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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics

The U.S. Department of Energy National Energy Technology Laboratory's (NETL) 50 kW th chemical looping reactor (CLR) has an underperforming cyclone, which was designed using empirical correlations. To improve the performance of this cyclone using computational fluid dynamics (CFD)-based modeling simulations, four critical design parameters including the vortex tube radius and length, barrel radius, and the inlet width and height were optimized. Here, NETL's open source multiphase flow with interphase exchange (MFiX) CFD code has been used to model a series of cyclones by systematically varying the geometric design parameters. To perform the optimization process, the surrogate modeling and sensitivity analysis followed by the optimization capability in nodeworks was used. The basic methodology for the process is to employ a statistical design of experiments (DOE) method to generate sampling simulations that fill the design space. Corresponding CFD models are then created, executed, and postprocessed. A response surface is created to characterize the relationship between input parameters and the quantities of interest (QoI). Finally, the CFD-surrogate is used by an optimization method to find the optimal design condition based on the objective and constraints prescribed. The resulting optimal cyclone has a larger diameter and longer vortex tube, a larger diameter barrel, and a taller and narrower solids inlet. The improved design has a predicted pressure drop 11 times lower than the original design while reducing the mass loss by a factor of 2.3.

42 ENGINEERING↗
DOE OSTI2024

Dependence of tropical cyclone seeds and climate sensitivity on tropical cloud response

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth’s surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase.

Science & Technology - Other Topics↗
DOE OSTI2020

Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble

Future changes in tropical cyclone properties are an important component of climate change impacts and risk for many tropical and midlatitude countries. In this study we assess the performance of a multimodel ensemble of climate models, at resolutions ranging from 250 to 25 km. We use a common experimental design including both atmosphere-only and coupled simulations run over the period 1950–2050, with two tracking algorithms applied uniformly across the models. There are overall improvements in tropical cyclone frequency, spatial distribution, and intensity in models at 25 km resolution, with several of them able to represent very intense storms. Projected tropical cyclone activity by 2050 generally declines in the South Indian Ocean, while changes in other ocean basins are more uncertain and sensitive to both tracking algorithm and imposed forcings. Coupled models with smaller biases suggest a slight increase in average TC 10 m wind speeds by 2050.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Impacts of urbanization, antecedent rainfall event, and cyclone tracks on extreme floods at Houston reservoirs during Hurricane Harvey

The objective of this study is to evaluate the effects of urbanization, antecedent rainfall event (ARE), and varying cyclone tracks on the streamflow—and thus the subsequent reservoir status—during the floods caused by Hurricane Harvey in August-September 2017. Through a hydrological modeling approach, we examined how these factors influenced the inflows, peak pool elevations and outflows of the two most important detention reservoirs in the Houston region, the Addicks and Barker Reservoirs. A high-resolution rainfall reanalysis dataset for extreme storm events, along with a suite of synthetic rainfall values from a variety of storm tracks, were adopted to represent both the truth and the maximum possible rainfall during the Hurricane Harvey period. Results showed the following: Urbanization only led to slight increases in peak inflows, not necessarily leading to an increase in peak pool elevations. ARE contributed to the peak inflow and pool elevation slightly. In contrast, if the cyclone had followed the most adverse track consistent with earlier forecasts (all else being equal), the total volumetric flow into the two reservoirs could have been significantly larger (37.00% and 48.70% respectively), thus increasing the peak pool elevations by 1.06 and 1.37 meters respectively. These results suggest that large uncertainties exist for flood management at a watershed scale during hurricanes, because of the uncertainties with regard to the cyclone track. This is true even if storm-relative precipitation rates could be predicted perfectly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Polarimetric Signatures in Landfalling Tropical Cyclones

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the -15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mixed-phase clouds and precipitation in Southern Ocean cyclones and cloud systems observed poleward of 64°S by ship-based cloud radar and lidar

Mixed-phase clouds (MPCs), composed of both liquid and ice, are prevalent in Southern Ocean cyclones. A characterisation of these clouds on fine vertical scales is required in order to understand the microphysical processes within these clouds, and for model and satellite evaluations over this region. We investigated three examples of cloud systems collected by ship-mounted remote sensing instruments adjacent to East Antarctica at latitudes between 64°S and 69°S. These cases allow us to examine the properties of mid-level MPCs, with cloud tops between 2 and 6 km. Mid-level MPCs contain multiple layers of supercooled liquid water (SLW) embedded within ice during the passage of cyclones. SLW layers are capped by strong temperature inversions and are observed at temperatures as low as - 31°C. Convective generating cells (GCs) are present inside supercooled liquid-topped mid-level MPCs. The horizontal extent, vertical extent and maximum upward Doppler velocity of these GCs were 0.6 – 3.6 km, 0.7 – 1.0 km and 0.5 – 1.0 m s -1 , respectively, and are consistent with observations from previous lower-latitude studies. Ice precipitation is nearly ubiquitous, except in the thinnest clouds at the trailing end of the observed systems. Seeding of lower SLW layers from above leads to periods with either larger ice particles or greater ice precipitation rates. Additionally, periods of supercooled drizzle lasting up to two hours were observed toward the end of two of the three cyclone systems. This supercooled drizzle turns into predominantly ice precipitation as the result of seeding by ice clouds located above the precipitating SLW layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Method to Design and Optimize Axial Flow Cyclones for Gas–Liquid Separation

This article provides a detailed design guide, optimization, and performance assessment for air–water separation of an axial flow cyclone. Axial flow cyclones (also known as swirl tube demisters, mist eliminators, or Austin–Write cyclones) have a range of applications in several different industries. This method of gas–liquid separation offers many benefits. Among these are high liquid separation efficiencies (near 99%) and an inline design that allows them to be more easily fitted into existing piping structures. Despite these benefits, there are several design parameters that have not been optimized for performance in wastewater purification applications. This research fills the gap in the literature by quantifying the effect of new design parameters on water collection efficiency, η water collection , and the air bypass efficiency, η air bypass , defined as the ratio of the air mass flowrate exiting through the desired air outlet over the inlet air mass flowrate. A set of wide-ranging experiments were conducted to study the effects of gas–liquid flow rates, tube geometry, and relative injection angles to optimize the water collection and air bypass efficiencies. The water collection efficiency exceeded 99.8% when the liquid streamline came in direct contact with the water drainage exit. An empirical correlation was developed to predict the swirl pitch as a function of the above design parameters. Predictions from the correlation were within 10% of the experimental results. The correlation can be used to design highly efficient in-line gas–liquid separators.

Engineering↗
DOE OSTI2023

Measurements and Prediction of Ash Deposition in a Cyclone-Fired Boiler Operating under Variable Load Conditions

Measurements of ash deposition rates were made between the secondary superheater and reheater sections of a 450 MW cyclone-fired lignite boiler as the operational load varied from 33 to 100%. Significant reductions in deposition rates with a decrease in operational load were observed. To uncover the causative mechanisms behind these observations, operational data from the power plant were used to carry out computational fluid dynamic (CFD) simulations of the boiler. After ascertaining that the gas temperatures and velocities at various sections within the boiler were being represented adequately, decoupled simulations of the ash deposition process on the deposit probe were carried out using a finely resolved boundary layer mesh. Fly ash particle size distribution (PSD) and its concentration for the decoupled calculations were determined from stand-alone cyclone barrel simulations. The ash partitioning (mass %) between the fly ash and slag was found to be ~50:50, which was in line with previous field observations, and it did not vary significantly across different cyclone loads. The predicted PSD of the deposit ash was concentrated in the size range 10–30 microns, which was in agreement with cross-sectional images of the deposit obtained from the measurements. At lower loads, sharp variations in the deposition rates were predicted in the gas temperature range 950–1150 K. The particle kinetic energy—particle viscosity-based capture methodology utilized in this study in conjunction with appropriate ash compositions, ash viscosity models and gas temperature estimates can help estimate slagging propensities at different loads reasonably well in these systems.

Krishnamoorthy, Gautham (ORCID:0000000255205092)↗
DOE OSTI2021

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Regional MJO Modulation of Northwest Pacific Tropical Cyclones Driven by Multiple Transient Controls

The Madden–Julian Oscillation (MJO) is widely acknowledged for its ability to modulate Northwest Pacific tropical cyclones (TCs), but a complete understanding of the underlying mechanisms remains uncertain. Beyond established effects of the MJO's relative humidity envelope, other dynamical factors have recently been invoked via new genesis potential indices and high-resolution modeling studies. Here we revisit the ability of the MJO to modulate West Pacific TCs through a quasi-explicit cyclone downscaling strategy driven by composited observations, paired later with a genesis index to investigate regional drivers of modulation. In this work, we reveal two distinct spatial modes of TC modulation in which the MJO's dynamic and thermodynamic effects act in tandem to increase TCs. In the South China Sea, for instance, shear reductions associated with the MJO's circulation lead to increasing potential intensity ahead of the arrival of a positive humidity anomaly, all of which combine for an extended period of cyclogenesis favorability.

58 GEOSCIENCES↗
DOE OSTI2022

Subseasonal Tropical Cyclone Prediction and Modulations by MJO and ENSO in CESM2

Subseasonal tropical cyclone (TC) reforecasts from the Community Earth System Model version 2 (CAM6) subseasonal prediction system are examined in this study. Here, we evaluate the modeled TC climatology and the probabilistic forecast skill of basin-wide TC genesis at weekly temporal resolution. Prediction skill is calculated using the Brier skill score relative to a constant annual mean climatology and to a monthly varying seasonal climatology during TC season. The model captures the observed basin-wide climatological TC seasonality and spatial distributions at weeks 1–6, but TC genesis is largely underestimated from Week 2 onward. For some basins and lead times, the predicted TC genesis is primarily controlled by the number of TC “seeds” and the mean-state climate condition. The model has good prediction skill relative to the constant climatology across all the basins and lead times, but is only skillful in the eastern Pacific, North Indian Ocean, and Southern Hemisphere at Week 1 when compared to the seasonal climatology, indicating limited skill in predicting deviations from the seasonal cycle. We find strong modulations of the predicted TC genesis at up to 3 weeks of forecast lead time by the Madden-Julian Oscillation. The interannual variability of predicted TC genesis and accumulated cyclone energy are skillfully predicted in the North Atlantic and the Northwestern Pacific, with a strong modulation by the El Nino-Southern Oscillation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Can socio-economic indicators of vulnerability help predict spatial variations in the duration and severity of power outages due to tropical cyclones?

Abstract Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Metrics for evaluating tropical cyclones in climate data

This manuscript describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products to one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or regional domains. Simple to visualize ‘scorecards’ allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic TC frequency. Furthermore, upstream refinement over northern Africa offers little benefit in simulating storm occurrence but spatial genesis patterns are improved. Finally, we show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on version of the Morrison-Gettelman microphysics package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stronger Tropical Cyclone–Induced Ocean Cooling in Near-Coastal Regions Compared to the Open Ocean

Abstract Tropical cyclones (TC) often induce strong mixing in the upper ocean that generates a trail of cooler sea surface temperature (Twake) in their wakes. The Twake can affect TC intensity, so its prediction is important, especially in coastal regions where TCs can make landfall. Coastal Twakes are often more complex than those in the open ocean due to the influences of coastline geometry, highly variable water depth, continental runoff, and shelf processes. Using observational data since 2002, here we show a significantly stronger global mean Twake in coastal regions compared to offshore regions. Temperature stratification is the main driver of stronger coastal Twakes in the North Atlantic and east Pacific. In the northwest Pacific and north Indian Ocean, the differences between coastal and offshore Twakes are smaller due to compensation between TC forcings and ocean stratification. The north Indian Ocean is unique in the Northern Hemisphere because salinity stratification plays a major role on the spatial distribution of Twake. In the South Pacific Ocean, TC intensity and translation speed are crucial for explaining coastal–offshore Twake differences, while ocean stratification and mixed layer depth are more important for the coastal–offshore Twake differences in the south Indian Ocean. These findings suggest that coastal–offshore differences in ocean stratification need to be properly represented in models in order to capture changes in TC-induced ocean cooling as storms approach landfall. Significance Statement Landfalling tropical cyclones (TCs) often cause considerable damage in coastal regions with dense human populations. Understanding TC–ocean interaction and how it differs between coastal and offshore regions can help predict TC intensity prior to landfall. Sea surface cooling after TC passage is an important proxy for TC–ocean interaction. A global evaluation of coastal TC-induced cooling has not been conducted. Using data covering two decades, we show significantly stronger TC-induced surface cooling in coastal regions compared to offshore regions at the global scale and in all basins except the northwest Pacific and north Indian Ocean. The difference is driven mainly by upper-ocean conditions in the North Atlantic, east Pacific, and south Indian Ocean, and by TC characteristics in the South Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Extratropical Pathway for the Madden–Julian Oscillation’s Influence on North Atlantic Tropical Cyclones

Abstract This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Tropical cyclones (TCs) rank as the deadliest and most financially crippling natural disasters in the United States for the last half-century. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study, we have modeled the intensities of 620 historical TC events in the North Atlantic Basin using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep learning intensity model. By applying a thermodynamic warming signal extrapolated from Global Climate Models, we rerun historical events under eight different future climate scenarios, providing a spectrum of potential TC intensity outcomes. One of the future simulations indicates a staggering 43% increase in the number of major hurricanes, underscoring the critical impact of climate change on TC intensity. Additionally, an interactive dashboard has been created to enable users to explore individual storm simulations and understand the influence of future climate signals on environmental conditions of TC development and resulting TC intensities. This dataset and the user-friendly tool offer invaluable resources for systematic exploration of the discrete effects that changes in the air-sea thermodynamic state have on the intensities of TCs.

Climate Change↗
DOE OSTI2025

Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Tropical cyclones (TCs) have ranked as the deadliest and most financially crippling natural disasters in the United States. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study we apply a fixed-constraint storyline approach that holds storm tracks and initial conditions constant to probe future TC intensity in the North Atlantic Basin. First, we simulate 618 historical TC events using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep-learning intensity model. Next, we apply warming signals derived from eight CMIP6 climate scenarios and rerun each event to explore how intensities respond across scenarios. Finally, we develop an interactive dashboard that allows users to explore individual storm simulations and the scenario-modified environmental drivers. Together, this dataset and tool provide a clear, illustrative way to investigate how TC intensity responds to changes in air-sea state.

Climate Change↗