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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DOE STTR Phase I Final Report Report: Machine-learning Based Prediction of Thermal Limits for Conjugated Organic Materials

The SCANN-DT Phase I STTR project led by NLM Photonics and partnering with the National Renewable Energy Laboratory (NREL) sought to apply machine learning techniques based on graph neural networks (GNNs) towards the prediction of decomposition temperatures (Td) of organic semiconductors, based on prior work on bond dissociation energy (BDE) prediction as implemented in NREL’s ALFABET prediction tool. Using a curated set of experimental decomposition energies, the project examined GNN-based, classical quantitative structure-property relationship (QSPR) based on DFT calculations, and combinations of both methods to predict Td. While the best MAEs in Td achieved were near 40°C, below project targets, the project led to improvements in the ALFABET model, improvements in cloud-based implementations of NWChem software, and a substantial dataset of calculations on medium-sized conjugated organic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exploration of a Novel Technique for Waste Heat Recovery Through Molecular Dynamics: Influence of Wettability and Electric Field on Water and Water-Based Nanofluids

Most of the energy produced globally comes by way of a heat engine. The Carnot principle places a limit as to how thermodynamically efficient a heat engine can be. There is no heat engine that can be 100% thermodynamically efficient and as such a substantial proportion of all heat supplied to a heat engine is lost as waste heat. Waste heat therefore is a large energy source ready to be properly utilized. Herein, a novel approach for converting waste heat to electricity is discussed. It involves the use of the liquid to vapor phase change of a material dielectric (water) or electrolyte (nanofluid) in the embodiment of a capacitor for direct thermal to electrostatic energy conversion. While this method of waste heat recovery could potentially be added to the ever expanding portfolio of energy conversion techniques, a number of aspects must be addressed before it can be brought into practice. Water was seen as an ideal dielectric phase change material given its high relative permittivity ratio when in the liquid form as compared to its vapor form. However, given its short voltage holdoff time the phase change of water would need to occur rapidly. This brings up concerns of explosive boiling. Herein, molecular dynamics analysis into the explosive boiling behavior of thin water films gave more insight into how the interaction between the surface and liquid affected explosive boiling onset time. A Lennard-Jones potential with one interaction site and a Morse potential with three interaction sites between water and solid substrate were used. It was found generally that a stronger interaction between water film and substrate led to faster explosive boiling onset times but an increase in the number of interaction sites delayed explosive boiling, even at the same wettability (contact angle). Understanding changes in the density and enthalpy of vaporization of a liquid dielectric such as water in the presence of an electric field is of importance due to the electrostatic nature of the waste heat conversion method under consideration. Specifically, if both density and enthalpy of vaporization are increased, the thermodynamic efficiency of the waste heat conversion method under consideration is decreased. Electric field effects are explored herein via molecular dynamics using two water models, the TIP4P-Ew and SWM4-NDP. The SWM4-NDP model is polarizable while the TIP4P-Ew model is not, which allows for a determination of the importance of model polarizability (i.e. variation in water model dipole moment) on these two properties of water when subjected to an electric field. Herein it was found that both water models respond similarly in terms of density and vaporization enthalpy variance upon the introduction of an electric field. Comparison was also made to the pressure induced by the electric field (electrostriction pressure) by way of a density comparison and it was found that the predicted electrostriction pressure overestimates the pressure experienced by water. Water by itself has a high enthalpy of vaporization, which limits the efficiency of the newly proposed conversion method. Research both experimental and through simulation has shown that the vaporization enthalpy of nanofluids can be engineered via nanoparticle size and material selection. An avenue less explored is manipulating the enthalpy of vaporization by altering the interaction strength between the nanoparticles and the base fluid. In practice this could be achieved through the addition of coatings to the nanoparticles to alter their wettability to the base fluid. This was explored by using a Lennard-Jones potential and Morse potential to model the interaction between base fluid (water) and the nanoparticle. For nanoparticles 2nm in diameter and at weight percentages up to 6%, the change in vaporization enthalpy due to alterations of the interaction strength between nanoparticle and base fluid was not significant (less than a 1% difference) when compared to the effect of altering the weight percentage of nanoparticles in the nanofluid or introducing an electric field. However, the effect of wettability may still become important at other nanoparticle concentrations and sizes. In all, the studies presented here further the understanding of phase change and thermodynamic properties of water and water based nanofluids under an electrostatic field which will help inform the development of a novel approach to waste heat conversion. The reduction of waste heat will improve energy sustainability outlooks.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

First Principles Modeling of Cluster-Based Solid Electrolytes (Final Technical Report)

Given the trend of global warming and the urgent need to transition from fossil fuels to green energy, lithium-ion batteries continue to be an integral part of our lives. Design, development, and understanding of novel solid-state electrolyte materials play the key role for achieving next-generation all-solid-state batteries with high energy and great safety. The current modeling schemes to develop advanced solid electrolytes are focusing on materials in which the building blocks are individual atoms. Our theoretical approach is a paradigm shift in solid-state electrolyte design. Instead of atoms, we focus on clusters as the building blocks and model these solid electrolytes and their interfaces with electrodes, especially Li-metal anode, for their successful implementation in solid-state batteries. The advantage of using the cluster ions to replace elemental ions is that the size, composition, and shape of the former can be tailored to achieve higher ionic conductivity at room temperature, electrochemical stability, and charge transfer across solid-solid interfaces than conventional materials. Specifically, the project includes: (1) Developing cluster-based solid electrolytes, where the halogen components are replaced by cluster ions that mimic the chemistry of halogens but are characterized by additional degrees of freedom, including the size, shape, composition, and motional dynamics under excitation. (2) Providing a fundamental understanding of the ion conduction mechanism in the developed cluster-based solid electrolytes; (3) Modeling the interfacial properties (i.e., structural, chemical, and transport properties) between the cluster-based solid electrolytes and electrodes at the atomic level. For the cluster-based solid electrolytes incompatible with the Li-metal anode or cathode materials, potential candidates for interfacial coatings are identified and studied. (4) Providing a theoretical framework towards optimizing critical parameters of the solid-state electrolytes that guides experimentalists to attain desired cathode-electrode interface for cluster-based solid-state electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2023

The use of price-based demand response as a resource in electricity system planning

Utilities have conducted IRP for decades to consider both supply- and demand-side resources to meet bulk power system needs. More recently, an increasing number of states are requiring regulated utilities to file plans that identify distribution system needs, including DERs that can avoid or defer certain types of traditional utility investments cost-effectively. Price-based demand response (DR) is an underutilized resource that could substantially contribute to load flexibility. We find that evaluation of price-based DR as a solution to meet identified electricity system needs is uncommon at both planning processes. Where price-based DR is considered as a solution, methodologies are often deficient in the characterization and treatment of this resource. Based on our analysis of utility filings and state requirements, this brief provides recommendations for improving consideration of price-based DR in the context of long-term planning for bulk power and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An Inverse Heat Conduction Algorithm Used to Calculate the Temperatures on the Inner and Outer Cylindrical Surfaces of an HMX-based PBX Explosive Annulus

In this work, a new Inverse Heat Conduction (IHC) algorithm is applied to estimate the surface temperatures at twelve locations on the inner and outer cylindrical boundaries of an HMX-based Plastic Bonded Explosive (PBX) annulus. This IHC algorithm was developed in references using a set of Direct Heat Conduction (DHC) solutions and a temperature correction method. The DHC solutions were calculated using a Galerkin based finite element (FE) method. This HMX based PBX annulus was used in the Large Scale Annular Cookoff (LSAC) experiment, Shot 5. The reason Shot 5 was chosen as a prototype mathematical model for this study is that the temperature was measured at eighteen locations in the midplane of the HMX-based PBX annulus. In addition, this annulus underwent an experimental thermal ignition and a deflagration that caused a thermal explosion and the disassembly of the experiment. The objective of this study is to describe how the application of the temperature correction algorithm produced the convergence of the DHC solutions to the measured temperatures at twelve internal locations in the midplane of the HMX-based PBX annulus.

36 MATERIALS SCIENCE↗
DOE OSTI2024

First Phase Consensus Roadmap for Development of Condition-Based Cable Reliability Assurance

The objective of this work was to develop a first phase consensus roadmap for condition-based qualification (CBQ) of electrical cables. With CBQ, qualification of Class 1E electrical cables moves from a time-based approach to a condition-based approach, which is anticipated to be safer in terms of reliability and conservatism, and more cost effective in the long run. However, due to barriers, the CBQ approach has not yet been adopted by U.S. nuclear power plants (NPPs). Based upon a review of current work evaluating CBQ, the limitation of available condition monitoring technology seems to be the largest barrier. The importance of condition monitoring, or more specifically selecting appropriate condition indicators, during CBQ cannot be understated. However, selecting appropriate condition indicators is challenged by techniques that are destructive and only evaluate cable degradation locally. Further, arguably, no one identified condition indicator fully establishes cable condition. Thus, additional work is necessary to evaluate potential condition indicators towards CBQ. In addition to the requirements of IEC/IEEE Std. 60780-323, ideal condition indicators should include a) both destructive and non-destructive approaches, b) both local and global measurements, c) real-time (i.e., online) monitoring that trends with degradation, d) enable correlation with qualified levels of degradation, and e) be established within a repository of condition indicators with applicable materials and/or components and their acceptance criteria. Additional work is needed in development of technology and methodology prior to adoption of CBQ, especially for extending qualified life of installed components. Education and early experience by the industry and regulators will be required for this change in approach as an alternative to re-analysis. A series of workshops that bring together stakeholders to identify and address gaps will be needed. The longstanding cooperative working group of cable researchers from the U.S. Department of Energy, the Electric Power Research Institute, and the Nuclear Regulatory Commission forms a valuable starting point for development of a consensus roadmap to condition-based qualification approach as a viable options for qualification of cable systems in U.S. light water reactors.

42 ENGINEERING↗
DOE OSTI2026

A Vertical GaN-Based Neutral-Pointless Three-Level Inverter for High-Performance Automotive Traction Applications

Silicon Carbide (SiC) MOSFETs have emerged as a dominant solution for electric vehicle traction inverters because of their superior power density and efficiency compared to Silicon (Si) counterparts. However, SiC devices possess inherent switching speed limitations that constrain the maximum switching frequency and hinder further power density and efficiency improvements. To address these limitations, this paper proposes a vertical Gallium Nitride (vGaN) based X-type Neutral-Pointless (NPL.X) three-level (3L) inverter for an 800V, 200 kW traction system. Integrating 700V vGaN technology within the NPL.X 3L topology enables improved drive efficiency and higher switching frequencies, resulting in enhanced power density and output power quality. To evaluate the performance benefits, this work conducts a comprehensive loss characterization of the vGaN￾based NPL.X topology, providing a direct comparison with a SiC￾based two level (2L) inverter. The results show that the proposed vGaN-based inverter achieves superior efficiency, particularly within the low-torque regions that dominate standard automotive drive cycles. The proposed vGaN-based inverter represents a critical advancement toward high-power-density and high efficiency traction drives

Halawa, Ali [Purdue Univ., West Lafayette, IN (Uni↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Machine Learning-Based Regression Models for Ironmaking Blast Furnace Automation

Computational fluid dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace system. The problem with the CFD-based simulation approach is that it tends to be very slow for generating data. The CFD-only approach may not be fast enough for use in real-time decisionmaking. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared with tree-boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R 2 metrics are, in general, very high. The results appear promising and may help to improve the efficiency of operator and process engineer decisionmaking when running a blast furnace.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error εH arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Mathematics↗
DOE OSTI2023

Electromagnetic Radiation Effects on MgO-Based Magnetic Tunnel Junctions: A Review

Magnetic tunnel junctions (MTJs) have been widely utilized in sensitive sensors, magnetic memory, and logic gates due to their tunneling magnetoresistance. Moreover, these MTJ devices have promising potential for renewable energy generation and storage. Compared with Si-based devices, MTJs are more tolerant to electromagnetic radiation. In this review, we summarize the functionalities of MgO-based MTJ devices under different electromagnetic irradiation environments, with a focus on gamma-ray radiation. We explore the effects of these radiation exposures on the MgO tunnel barriers, magnetic layers, and interfaces to understand the origin of their tolerance. This review enhances our knowledge of the radiation tolerance of MgO-based MTJs, improves the design of these MgO-based MTJ devices with better tolerances, and provides information to minimize the risks of irradiation under various irradiation environments. This review starts with an introduction to MTJs and irradiation backgrounds, followed by the fundamental properties of MTJ materials, such as the MgO barrier and magnetic layers. Then, we review and discuss the MTJ materials and devices’ radiation tolerances under different irradiation environments, including high-energy cosmic radiation, gamma-ray radiation, and lower-energy electromagnetic radiation (X-ray, UV–vis, infrared, microwave, and radiofrequency electromagnetic radiation). In conclusion, we summarize the radiation effects based on the published literature, which might benefit material design and protection.

42 ENGINEERING↗
DOE OSTI2025

Bio-Based Polyurethane Materials: Technical, Environmental, and Economic Insights

Polyurethane (PU) is widely used due to its attractive properties, but the shift to a low-carbon economy necessitates alternative, renewable feedstocks for its production. This review examines the synthesis, properties, and sustainability of bio-based PU materials, focusing on renewable resources such as lignin, vegetable oils, and polysaccharides. It discusses recent advances in bio-based polyols, their incorporation into PU formulations, and the use of bio-fillers like chitin and nanocellulose to improve mechanical, thermal, and biocompatibility properties. Despite promising material performance, challenges related to large-scale production, economic feasibility, and recycling technologies are highlighted. The paper also reviews life cycle assessment (LCA) studies, revealing the complex and context-dependent environmental benefits of bio-based PU materials. These studies indicate that while bio-based PU materials generally reduce greenhouse gas emissions and non-renewable energy use, their environmental performance varies depending on feedstock and formulation. The paper identifies key areas for future research, including improving biorefinery processes, optimizing crosslinker performance, and advancing recycling methods to unlock the full environmental and economic potential of bio-based PU in commercial applications.

Jayalath, Piumi↗
DOE OSTI2026

Comparison of CNN-Based Image Classification Approaches for Implementation of Low-Cost Multispectral Arcing Detection

Camera-based sensing has benefited in recent years from developments in machine learning data processing methods, as well as improved data collection options such as Unmanned Aerial Vehicles (UAV) mounted sensors. However, cost considerations, both for the initial purchase of sensors as well as updates, maintenance, or potential replacement if damaged, can limit adoption of more expensive sensing options for some applications. To evaluate more affordable options with less expensive, more available, and more easily replaceable hardware, we examine the use of machine learning-based image classification with custom datasets, utilizing deep learning based-image classification and the use of ensemble models for sensor fusion. Utilizing the same models for each camera to reduce technical overhead, we showed that for a very representative training dataset, camera-based detection can be successful for detection of electrical arcing. We also use multiple validation datasets, based on conditions expected to be of varying difficulty, to evaluate custom data. These results show that ensemble models of different data sources can mitigate risks from gaps in training data, though the system will be less redundant for those cases unless other precautions are taken. We found that with good quality custom datasets, data fusion models can be utilized without specialization in design to the specific cameras utilized, allowing for less specialized, more accessible equipment to be utilized as multispectral camera components. This approach can provide an alternative to expensive sensing equipment for applications in which lower-cost or more easily replaceable sensing equipment is desirable.

convolutional neural networks↗
DOE OSTI2023

Stellar-reddening-based Extinction Maps for Cosmological Applications

Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe. Errors in extinction maps can propagate systematic biases into samples of dereddened extragalactic objects and into cosmological measurements such as correlation functions between foreground lenses and background objects and the primordial non-Gaussianity parameter f NL . Emission-based maps are contaminated by the cosmic infrared background, while maps inferred from stellar reddenings suffer from imperfect removal of quasars and galaxies from stellar catalogs. Thus, stellar-reddening-based maps using catalogs without extragalactic objects offer a promising path to making dust maps with minimal correlations with large-scale structure. We present two high-latitude integrated extinction maps based on stellar reddenings, with a point-spread functions of FWHMs 6'. 1 and 15'. We employ a strict selection of catalog objects to filter out galaxies and quasars and measure the spatial correlation of our extinction maps with extragalactic structure. Our galactic extinction maps have reduced spatial correlation with large-scale structure relative to most existing stellar-reddening-based and emission-based extinction maps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Coordination Chemistry in the Structural and Functional Exploration of Actinide-Based Metal-Organic Frameworks

The coordination chemistry between inorganic and organic species can be optimally exemplified by metal–organic frameworks (MOFs), whose structures and functionalities can be rationally designed from these highly tunable building blocks. The high porosity, stability, and versatile functionalities of MOFs have attracted wide-spread attention from energy-related research and pollution remediation to biomedical applications. A unique and underexplored subset of these materials are MOFs based on actinide nodes; these MOFs have distinguished themselves as a unique platform for investigating the versatile oxidation states, reactivity, and coordination chemistry of actinides. Herein, we will focus on the rational design and synthesis of actinide-based MOFs under the general guidelines of coordination chemistry for their structural and functional explorations. The dimensionality, topology, and structures of actinide-based MOFs can be controlled by selecting pre-designed building blocks of actinide-based nodes and organic linkers with certain desired coordination geometries and functionalities. Furthermore, these unique actinide-based MOFs have shown promise for applications in nuclear waste mitigation, pollution control, and catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Assessing Resilience in Lane Detection Methods: Infrastructure-Based Sensors and Traditional Approaches for Autonomous Vehicles

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions. We address this need by evaluating the influence of hazards on the resilience of three different lane detection methods in simulation: (1) traditional camera detection using a U-Net algorithm, (2) radar detections using infrastructure-based radar retro-reflectors (RRs), and (3) direct communication of lane line information using chip-enabled raised pavement markers (CERPMs). The performance of each of these methods is assessed using resilience engineering metrics by simulating the individual methods for each sensor technology’s response to related hazards in the CARLA simulator. Using simulation techniques to replicate these methods and hazards acquires extensive datasets without lengthy time investments. Specifically, the resilience triangle was used to quantitatively measure the resilience of the lane detection system to obtain unique insights into each of the three lane detection methods; notably the infrastructure-based CERPMs and RRs had high resistance to hazards and were not as easily affected as the vision-based U-Net. However, while U-Net was able to recover the fastest from the disruption as compared to the other two methods, it also had the most performance loss. Overall, this study demonstrates that while infrastructure-based lane keeping technologies are still in early development, they have great potential as alternatives to traditional ones.

Patil, Pritesh↗
DOE OSTI2023

Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements

Abstract. Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions.

54 ENVIRONMENTAL SCIENCES↗