including increasing heat, changing precipitation patterns, and extended drought. To better understand how these factors contribute to large fire emissions (gridded monthly fire emissions >95th percentile of all the fire emissions in the western US; 0.009 Gg/month), we build a machine learning model to predict fire emissions (PM 2.5 ) over the western US at 0.25° resolution, interpreted using explainable artificial intelligence (XAI). From the predictor contributions derived from XAI, we conduct k‐means clustering analysis to identify four clusters of predictor variables representing different drivers of large fire emissions. The four clusters feature the contributions of fuel load (Cluster 1) and different levels of dryness (Cluster 2–4), controlled by fuel moisture, drought condition, and fire‐favorable large‐scale meteorological patterns featuring high temperature, high pressure, and low relative humidity. In the past two decades, large fire emissions peak in summer. However, large fire emissions increased significantly in September and October in 2010–2020 relative to 2000–2009, extending the peak large fire emissions from summer to autumn. The larger enhancements of large fire emissions during autumn compared to summer are contributed by decreased fuel moisture, along with more frequent concurrent fire‐favorable large‐scale meteorological patterns and drought. These results highlight fuel drying as a common driver supported by multiple drivers, such as warmer temperature and more frequent synoptic patterns favorable for fires, in increasing the autumn risk of large fire emissions across the western US.
Sink or Swim: A Tutorial on the Control of Floating Wind Turbines: Preprint
Within the rapidly growing wind energy sector, floating offshore wind turbines are expected to be the fastest growing portion. This is largely driven by the immense offshore wind resources that are mostly over deep water, where fixed-bottom concepts become cost prohibitive. However, compared to fixed-bottom wind turbines, floating wind turbines are more dynamic and exhibit potential instabilities, which requires advanced control technologies to ensure a safe and efficient operation. Beyond their existing objectives of maximizing power production while minimizing structural loads, floating wind turbine controllers must also avoid large platform oscillations and accommodate wave disturbances. This paper provides an overview of the challenges and opportunities in the control of floating offshore wind energy systems.
Multiaxial Plastic Deformation of Zircaloy-4 Nuclear Fuel Cladding Tubes
Here, this work is motivated by the desire to devise an internal pressure test that can mimic a displacement-controlled loading scenario and demonstrate how to apply the multiaxial stress and strain data from the test to develop an elastic/plastic constitutive model for a thin-walled tubular component. This is achieved by conducting simultaneous measurements of tangential and axial strain during the pressure test and integrating these strain measures into a feedback loop with the pressure controller. It is shown how data from such a test can be used to develop a large mechanical property data set relevant to biaxial loading conditions. The data obtained have high confidence evidenced by their low variability and alignment with other literature studies. Additionally, data from these internal pressure tests combined with full-tube axial tensile tests allow for the derivation of the Hill anisotropic yield function. The developed Hill yield function is validated by comparing the plastic strain ratios from the full-tube tension tests and by comparing the predicted yield stress in the tangential direction with measured values from ring tension tests in a previous study.
Controlled Enzyme Cargo Loading in Engineered Bacterial Microcompartment Shells
Bacterial microcompartments (BMCs) are nanometer-scale organelles with a protein-based shell that serve to colocalize and encapsulate metabolic enzymes. They may provide a range of benefits to improve pathway catalysis, including substrate channeling and selective permeability. Several groups are working toward using BMC shells as a platform for enhancing engineered metabolic pathways. The microcompartment shell of Haliangium ochraceum (HO) has emerged as a versatile and modular shell system that can be expressed and assembled outside its native host and with non-native cargo. Further, the HO shell has been modified to use the engineered protein conjugation system SpyCatcher–SpyTag for non-native cargo loading. Here, we used a model enzyme, triose phosphate isomerase (Tpi), to study non-native cargo loading into four HO shell variants and begin to understand maximal shell loading levels. We also measured activity of Tpi encapsulated in the HO shell variants and found that activity was determined by the amount of cargo loaded and was not strongly impacted by the predicted permeability of the shell variant to large molecules. All shell variants tested could be used to generate active, Tpi-loaded versions, but the simplest variants assembled most robustly. We propose that the simple variant is the most promising for continued development as a metabolic engineering platform.
An airfoil-based synthetic actuator disk model for wind turbine aerodynamic and structural analysis
Here, this study introduces an airfoil-based refinement technique to enhance the Actuator Disk Model (ADM) for improved wind turbine aerodynamic load prediction and structural simulation in conjunction with Large Eddy Simulations of the wind flow. While ADM offers higher computational efficiency than the more detailed but resource-intensive Actuator Line Model (ALM), it traditionally lacks the resolution needed to capture the localized blade forces accurately. To address this limitation, we introduce a refinement technique that uses airfoil-specific data and employs interpolation-based grid point refinement, achieving ALM-comparable accuracy while preserving ADM's efficiency. Unlike conventional ADM that provides only rotor-disk averaged forces, our synthetic method tracks transient aerodynamic load variations over multiple blade revolutions, allowing us to calculate the distributions of maximum and minimum loads during typical cycles. Applied to the NREL 5 MW reference turbine, our enhanced ADM accurately predicts key aerodynamic parameters (angle of attack, axial velocity, lift, drag, axial and tangential forces along the blades) as well as structural responses (blade tip deflection, maximum stress, and stress concentration). Our results show that the tip deflection ranges from 2.33m (3.69 % of blade length) to 4.28m (6.79 %), with maximum stress concentration occurring near the blade root. This research demonstrates that a refined synthetic ADM approach can serve as a computationally efficient alternative for both aerodynamic analysis and structural simulation of wind turbine blades subjected to realistic wind fields.
An Analytical Model for the Tension-Shear Coupling of Woven Fabrics with Different Weave Patterns under Large Shear Deformation
It is essential to accurately describe the large shear behavior of woven fabrics in the composite preforming process. An analytical model is proposed to describe the shear behavior of fabrics with different weave patterns, in which tension-shear coupling is considered. The coupling is involved in two parts, the friction between overlapped yarns and the in-plane transverse compression between two parallel yarns. By introducing the concept of inflection points of a yarn, the model is applicable for fabrics with different weave patterns. The analytical model is validated by biaxial tension-shear experiments. A parametric study is conducted to investigate the effects of external load, yarn geometry, and weave structure on the large shear behavior of fabrics. The developed model can reveal the physical mechanism of tension-shear coupling of woven fabrics. Moreover, the model has a high computational efficiency due to its explicit expressions, thus benefiting the material design process.
New large-strain FFT-based formulation and its application to model strain localization in nano-metallic laminates and other strongly anisotropic crystalline materials
This paper presents a new robust large-strain (LS) elasto-viscoplastic (EVP) formulation based on Fast Fourier Transforms (FFTs) for the prediction of the micro-mechanical response and microstructure evolution of polycrystalline and multiphase materials, with emphasis on the effect of strong crystallographic and/or morphologic anisotropy on localization of plastic deformation. In this work, the novel LS-EVPFFT formulation allows treatment of complex initial geometries and large deformations considering three grids of material points: a regular grid in the reference configuration, where FFTs can be performed; an irregular grid in the initial configuration, created by applying a stress-free displacement field to the reference regular grid; and an irregular grid in the current configuration, undergoing large strains and rotations as the material is loaded. Further numerical stability of the new formulation also required the use of a novel expression for the discrete modified Green’s operator, which reduces spurious field oscillations. After presenting and validating the new formulation by comparison with preexisting implementations and analytical solutions, LS-EVPFFT is applied to the prediction of slip and kink bands formation in polycrystalline columnar ice, and kink bands in single crystal zinc wires, showing good agreement with classic experiments. Finally, the model is used to study kink band formation during compression of Cu–Nb nano-metallic laminates (NMLs), in which accurate treatment of the complex geometry associated with the tortuosity of interfaces and large deformations become critical, showing consistency with corresponding micropillar experiments.
Hanover LED Streetlight Conversion
On May 8, 2017, Hanover residents voted overwhelmingly to transition to 100% renewable electricity by 2030 and heating, cooling and transportation by 2050. In so doing, we became the first municipality in the country to make this commitment by popular vote. To accomplish these goal town leaders developed a plan to address first transitioning municipal energy uses to renewable forms of energy, through on-site generation of the municipal electrical load, load reduction through energy efficiency improvements to town facilities and infrastructure. Significant progress has been accomplished in the generation of energy with 90%+ of the 2024 municipal load being offset by generation and through various energy efficiency projects. The utility owned street lighting was identified as a large energy consumer of 165 MWh annually (7% of the annual municipal load) due to old inefficient high-pressure sodium and mercury vapor streetlights with no ability to reduce wattage during periods with low lighting needs. The Hanover LED Streetlight Replacement Project replaced utility owned streetlights with town owned controllable (dimmable and trimmable) LED lights that can be adjusted by location and time of night. Coupled with ownership of the streetlights transfer to the town, the town’s share of the project pay back is 11 months and for the complete project including federal share the payback is approximately 3 years. With the completion of lighting upgrade, the annual streetlighting energy load has dropped by 60% to 67 MWh in the first year of operation (7/1/24 - 6/30/25). This amounts to an approximate $90,000 annual savings or a local 1% tax rate impact. Additionally, over 200,000 pounds of CO2 will be saved annually.
Heat Pumps with Phase Change Thermal Storage: Flexible, Efficient, and Electrification Friendly
As we continue to electrify space- and water-heating, the electricity demand profile of many buildings will change significantly, and periods of high electricity demand will likely not align with renewable energy generation. We expect electricity demands will increase substantially in the winter, annual maximum electricity demands will increase, and more regions will experience annual peak electrical demands – and higher wholesale electricity prices – in the winter. This is especially important for cold climates where 60% of site energy use in buildings is for heating, and where heat pumps perform least efficiently. This paper focuses on one promising solution among the many paths to electrification: the use of phase change materials (PCM) for compact low-cost thermal energy storage (TES). We present the design and simulation of a combi heat pump and phase change thermal storage system used for space- and water-heating in a multifamily residence in a cold climate. To assess the benefits of this technology, we compare its annual performance to that of a current state-of-the-art air-to-air heat pump and separate heat pump water heater. Simulation results for IECC Climate Zone 6A reveal that the combi heat pump with phase change thermal storage can reduce the design size for heat pumps by 40-60%, reduce maximum electric demand by 30-50%, reduce electricity use during 4-12-hour load shed periods by 50%, and avoid the need for auxiliary electric resistance for both space- and water-heating. Tariff structures are highly varied between different utilities and currently reflect higher wholesale market prices for electricity during summer days. Consequently, although this system design provides large electric demand reductions during hypothetical 4-12-hour load shed periods, it does not provide energy cost reductions with current winter residential time-of-use tariffs.
Intracellular Biomacromolecule Delivery by Stimuli-Responsive Protein Vesicles Loaded by Hydrophobic Ion Pairing
Proteins can perform ideal therapeutic functions. However, their large size and significant surface hydrophilicity and charge prohibit them from reaching intracellular targets. These chemical features also render them poorly encapsulated by nanoparticles used for intracellular delivery. In this work, a novel combination of protein vesicles and hydrophobic ion pairing (HIP) was used to load protein cargo and achieve cytosolic delivery to overcome the limitations of previous protein vesicle properties. Protein vesicles are thermally self-assembling nanoparticles made from elastin-like polypeptide (ELP) fused to an arginine-rich leucine zipper and a globular protein fused to a glutamate-rich leucine zipper. To impart stimuli-responsive disassembly, physiological stability, and small size, the ELP sequence was modified to include histidine and tyrosine residues. HIP was used to load and release protein cargo requiring endosomal escape for cytosolic function. HIP vesicles enabled delivery of cytochrome c, a cytosolically active protein, and a significant reduction in viability in both a traditional two-dimensional (2D) human cancer cell line culture and a biomimetic three-dimensional (3D) organoid model of acute myeloid leukemia. By examining the uptake of positively and negatively charged fluorescent protein cargos loaded by HIP, this work revealed the necessity of HIP for cytosolic cargo delivery and how HIP loading influences protein vesicle self-assembly and disassembly using microscopy, small-angle X-ray scattering, and nanoparticle tracking analysis. HIP protein vesicles have the potential to broaden the use of intracellular proteins as therapeutics for various diseases and extend protein vesicles to deliver other biomacromolecules, as the strategy developed here resulted in the first cytosolic protein cargo delivery using protein vesicles.
Efficient Loads Surrogates for Waked Turbines in an Array
Accurately and efficiently predicting wind turbine structural loading is a crucial step in wind farm design. Without considering structural loading, wind farm optimization could negatively impact turbine fatigue and ultimate loads, especially for waked and partially waked turbines, which could result in higher maintenance costs and reduced turbine lifetime. However, predicting turbine loads throughout an array is a costly step, as these quantities require time-accurate results across long time histories, which is often intractable for large array optimization. Therefore, surrogate models that link array spacing to load outputs are often used, but the surrogates are then unique to the inflow conditions and array configurations in the training library. This work develops surrogate models for many wind turbine load outputs based solely on rotor plane velocity measurements, with no required input about array configuration or freestream inflow parameters. Surrogate models were constructed for many turbine quantities of interest (QoI), considering mean, standard deviation, ultimate, and fatigue loads. In general, most QoI statistics were accurately captured, as measured by predicted vs. actual correlation coefficient, confirming the suitability of the approach. Temporal mean values of the QoI required only temporal mean measurements of the rotor plane inflow velocity. However, accurate prediction of temporal standard deviation, ultimate, and fatigue values of QoI also required temporal standard deviations of the rotor plane velocity field. Poor surrogate performance was observed when too many correlated inputs were used, such as multiple velocity components. If the fewest inflow parameters are used to construct the surrogates, the average correlation coefficient value for all output QoI statistics is 0.89. Surrogates for standard deviations and damage equivalent loads (DELs) of turbine QoIs generally had lower accuracy and tower-base and shaft load channels posed the most difficult to capture accurately. The results suggest that these surrogates could be easily paired with analytic wake models, which are frequently used for pre-construction wind farm array optimization, to account for turbine loading in addition to power production. By including the optimal inflow conditions, the surrogate accuracy can improve to an average correlation coefficient value for all output QoI statistics of 0.92. This work has established the ability to build accurate surrogates for mean, standard deviation, ultimate load, and DEL turbine QoI values based on the rotor plane inflow velocity, and identified which inflow conditions lead to greater surrogate accuracy.
Observational constraints on the multiphase nature of outflows using large spectroscopic surveys at z ∼ 0
ABSTRACT Mass outflow rates and loading factors are typically used to infer the quenching potential of galactic-scale outflows. However, these generally rely on observations of a single gas phase that can severely underestimate the total ejected gas mass. To address this, we use observations of high mass (≥1010 M⊙), normal star-forming galaxies at z ∼ 0 from the MaNGA, xCOLD GASS, xGASS, and ALFALFA surveys and a stacking of Na d, Hα, CO(1–0), and H i 21 cm tracers with the aim of placing constraints on an average, total mass outflow rate, and loading factor. We find detections of outflows in both neutral and ionized gas tracers, with no detections in stacks of molecular or atomic gas emission. Modelling of the outflow components reveals velocities of |vNa d| = 131 km s−1 and |vHα| = 439 km s−1 and outflow rates of $\dot{M}_{\rm {Na\,\small{D}}}$ = 7.55 M⊙ yr−1 and $\dot{M}_{\text{H}\alpha }$ = 0.10 M⊙ yr−1 for neutral and ionized gas, respectively. Assuming a molecular/atomic outflow velocity of 200 km s−1, we derive upper limits of $\dot{M}_{\text{CO}}\lt 19.43$ M⊙ yr−1 and $\dot{M}_{\rm {H\,\small {I}}}\lt $ 26.72 M⊙ yr−1 for the molecular and atomic gas, respectively. Combining the detections and upper limits, we find average total outflow rates of $\dot{M}_{\text{tot}}\lesssim$27 M⊙ yr−1 and a loading factor of ηtot ≲ 6.39, with molecular gas likely contributing ≲72 per cent of the total mass outflow rate, and neutral and ionized gas contributing ∼28 and <1 per cent, respectively. Our results suggest that, to first order, a degree of quenching via ejective feedback could occur in normal galaxies when considering all gas phases, even in the absence of an active galactic nucleus.
Large Area Detection of Microstructural Defects with Multi-Mode Ultrasonic Signals
Cyclic loading or other stresses can lead to development of cracks and crack growth in mechanical structures, leading to eventual failure. While ultrasound imaging can be used for non-destructive testing of such structures, conventional ultrasound techniques are often limited by crack size, density, and areal coverage. An effective characterization of real-world, large-area structures is required at an early damage stage to prevent catastrophic failure and predict remaining life. In this study, a new nonlinear ultrasonic testing (NUT) method is proposed for large-area monitoring of practical structures with arbitrary complexity by using multiple-mode guided-wave ultrasonic signals. The proposed guided-wave NUT technique requires single-element transducers, simple electronics, and a mixed time-frequency domain signal processing. As a proof-of-concept demonstration, numerical simulations and experiments are performed on an A36 carbon steel beam assembly with previously formed microstructural defects that cause nonlinearities in ultrasonic response. The quadratic dependence of the nonlinear wave excitation on the input ultrasonic signal amplitude is shown by numerical simulations, and such a nonlinear ultrasonic response is experimentally observed in the zone with a high density of microstructural defects.
Characteristics of Fluid‐Solid Interaction Constitutive Models Within Poroelastodynamics at Higher Strain‐Rates and Large Deformations Implemented in 1D
The large deformation, mixed formulation, finite element (FE) modeling approach presented in Irwin et al. 2024 is extended herein to include improved constitutive models for representing dynamic solid-fluid interactions at higher strain rates (𝒪(10 2 −10 3 )s −1 ) and larger overpressure magnitudes (𝒪(10 2 )kPa) within a biphasic soft porous material using Theory of Porous Media (TPM) at finite strain. Specifically, these constitutive modeling improvements are the following: (i) a more physically robust constitutive model for pore fluid seepage velocity via inclusion of pore fluid viscous stress, and (ii) a modified deformation-dependent-permeability model and updated hyperelastic constitutive model better suited for handling larger volumetric compressions and extensions. The novelty of the present work is mainly the contribution (i): inclusion of pore fluid viscous stress at higher strain-rate and large deformations, which requires 𝐶 1 continuity in the weak formulation, accomplished by employing Hermite cubic interpolation functions within a mixed nonlinear poromechanical finite element formulation. In (ii), the model is updated to weakly enforce solid phase incompressibility, such that this assumption is not violated numerically, which provides improved numerical stability for achieving larger overpressure magnitudes on 𝒪(10 2 ) kPa, which were not achievable with the previous Kozeny–Carman model in Irwin et al. 2024. Also in (ii), the volumetric part of the solid skeleton free energy function is modified to ensure proper bounds on the solid skeleton Jacobian of deformation 𝐽 s related to incompressibility of the solid phase. Uniaxial strain, unidirectional flow examples at higher strain rates (𝒪(10 2 −10 3 )s −1 ) and larger deformations (up to 0.2 (or 20%) nominal axial strain) demonstrate the improved physical representation—and numerical stability—of these constitutive model improvements.
California Price Response Potential Study
California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.
Distribution Substation Planning Toolkit (dsp-toolkit) v1.0
The Distribution Substation Planning Toolkit (DSP Toolkit) is a software suite designed to streamline the planning and optimization of distribution substations. This toolkit offers a comprehensive set of tools and APIs for data curation, short-term electric load forecasting, and weather-sensitive load adjustment, making it an essential resource for utility companies, engineers, and researchers. Features • Data Preprocessing and Curation: Efficiently manage and preprocess large datasets to ensure high-quality input for analysis. • Short-Term Load Forecasting: Utilize data-driven models to predict short-term electric loads accurately. • Weather-Sensitive Modeling: Automatically adjust load forecasts based on weather data to predict future peak demands more precisely. Uses The DSP Toolkit is ideal for planning and optimizing distribution substations, providing a user-friendly interface and comprehensive documentation. It is suitable for both novice and experienced users, facilitating efficient and accurate planning processes. Advantages • Efficiency: Automates complex planning tasks, reducing manual effort and minimizing errors. • Scalability: Handles large datasets and complex models, making it suitable for large-scale projects. • Community and Support: Open-source with active community contributions, ensuring continuous improvement and support. • Extensibility: Easily extendable with custom modules and plugins, allowing users to tailor the toolkit to their specific needs. The DSP Toolkit stands out by offering a robust, flexible, and user-friendly solution for distribution substation planning. Public Abstract