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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 OSTI2020

Use of Remote Sensing and In-Situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the ACME Model

The overachieving goal of the whole CMDV-MCS project is to improve understanding of warm season continental convection and to develop treatments of convection and microphysics capable of representing mesoscale convective systems (MCSs) features in large-scale models. Our tasks for this project contributing to the overachieving goal include: (1) Improve the ice nucleation formulation for MG2 and P3 cloud microphysics schemes; (2) Improve the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM; and (3) Test the performance of improved ice microphysics in E3SM with observation data. In this project, we have (1) Improved the ice nucleation parameterization for MG2 and P3 in E3SM by implementing two advanced empirical parameterizations with connection to aerosols. The two deterministic heterogeneous ice nucleation parameterizations (i.e., DeMott et al., 2015; Niemand et al., 2012) were merged with the MG2 and P3 cloud microphysics schemes in E3SM. Long-term simulations were conducted to examine the impacts of these new parameterizations on simulated cloud properties; (2) Improved the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM. We evaluated the double Gaussian PDF of vertical velocity simulated by the Cloud Layers Unified By Binormals (CLUBB) and the sub-column vertical velocity sampled from the Subgrid Importance Latin Hypercube Sampler (SILHS) in E3SM. We introduced the vertical velocity variance induced by topographic gravity waves for ice nucleation and droplet activation; and (3) Tested the performance of improved ice microphysics in E3SM with observation data. We tested the new treatments of ice nucleation in the single column model (SCM) mode for the stratiform mixed-phase clouds observed during 9-10 October 2004 in the DOE ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) and for the convective clouds observed on 20 May 2011 in the Midlatitude Continental Convective Clouds Experiment (MC3E). Modeled ice nucleating particles (INPs) concentrations were compared against observations collected around the globe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Recent advances in integrated hydrologic models: Integration of new domains

Over the past several decades, hydrologic models have advanced from independent models of the surface and subsurface to integrated models that can capture the terrestrial hydrologic cycle within one framework. In recent years, these coupled frameworks have seen the inclusion of biogeochemical processes, ecohydrology, sedimentation and erosion, cold region hydrology, anthropogenic activities, and atmospheric processes. This expansion is the result of increased computational, data, and modeling capabilities and capacities, as well as improved understanding of the processes that drive these integrated systems. Here, in this study, we review these recent advances to integrate new processes and systems into existing terrestrial hydrologic models and highlight the significant challenges and opportunities that remain. We identify that with so many models currently available and in development, selecting the most appropriate model is difficult, and we suggest a path for new or novice modelers to find the most appropriate code based on their needs. In addition, data required to parameterize and calibrate these models can often constrain their applicability and usefulness. However, advances in environmental sensors and measurement technology, in addition to data assimilation of non-traditional data (e.g. remote sensing, qualitative data) are providing new ways of addressing this issue. As we expand hydrologic models to integrate more processes and systems, our computational demands also increase. Recent and emerging advances in computational platforms, including cloud and quantum computing, in addition to the use of machine learning to capture some processes, will continue to support the use of increasingly larger and more complex, process-based models. Finally, we highlight that it is critical to develop state-of-the-science models that are accessible to all model users, not just those applied for research and development. We encourage continued development of diverse modeling platforms, considering the user needs, data availability, and computational resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of a Stochastic Mixing Scheme in the Deep Convective Gray Zone Using a Tropical Oceanic Deep Convection Case Study

Abstract A stochastic horizontal subgrid‐scale mixing scheme is evaluated in ensemble simulations of a tropical oceanic deep convection case using a horizontal grid spacing (Δ h ) of 3 km. The stochastic scheme, which perturbs the horizontal mixing coefficient according to a prescribed spatiotemporal autocorrelation scale, is found to generally increase mesoscale organization and convective intensity relative to a non‐stochastic control simulation. Perturbations applied at relatively short autocorrelation scales induce differences relative to the control that are more systematic than those from perturbations applied at relatively long scales that yield more variable outcomes. A simulation with mixing enhanced by a constant factor of 4 significantly increases mesoscale organization and convective intensity, while turning off horizontal subgrid‐scale mixing decreases both. Total rainfall is modulated by a combination of mesoscale organization, areal coverage of convection, and convective intensity. The stochastic simulations tend to behave more similarly to the constant enhanced mixing simulation owing to greater impacts from enhanced mixing as compared to reduced mixing. The impacts of stochastic mixing are robust, ascertained by comparing the stochastic mixing ensembles with a non‐stochastic mixing ensemble that has grid‐scale noise added to the initial thermodynamic field. Compared to radar observations and a higher resolution Δ h = 1 km simulation, stochastic mixing seemingly degrades the simulation performance. These results imply that stochastic mixing produces non‐negligible impacts on convective system properties and evolution but does not lead to an improved representation of convective cloud characteristics in the case studied here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. Here, to inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Aerosol-Ice Formation Closure Pilot Study (AEROICSTUDY) Field Campaign Report

The aerosol-ice formation closure pilot study (AEROICESTUDY) was a field campaign conducted at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility at the Southern Great Plains (SGP) observatory from October 7 to 28, 2019. The purpose of this campaign was to test a field observational approach for conducting an aerosol-ice formation closure study. In other words, the goal was to predict the number of ice nucleating particles (INPs) from the measurements of physical and chemical aerosol properties and compare those to measured INP number concentrations. This project was motivated by the fact that the aerosol community has widely conducted aerosol radiative closure, aerosol-cloud condensation nuclei (CCN), and CCN-droplet closure studies to test the physical models and parameterizations that cloud-resolving and climate models rely on to perform reliable simulations of the Earth system and energy budget. However, very few closure studies related to INPs have been conducted, and to our knowledge, none using robust, size-resolved, ambient aerosol composition measurements as model inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-Element Water Content System Instrument Handbook

The SEA Model WCM-2000 multi-element water content system was designed to provide aircraft and wind tunnel users with a single, rugged sensor to simultaneously measure liquid water content (LWC), total water content (TWC), and ice water content (IWC). The instrument consists of four heated stainless steel elements, each with a different shape or size. Each element is directly heated by low-voltage DC current flowing through the element. These elements are maintained at a constant temperature, typically 140°C, by a digital closed-loop control system. Three of the heated elements are exposed directly to the airstream and incoming cloud water. A fourth element, the reference/comp element, is arranged to be exposed to the airflow but not to incoming cloud water. This element is used to establish the “dry power term” that is subtracted from the other three elements’ power readings to establish the measured water content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Description of the Three-Dimensional Large-Scale Forcing Data from the 3D Constrained Variational Analysis (VARANAL3D)

This technical report introduces a Three-Dimensional Constrained Variational Analysis (3DCVA) (Tang and Zhang 2015) and its product of three-dimensional large-scale forcing data to drive single-column models (SCM), cloud-resolving models (CRM), and large-eddy simulation (LES) models, and to evaluate model results. The 3DCVA algorithm is an extension of the original 1D constrained variational analysis (1DCVA) (Zhang and Lin 1997, Zhang et al. 2001). The three-dimensional structure of the forcing data allows studies of spatial variation of the large-scale forcing fields and tests of physical parameterizations across scales. In the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility, the 3D forcing data are assigned the datastream name varanal3d. In this technical report, 3DCVA will be used to refer to the algorithm, while VARANAL3D will be used to refer to the data product.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dust Formation and Destruction in Core Collapse Supernovae Explosions [Dissertation]

Dust is a crucial component of the Interstellar Medium (ISM) and an important driver in many astrophysical processes. Dust formed from evolved stars increases the metallicity of the ISM, enriching it with heavier elements. Dust makes up the source material of collapsing clouds in the ISM leading to stellar and galactic formation. The stabilization of these systems are aided by dust through absorption and scattering of excess energy. As stellar systems evolve and undergo supernova and mass loss events, enriched dust is produced, further enhancing the metallicity of the ISM. However, despite the importance of dust, the processes that produce dust are not well understood. Core-Collapse Supernovae are one source of dust. Dying massive stars explode, sending their constituent material into the Interstellar Medium, which as the ejecta expands and cools, material condenses and nucleates forming dust grains. I investigate the formation, survivability, and properties of dust. In Chapter 2, dust grain nucleation in CCSNe is discussed. A revised formulation of Kinetic Nucleation Theory (KNT) is used to track the nucleation and growth of dust grains in the ejecta. The affects of the progenitor system on the amount and type of dust is explored, showing a strong dependence on explosion energy. I discuss the code and methods used to model dust nucleation and destruction in Chapter 3. The sputtering and erosion of dust grains in CCSNe shocks are explored in Chapter 4. The erosion and survival of this nucleated dust is modeled through thermal and non-thermal sputtering by the surrounding gas and shocks. The optical properties and scattering of light by surviving dust grains is modeled by Mie scattering in Chapter 5. A discussion of observations of dust in variable star systems and supernovae is outlined in Chapter 6.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Warm-phase microphysical evolution in large-eddy simulations of tropical cumulus congestus: evaluating drop size distribution evolution using polarimetry retrievals, in situ measurements, and a thermal-based framework

Owing to uncertainties in convective microphysics processes, improving parameterizations in Earth system models (ESMs) can benefit from observationally constrained cases suitable for scaling between cloud-resolving models and ESMs. We propose a benchmark large-eddy simulation (LES) cumulus congestus case study from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP 2 Ex) for evaluating and improving ESMs in single-column model (SCM) mode. We seek observational constraints using novel polarimetric retrievals and in situ cloud microphysics measurements. Simulations using bulk and bin microphysics initialized with observed aerosol profiles are compared to cloud-top retrievals of cloud droplet effective radius (R eff ), effective variance (ν eff ), and number concentration (N d ) from the airborne Research Scanning Polarimeter (RSP). Both schemes reproduce characteristics of cloud-top N d and R eff that increase and decrease with altitude, respectively. Cloud-top N d is low-biased relative to RSP retrievals in both schemes, potentially due to limitations in both simulations and retrieval assumptions. Cloud-top R eff is low-biased in the bulk scheme but reasonably reproduced by the bin scheme. Profiles of N d and R eff are sensitive to the collision–coalescence process and the vertical variation in aerosol size distribution. Comparison of simulated and in situ droplet size distributions (DSDs) shows that, to first order, integrated moments are always sensitive to sizes < ~ 30 µm and can also be sensitive to larger sizes if the DSDs are sufficiently broad, with implications for the assumed maximum observed size retrieved by the RSP. The bin scheme captures the observed extended tail of the DSD, while the bulk scheme is unable to due to parametric constraints. Differences in expected relationships between in situ measurements of cloud cores and cloud-top retrievals by RSP demonstrate difficulty in constraining well the case presented herein. Finally, a thermal-tracking framework demonstrates that the dilution of N d throughout a thermal's lifetime is heavily determined by collision–coalescence and the height-varying aerosol distribution and that, in the absence of these, the impact of entrainment on diluting N d is largely offset by secondary activation. Implications for evaluating warm-phase convective microphysics schemes in ESMs and translating results for use on global, space-based polarimetry platforms are discussed.

Stanford, McKenna Wallace [Columbia Univ., New Yor↗
DOE OSTI2024

The Challenges of Modeling Distributed Energy Resources (DERs) as Blackstart Resources and for Volt-VAR Optimality

Modeling, testing and instituting Distributed Energy Resources (DERs) as Blackstart Resoucrces presents several challenges due to the fundamental differences between traditional black start resources (e.g., large generators) versus DERs like solar PV systems with battery storage. This paper addresses some of these key challenges. These include intermittency of coordinating DERs to provide continuous power during a black start event, especially during extended periods of cloud cover or when battery energy storage is depleted. This paper additionally addresses the collapsing voltage and stability control challenges specific to maintaining bulk power system stability during black start synchronization These physical and engineering limitations require careful engineering design, modeling and engineering to ensure that DERs can support critical loads and substations during black start events. A variety of additional challenges also exist. Additionally, there are challenges associated with feeder location and low voltage secondary system impacts on DER functions and settings. We compare typical functions and settings for DERs for power factor control and correction. We also demonstrate how voltag control via Volta-VAR power factor correction can be done

Mukherjee, Srijib↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development of a Miniaturized Mobile Mapping System for In-Row, Under-Canopy Phenotyping

This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms of acquiring data that facilitate under-canopy plant trait extraction. The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products. The paper addresses three main aspects pertaining to the UGV development: (a) architecture of the UGV mobile mapping system (MMS), (b) quality assessment of acquired data in terms of georeferencing information as well as derived 3D point cloud, and (c) ability to derive phenotypic plant traits using data acquired by the UGV MMS. The experimental results from this study demonstrate the ability of the UGV MMS to acquire dense and accurate data over agricultural fields that would facilitate highly accurate plant phenotyping (better than above-canopy platforms such as unmanned aerial systems and high-clearance tractors). Plant centers and plant count with an accuracy in the 90% range have been achieved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Game-Theoretic Strategies for Cyber-Physical Infrastructures Under Component Disruptions

In this work, networked infrastructures of recursively defined systems composed of discrete cyber and physical components are considered. The components of basic systems at the finest levels can be disrupted by cyber or physical means, and can be reinforced to survive at certain costs. A problem of ensuring the infrastructure performance is formulated as a game between a provider and an attacker, who probabilistically choose components to reinforce and attack, respectively. The disruptions of this infrastructure are characterized using the aggregate failure correlation function that specifies the conditional failure probability of the infrastructure given the failure of an individual system at that level. The survival probabilities of basic systems satisfy simple product-form, first-order differential equations expressed in terms of the multiplier functions. The utility functions of the provider and attacker are composed of the reward and cost terms, both expressed in terms of the component reinforcement and attack probabilities. The Nash equilibrium of this game is characterized, along with the sensitivity functions of the survival probabilities of basic systems that highlight their dependence individually on the cost-benefit terms, the correlation functions, and the multiplier functions. These results are illustrated using simplified models of a distributed cloud servers infrastructure, a 5G data network infrastructure, a high performance computing federation, and a smart energy grid infrastructure.

42 ENGINEERING↗
DOE OSTI2020

Real-Time Implementation of Smart Wireless Charging of On-Demand Shuttle Service for Demand Charge Mitigation

This paper presents a smart charge management strategy for an on-demand electric shuttle operating at the National Renewable Energy Laboratory (NREL) campus and supported by an inductive charger at the vehicle's waiting spot. A new control algorithm has been proposed for mitigating the demand charges incurred from the wireless charger. It monitors the shuttle, wireless charger, renewable energy generation, and other loads and regulates charging behavior for demand charge mitigation. Within the control algorithm, an energy prediction is made to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller is designed and optimized using a Simulink model for the entire system. It is then implemented and tested in real time at the NREL campus using online cloud services. Two vehicle-use cases' charge-sustaining and charge-depletion operation' are tested under different campus power profiles and drive cycles to assess the controller's performance. In this work, the proposed controller showed a robust performance under different driving scenarios, with high correlation between simulation and experimental data. The results show that proper demand response can be achieved, with an average of 94% reduction of charging loads during peak demand events.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions

Aerosol effects on clouds and radiation are the dominant contribution to uncertainty in radiative forcing relative to the pre-industrial atmosphere. While previous studies have assessed the impact of parametric uncertainty on modeled forcing, structural errors from the numerical representation of particle distributions have not been well quantified. Here we present a framework for quantifying error in aerosol size distributions and cloud condensation nuclei activity, which we apply to the widely used 4-mode version of the Modal Aerosol Module (MAM4). Box model predictions from the MAM4 are evaluated against the Particle Monte Carlo Model for Simulating Aerosol Interactions and Chemistry (PartMC-MOSAIC), a benchmark model that tracks the evolution of individual particles. We show that size distributions simulated by MAM4 diverge from those simulated by PartMC-MOSAIC after only a few hours of aging by condensation and coagulation in polluted conditions, which leads to large errors in modeled cloud condensation nuclei concentrations. We find that differences between MAM4 and PartMC-MOSAIC are largest under polluted conditions, where the size distribution evolves rapidly though aging by condensation of semi-volatile substances and coagulation among particles. These findings suggest that structural error in modeled aerosol properties contributes to the large inter-model variability in aerosol radiative forcing.

Fierce, Laura M.↗
DOE OSTI2022

Cloud Type and Life Stage Dependency of Liquid–Ice Mass Partitioning in Mixed-Phase Clouds

This paper analyzes the temperature, cloud type, and life stage dependencies of phase partitioning in mixed-phase clouds spanning tropics, midlatitudes, and the Arctic, using data from ground-based remote sensing measurements in Alaska and aircraft measurements from three field campaigns. The results show: (1) The liquid fraction in Arctic stratiform clouds decreased from 1 to 0.6 between 0 °C and −30 °C and was lower in spring because of the higher dust occurrence in Barrow, Alaska; (2) In wintertime orographic clouds, the liquid fraction was greater than 0.8; (3) Phase partitioning in convective clouds varied significantly with life stages. In the developing stage, it decreased from 1 to 0.3 between −5 °C and −15 °C, indicating rapid ice generation, while at the mature and dissipating stages, the liquid fractions were lower; (4) The stratiform regions of mesoscale convective systems were dominated by ice, with liquid fractions lower than 0.2; and (5) The variability of phase partitioning varied for different cloud types. In stratiform clouds, liquid dominated at warm temperatures. As the temperature decreased, an ice-dominated region was more frequently observed, while the occurrence of the mixed-phase region remained low. For convective clouds, the variability of phase partitioning was controlled by continuous glaciation with decreasing temperature and life cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ship-based Observations and Climate Model Simulations of Cloud Phase over the Southern Ocean

The Southern Ocean (SO) clouds exert a significant influence on the Earth’s radiation budget. Here we analyzed ship-based remote sensing observations of SO clouds over a five-month long DOE ARM Measurements of Aerosols, Radiation and Clouds over the Southern Oceans (MARCUS) field campaign to better understand cloud phase variability. We developed a method to classify eight categories of hydrometeors (ice, liquid, mixed phase, rain, drizzle, snow, aerosols, and clear sky) based on measurements of lidar, radar and radiosondes. Cloud thermodynamic phases (liquid, ice and mixed phase) at coarser scales were further derived to compare with the DOE Energy Exascale Earth System Model version 1 (E3SMv1) simulation and the Earth Model Column Collaboratory (EMC 2 ) instrument simulator. For a scale-aware comparison with climate model simulations, we found that spatially averaging the raw remote sensing data (e.g., backscatter, reflectivity) results in increased cloud cover and cloud liquid, whereas, directly averaging cloud phase from higher to lower resolution maintains clear air regions and is thus recommended for in-cloud frequency comparisons. For cloud thermodynamic phases in stratiform clouds, the E3SM underestimates cloud ice and overestimates cloud liquid at temperatures between -40 and 0°C. When latitudes increase, both observations and simulations show a transition of dominant phase from liquid to ice for cloud tops as well as for the entire cloud columns, but the model underestimation of ice phase is more severe at higher latitudes. Such model bias is unlikely caused by spatial scale differences or lack of heterogeneity in cloud vertical structure in the simulation.

54 ENVIRONMENTAL SCIENCES↗