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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Potential benefits and optimization of cool-coated office buildings: A case study in Chongqing, China

Increasing envelope facet albedos considerably reduces solar heat gain, thus yielding building cooling energy savings. Few studies have explored the potential benefits of utilizing cool coatings on building envelopes (“cool-coated buildings”) based on life-cycle cost analysis. A holistic approach integrating the field testing, building energy simulation, and a 20-year life-cycle-based optimization was developed to explore cool-coated building performance and the maximum net savings of optimal building envelope retrofit and design. Experimental results showed that applying cool coatings to a west wall of an office building in Chongqing, China reduced its exterior surface temperature by up to 9.3 °C in summer. Additionally, simulation results showed that in Chongqing, making the roof and walls cool could reduce annual HVAC electricity use by up to 11.9% in old buildings (with poorly insulated envelopes) and up to 5.9% in new buildings. Retrofitting old buildings with a cool roof provided the net savings per modified area with present values up to 42.8 CNY/m2; retrofitting a new building with a cool roof or cool walls was not cost-effective. Optimizing both envelope insulation and envelope albedo can achieve 5.6 times the net savings of optimizing the insulation only, and 1.6 times that of optimizing albedo only.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Metal additively manufactured wavy fin cold-plate architecture for improved thermal-hydraulic performance

Rapid growth in artificial intelligence and data center workloads demands high-performance liquid cooling to manage increasing chip power. This study presents two metal-additive-manufactured cold plates with sinusoidal fins, constant-amplitude wavy fins and linearly variable-amplitude wavy fins and compares them against metal-additive-manufactured straight fins using experiments conducted at 1 kW heat dissipation as well as high-fidelity 3D conjugate computational fluid dynamic simulations. The cold plates were printed in AlSi10Mg material and underwent design using a Python-automated workflow prior to manufacture and testing. The experiments show that wavy fins reduce the normalized thermal resistance by 35 to 45 % at water flow rates from 1 to 4 LPM. At a fixed 20 kPa pressure drop, the variable-waviness design lowered peak surface temperature by 9 °C and thermal resistance by 51 %, while edge-channel maldistribution in the constant wavy fin design limited gains. A thermal resistance breakdown revealed that 55–63 % of the total thermal resistance in wavy designs comes from base heat conduction, 27–33 % from fin heat conduction, and 9–13 % from fin heat convection, indicating the need to address conduction bottlenecks. Parametric sweeps identify a 3 mm fin pitch as optimal, and that horizontal inlet/outlet manifolds further reduce pressure drop by 30–60 % and thermal resistance by 9–16 % relative to vertical inlet-outlet manifolds. The results yield comprehensive guidelines for fin geometry, manifold alignment, material selection and additive-manufacturing constraints to realize high-performance liquid-cooled cold plates for power-dense electronics.

3d printing↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Explosion behavior investigation and safety assessment of large-format lithium-ion pouch cells

We report large-format lithium-ion (Li-ion) batteries with high energy density for electric vehicles are prone to thermal runaway (or even explosion) under abusive conditions. In this study, overcharge induced explosion behaviors of large-format Li-ion pouch cells with Li[Ni 0.8 Co 0.1 Mn 0.1 ]O 2 cathode at different current rates (C-rates) (0.5C, 1C, 2C) were investigated. The explosion characteristics of the cells were elucidated by discussing the evolution of the cell voltage, the surface temperature and the shock wave pressure. Generally, the whole overcharge process could be divided into four stages according to the evolution of several key parameters and the overcharge behaviors; the overcharge C-rate has a great influence on cells’ thermal behaviors. The experimental results showed that the thermal runaway process of Li-ion cells caused by overcharging consisted of two kinds of explosions, physical explosion and chemical explosion. The existence of observable negative pressure zone in the pressure curves indicated that the Li-ion cells are not a self-supplying oxygen system during the explosion. Further, the explosion dynamics parameters were matched. An explosion TNT-equivalent conversion strategy that depended on the pressure of the shock wave was utilized to evaluate the released energy and its hazards. In addition, with respect to the overcharge of Li-ion pouch cells, a safety assessment method and a safety management method were proposed based on the explosion behaviors. From the perspective of battery safety, this study is of great significance for the safety design of Li-ion cells and can provide guidance for engineers to optimize the safety function of battery packs.

25 ENERGY STORAGE↗
DOE OSTI2022

Impacts of climate change on future hurricane induced rainfall and flooding in a coastal watershed: A case study on Hurricane Harvey

The warming climate is likely to increase hurricane-associated extreme rainfall and lead to sea-level rise (SLR). Thus, how the floods induced by intense hurricanes respond to these potential changes is of great concern. This study investigates the future warmer climate impacts on hurricane-induced extreme rainfall, and—more importantly—the subsequent compound flooding at the watershed scale (from an event-based analysis perspective). To this goal, a modeling framework is designed based on the Distributed Hydrology Soil Vegetation Model (DHSVM), the Two-Dimensional fvand the Regional Community Earth System Model (R-CESM). The framework was applied to Hurricane Harvey (2017) at the Clear Creek watershed (a coastal watershed in the southern Houston) as a case study. The results show that the projected maximum rainfall totals over the watershed would be exacerbated by 17.7 % and 49.7 % in the 2050s and 2090s (respectively) under Representative Concentration Pathway 8.5 (RCP 8.5). This means a 16.1 % increase in Harvey rainfall over the watershed per degree Celsius increase in Mean Surface Temperature over the Gulf of Mexico region (18°~31° N, 77°~98° W). Meanwhile, the increases in maximum inundation extent would be 11.0 % (2050s) and 19.5 % (2090s). Furthermore, considerable increases in maximum inundation depth and duration in regions along the middle and downstream of Clear Creek (and also those around Clear Lake) are expected. The projected SLR will have little effect on the maximum inundation depth and extent if storm surge changes are not taken into account; meanwhile, it will influence the inundation duration at downstream locations. In conclusion, this modeling framework can be also applied at other coastal watersheds to evaluate the projected climate change impacts on the compound flooding induced by extreme climate events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Characterization of modeling and experimental data inconsistencies from burst testing for high-burnup commercial fuel rod applications

Fuel fragmentation, relocation, and dispersal are some of the largest issues remaining in the nuclear industry before rod-average burnup can be increased beyond 62 GWd/tU. The issue is primarily related to the potential for fuel to be dispersed into the reactor primary system, which may increase public risk. One way to prevent dispersal is to avoid cladding burst. The objective of this work is to support the high burnup safety case by evaluating cladding burst under high-burnup, full-length fuel rods and to identify uncertainties that could improve model predictions. The results of this analysis will evaluate realistic, prototypic loss-of-coolant accident (LOCA) conditions; support future cladding burst test designs; and inform the development of mechanistic material models. Realistic high-burnup operating conditions were implemented in the BISON fuel performance code to simulate steady-state and LOCA transient fuel rod evolution to the point at which cladding burst occurred. Parametric studies are performed to assess code response to changes in rod internal pressures and heating rates. Results were compared with simulated LOCA experiments to identify inconsistencies between commercial fuel rod analysis and experimental validation. The representative full-length fuel rod LOCA simulation results did not agree with cladding burst tests. Cladding burst tests indicated burst occurring well below (100–150 °C) those calculated in the full-length fuel rod LOCA analysis. Further investigation indicated that the cladding burst tests do not appear to be representative for full-length fuel rods. The inconsistency investigated in this work showed that the differences between the BISON simulation and the experiment's cladding burst conditions arise from an incomplete characterization of the cladding surface temperature, detailed rodlet characterization, lack of cladding strain measurements, and uncertainty in the cladding creep and failure models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Keratinophilic fungi: Specialized fungal communities in a desert ecosystem identified using cultured-based and Illumina sequencing approaches

Soil fungi in desert ecosystems are adapted to harsh environmental conditions such as high soil surface temperatures and limited organic matter and water. Given limited carbon inputs from plant material, heterotrophic fungi likely use unconventional sources of carbon in these systems. A baiting method was used to culture keratinophilic fungi from biocrust and rhizosphere soils in an arid grassland in Utah, USA. Fungi were baited using llama and sheep wool, horsehair, and snakeskin on two media, and pure cultures were identified using ITS and LSU rRNA sequences. One hundred-eighteen fungal colonies were grown, representing a total of 32 Operational Taxonomic Units (OTUs) at 97 % similarity. Cultures were dominated by the phylum Ascomycota (88 %) followed by Mucoromycota (8.6 %) and Basidiomycota (3.4 %). The orders Pleosporales, Eurotiales, Hypocreales, and Sordariales were commonly isolated, with the dominant taxa Alternaria (27 %), Aspergillus (22 %), Fusarium (11 %), and Chaetomium (8%). Thirty percent of the fungi isolated have the capacity to degrade keratin in vitro using a keratin azure assay, with Penicillium showing the highest degradation followed by Geomyces, Alternaria, and Fusarium. Although keratin degraders can be infectious, dermatophytes associated with skin infections were not isolated in culture or detected in Illumina sequencing. Illumina sequencing was used to determine general patterns in seasonal variation and habitat preference of keratinophiles. Alternaria was the most abundant genus with >70 % of the sequences. The combination of Illumina data with culture-dependent approaches facilitated the characterization of a specialized community and confirmed the low abundance of dermatophytes in this arid site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Survey of tungsten gross erosion from main plasma facing components in WEST during a L-mode high fluence campaign

An initial high fluence campaign was performed in WEST, in 2023, on the newly installed actively cooled tungsten divertor composed of ITER-grade monoblocks. The campaign consisted in the repetition of a 60 s long Deuterium L-mode pulse in attached divertor conditions, cumulating over 10000s of plasma exposure. A maximum deuterium fluence of approximately 5⋅1⁢026 m−2 was reached in the outer strike point region, representative of a few high performance ITER pulses. Gross tungsten erosion inferred from visible spectroscopy shows that the most eroded plasma facing component is the inner divertor target with rates ten times larger than on the outer divertor target. The outer midplane tungsten bumpers, located a few centimeters from the plasma, show gross erosion rates two times lower than at the outer divertor. We conclude that the outer midplane bumpers have a negligible contribution to the long range tungsten migration and deposition onto the lower divertor. The cumulated gross erosion rate on the inner divertor translates in an effective gross erosion thickness of about 20μ⁢m, while it is about 2μ⁢m for the outer divertor. Strikingly, these orderings coincide with the thickness of deposits found locally on the divertor: the exposed surfaces of high field side monoblocks are covered with several tens of μ⁢m tungsten deposits, while on the lower field side, few μ⁢m thin tungsten deposits are only found on the magnetically shadowed parts of monoblocks. The strong impact of those deposits on WEST operation, namely perturbation of surface temperature measurement with infra-red thermography, and the emission of flakes causing radiative perturbation of the confined plasma, calls for anticipating similar issues in ITER. In particular, the start of research operation shall consider the definition of a divertor erosion budget in order to anticipate the formation of deleterious deposits.

Fedorczak, N.↗
DOE OSTI2026

Innovative laser-based methods for monitoring fuel retention in ITER

This paper addresses the challenge of tritium inventory management in ITER and future fusion reactors, highlighting the importance of accurate tritium measurement and its spatial distribution within the vacuum vessel. Given ITER’s operational constraints, especially the limit on tritium retention, precise measurement is essential for both safety and regulatory compliance. To tackle these questions, the paper presents the T-monitor diagnostic system developed by Forschungszentrum Jülich, which uses Laser-Induced Desorption (LID) in combination with Diagnostic Residual Gas Analysis (DRGA) to measure hydrogen isotope concentrations on the surface of divertor tiles. The system integrates a high-power laser, advanced optical components, and a Fast Scanning Mirror Unit (FSMU) for accurate laser spot positioning with rapid response. Designed to measure in situ tritium retention, the diagnostic provides high-resolution spatial mapping, vital for evaluating detritiation strategies. The laser heating process increases the divertor surface temperature to 1600 K within the laser spot, promoting hydrogen isotope desorption. Accurate measurements require the precise control of laser parameters, including pulse duration and spot size, with a target relative accuracy of 20%. The optical design includes both in-vessel and ex-vessel components, such as durable high-reflectivity mirrors made of gold and copper, selected not only for their infrared performance but also for their transmission of visible wavelengths for observation purposes. To protect optical components from contamination, a pneumatic shutter is used.

Hydrogen isotopes↗
DOE OSTI2021

Diagnosing nuclear power plant pipe wall thinning due to flow accelerated corrosion using a passive, thermal non-destructive evaluation method: Feasibility assessment via numerical experiments

Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Design of a prototypical natural circulation water-based reactor cavity cooling system (RCCS) for a pebble-bed generic FHR

A prototypical natural circulation water-based reactor cavity cooling system (RCCS) for the UC Berkeley Mark-1 pebble-bed gFHR is designed based on one-dimensional thermal hydraulics modeling and optimization implemented in an in-house MATLAB code. The model employs a lumped core consisting of fuel pebble and graphite pebble regions, while the graphite reflector, vessel, and RCCS are represented as separate but energy-coupled regions. The model is derived based on steady state energy balance equations accounting for conductive heat transfer from the lumped core to the vessel and subsequent radiative heat transfer to the RCCS and convective heat transfer in the water. Further, mass flow rate is calculated based on momentum and thermal energy balance in the RCCS. Starting with ANL’s water NSTF as baseline, effects of RCCS design parameters such as size of the plate between pipes, pipe diameter, source-to-sink distance, surface emissivity, and pitch from the core are examined in an integral effects framework which accounts for subsequent variation of the mass flow rate, number of pipes, convection coefficient, mean region and surface temperatures, radiative view factors, and water outlet temperature. The importance of including a prototypical reactor design in RCCS design calculations is emphasized as the calculations show that temperature drops from the core to the RCCS fluid to transfer the heat tend to be the limitation not the heat removal capacity of the fluid. A closed-loop design is obtained with capability to safely remove up to 0.72% of the nominal reactor power at maximum estimated peak conditions and 0.40% at shutdown. The design is based on physics calculations and does not account for economic optimization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Ocean circulation and climate variability in the northern South China Sea during the Greek Minimum derived from coral Δ 14 C and Sr/Ca records

Ocean circulation transports heat, salt and nutrients, and has profound impacts on the marine environment and climate change. However, the seasonal to centennial variations of the paleocirculation of the South China Sea (SCS) and their driving forces are still unclear due to limited records. Here we reconstructed a high-resolution Δ 14 C record of a coral from Sanya to investigate the seasonal variations of the SCS ocean circulation around 2300 cal yr BP during a grand solar minimum (GSM) period. The seasonal Δ 14 C variability shows the influence of coastal upwelling caused by the East Asian Summer Monsoon (EASM) in summer and the effect of water intrusion from the western Pacific Ocean driven by the Kuroshio Intrusion (KI) in winter. Here we compiled the marine radiocarbon reservoir correction (ΔR) records since 2500 cal yr BP, which indicate a gradual decrease of upwelling significantly correlated with the EASM. Comparisons with other climate records suggest that both the EASM and KI may regulate the ocean circulation variability on centennial time scales. As a good index of the sea surface temperature (SST), our Sr/Ca record and spectral analysis results show a low temperature and a low frequency of El Niño-Southern Oscillation (ENSO) events around 2300 cal yr BP. Moreover, the composite coral Sr/Ca-SST and ENSO index records since 2500 cal yr BP show relatively low SST and weak ENSO during GSMs, supporting the modulation of tropical SST and ENSO by solar activity. Our study has provided high-resolution proxy data and revealed the driving forces of ocean circulation and climate change in the SCS at multiple time scales, which should be considered in further modeling work.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗
DOE OSTI2021

A mechanistic understanding of oxygen isotopic changes in the Western United States at the Last Glacial Maximum

At the Last Glacial Maximum (LGM), records suggest drier conditions in the northwest United States and wetter conditions in the southwest United States relative to present-day as well as widespread changes in the isotopic composition of water. However, the mechanisms responsible for these changes remain ambiguous. Here, we explore differences in western United States hydroclimate between the LGM and preindustrial with a water isotope tracer enabled Earth System Model. We then use proxy forward models to compare simulated and recorded δ 18 O in speleothems. We find that the pattern of hydroclimate response in the western United States at the LGM relates to a combination of 1) increased frequency and southward shifted wintertime extratropical cyclones in the North Pacific, 2) greater rainout of moisture as it moves over the continent, and 3) reduced evaporation in the cooler LGM climate. The simulated lower δ 18 O of precipitation at the LGM relates predominantly to an increase in cool season moisture removal efficiency, with a secondary contribution from relatively more cool season precipitation. Both surface temperatures and North American ice sheets contribute to these hydroclimate changes at the LGM. Comparisons between δ 18 O from proxy forward models and speleothem records in the western United States show general agreement at the LGM, with increasing depletion moving towards the continental interior. Finally, this study highlights the similarities and differences between hydrologic and δ 18 O changes at the LGM and emphasizes the utility of model-proxy comparison for interpretation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Estimation of intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method

Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.

Indicator↗
DOE OSTI2025

Compounding effects of Lake and urbanization on summer precipitation in the Greater Chicago area

Here, this study explores the impacts of Lake Michigan and Chicago's urbanization on precipitation patterns over the Greater Chicago Area, using 22 years of observational data and Weather Research and Forecasting (WRF) model simulations focused on an early summer rain event. Observational analysis reveals that urban areas consistently experience more precipitation than the adjacent southern Lake Michigan region throughout the year, particularly before 2015. However, this disparity has narrowed since 2016 due to a more rapid increase in heavy precipitation over the lake compared to urban areas. Specifically, lake precipitation has risen by 25 mm per year, compared to 15 mm per year over urban areas. Additionally, the number of days with precipitation exceeding 5 mm per day has been rising at a rate of 1.34 days per year over the lake and 0.84 days per year over urban areas. Modeling experiments reveal that both urbanization and lake effects, including lake breezes, enhance precipitation over urban areas, primarily through convergence induced by interactions between land and lake breezes. In contrast, these same factors suppress precipitation over the lake. The suppression results from Lake Michigan's stable environment, characterized by cooler surface temperatures, limited evaporation in early summer, and a high-pressure anomaly over the lake driven by urban heating, which creates upward motion over urban areas and downward motion over the lake, further influencing precipitation patterns.

Coastal urban↗
DOE OSTI2026

Numerical Analysis of Offshore Wind-Farm-Induced Drag Effects on Coastal Upwelling Dynamics

Wind farms extract momentum from the atmospheric flow, generating wind-speed deficits both within the plant, and extending downstream. When located offshore, these deficits modulate air-sea coupling, potentially impacting coastal upwelling in sensitive regions. We investigate impacts of wind farms on coastal upwelling using kilometre-scale, three-way-coupled simulations with the coupled ocean-atmosphere-wave-sediment transport system for the US West Coast. Wind-farm effects are represented by a generalised turbine drag formulation, an idealised, height-dependent body force whose magnitude is systematically varied. This approach isolates the leading-order fluid-dynamical response in a realistic coastal configuration. The atmospheric adjustment exhibits an approximately linear relation between drag force and wind-speed deficit, with wakes that expand downstream and increase in magnitude as drag increases. An empirical orthogonal function analysis of sea-surface-temperature anomalies reveals the emergence of a canonical dipole pattern under strong drag forcing. Subsurface diagnostics show consistent shoaling of the mixed layer and suppressed upward velocities in areas near wind-farm region, accompanied by compensating enhancements of shoaling closer to the coast. These results identify turbine drag as a control parameter in assessing interactions between wind-farm wake and coastal upwelling and provide scaling relationships for understanding offshore wind-farm effects on the coastal circulation dynamics.

16 TIDAL AND WAVE POWER↗