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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗
DOE OSTI2022

Health Assessment and Performance Monitoring of Large Machine Diagnostics

Presentation to be given at the CCS-6 Statistical Seminar Series. Regularly maintained and operated diagnostic machines are a backbone of data collection and are a vital component of the Stockpile Stewardship Program’s efforts to better understand nuclear physics. The Cygnus X-ray machine, stationed at the Nevada National Security Site’s U1a underground facility, is one such diagnostic that provides a radiographic capability for the subcritical experiment program. Component failures within Cygnus can result in catastrophic downtime for the diagnostic, affecting performance, schedules, and cost. However, over the years various measurements have been collected on the two Cygnus axes, including voltage and current measurements at different locations, which we believe have predictive power to indicate machine health. We will share preliminary insight into this data and the machine learning approaches we are taking to assess Cygnus’ health, observe declining performance, and predict failures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect

High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep learning, big data and planet-scale simulations. However, the lack of deep understanding on how modern GPUs can be connected and the real impact of state-of-the-art interconnect technology on multi-GPU application performance become a hurdle. In this paper, we fill the gap by conducting a thorough evaluation on five latest types of modern GPU interconnects: PCIe, NVLink-V1, NVLink-V2, NVLink-SLI and NVSwitch, from six high-end servers and HPC platforms: NVIDIA P100-DGX-1, V100-DGX-1, DGX-2, OLCF’s SummitDev and Summit supercomputers, as well as an SLI-linked system with two NVIDIA Turing RTX-2080 GPUs. Based on the empirical evaluation, we have observed four new types of GPU communication network NUMA effects: three are triggered by NVLink’s topology, connectivity and routing, while one is caused by PCIe chipset design issue. These observations indicate that, for a multi-GPU application running in a multi-GPU node, choosing the right GPU combination can impose considerable impact on GPU communication efficiency, as well as an application’s overall performance. Our evaluation can be leveraged in building practical multi-GPU performance models, which are vital for GPU task allocation, scheduling and migration in a shared environment (e.g., AI cloud and HPC centers), as well as communication-oriented performance tuning.

Performance Evaluation, GPU, Interconnect, NUMA, P↗
DOE OSTI2021

A generalized class of strongly stable and dimension-free T-RPMD integrators

Here, recent work shows that strong stability and dimensionality freedom are essential for robust numerical integration of thermostatted ringpolymer molecular dynamics (T-RPMD) and path-integral molecular dynamics, without which standard integrators exhibit non-ergodicity and other pathologies [R. Korol et al., J. Chem. Phys. 151, 124103 (2019) and R. Korol et al., J. Chem. Phys. 152, 104102 (2020)]. In particular, the BCOCB scheme, obtained via Cayley modification of the standard BAOAB scheme, features a simple reparametrization of the free ring-polymer sub-step that confers strong stability and dimensionality freedom and has been shown to yield excellent numerical accuracy in condensed-phase systems with large time steps. Here, we introduce a broader class of T-RPMD numerical integrators that exhibit strong stability and dimensionality freedom, irrespective of the Ornstein–Uhlenbeck friction schedule. In addition to considering equilibrium accuracy and time step stability as in previous work, we evaluate the integrators on the basis of their rates of convergence to equilibrium and their efficiency at evaluating equilibrium expectation values. Within the generalized class, we find BCOCB to be superior with respect to accuracy and efficiency for various configuration-dependent observables, although other integrators within the generalized class perform better for velocity-dependent quantities. Extensive numerical evidence indicates that the stated performance guarantees hold for the strongly anharmonic case of liquid water. Both analytical and numerical results indicate that BCOCB excels over other known integrators in terms of accuracy, efficiency, and stability with respect to time step for practical applications.

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

Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Profiles of upcoming HPC Applications and their Impact on Reservation Strategies

With the expected convergence between HPC, BigData and AI, new applications with different profiles are coming to HPC infrastructures. Here, we aim at better understanding the features and needs of these applications in order to be able to run them efficiently on HPC platforms. The approach followed is bottom-up: we study thoroughly an emerging application from the neuroscience community (SLANT) to understand its behavior. Based on these observations, we derive a generic, yet simple, application model (namely, a linear sequence of stochastic jobs). We expect this model to be representative for a large set of upcoming applications that require the computational power of HPC clusters without fitting the typical behavior of large-scale traditional applications. In a second step, we show how one can manipulate this generic model in a scheduling framework. Specifically we consider the problem of making reservations (both time and memory) for an execution on an HPC platform. We derive solutions using the model of the first step of this work. We experimentally show the robustness of the model, even with very few data or with another application, to generate the model, and provide performance gains with regards to standard and more recent approaches used in the neuroscience community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Topology, criticality, and dynamically generated qubits in a stochastic measurement-only Kitaev model

Here we consider a paradigmatic solvable model of topological order in two dimensions, Kitaev's honeycomb Hamiltonian, and turn it into a measurement-only dynamics consisting of stochastic measurements of two-qubit bond operators. We find an entanglement phase diagram that resembles that of the Hamiltonian problem in some ways, while being qualitatively different in others. When one type of bond is dominantly measured, we find area-law entangled phases that protect two topological qubits (on a torus) for a time exponential in system size. This generalizes the recently proposed idea of Floquet codes, where logical qubits are dynamically generated by a time-periodic measurement schedule, to a stochastic setting. When all types of bonds are measured with comparable frequency, we find a critical phase with a logarithmic violation of the area law, which sharply distinguishes it from its Hamiltonian counterpart. The critical phase has the same set of topological qubits, as diagnosed by the tripartite mutual information, but protects them only for a time polynomial in system size. Furthermore, we observe an unusual behavior for the dynamical purification of mixed states, characterized at late times by the dynamical exponent z = $\frac{1}{2}$ a superballistic dynamics made possible by measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

MARVEL Technical Overview: Breakdown of cost and scope growth through 90% Final Design

This report presents a breakdown of cost and scope growth for the Microreactor Applications Research Validation and Evaluation (MARVEL) project through the design phase, and includes observations, lessons learned, and the results from an independent project assessment. It presents the status and history of the project. Technology maturity is considered in high-level, qualitative comparison to other microreactor design efforts. Its purpose is to record MARVEL’s evolution and lessons learned from the planning and design phases through completion of 90% final design. Analyses included a detailed review of project cost, schedule, and periodic project reports and management documents. The Primary Coolant Apparatus Test (PCAT) is specifically highlighted. It was concluded that MARVEL would have benefited from more extensive planning early in the project to better define cost and schedule to provide more certainty in the total project cost and delivery date. Modest cost and schedule improvements may have been possible, but compared qualitatively, MARVEL’s total cost and schedule performance are consistent with that of other efforts currently underway. Better planning would have provided more certainty, improved risk mitigations, and potentially eliminated delays due to funding shortfalls. A recommended path forward is presented that addresses recommendations in the independent project assessment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Investigation into Scalable and Detection-Enhanced Satellite Conjunction Assessment

Imaging opportunities (viewable conjunctions) of Resident Space Objects (RSOs) by satellites are not continuously discovered. We propose to continuously produce and report viewable conjunctions among objects in orbit. Viewable conjunctions are events in space and time when a satellite may favorably view a Resident Space Object (RSO). Favorability is defined by a set of constraints, e.g., solar illumination, distance between observer and target, orbital location for viewable event. Computing viewable conjunctions requires calculation of orbital propagation while considering constraints based on the state vectors of position, velocity, with covariance for both satellite and RSO. We propose two parallel lanes of effort: acceleration and research. The objective of acceleration is to avoid missed opportunities and reduce latency for satellite maneuver requests through continuous prediction and reporting of viewable conjunctions. The effort will begin by deploying currently available software on dedicated systems and continue with optimizing the code for high performance computing hardware. The research lane aims to expand RSO inspection and modeling capabilities. Among our current research ideas are spectral characterization of RSO materials and planning multiple observations to recover RSO 3D form. Computing resources at Oak Ridge National Laboratory (ORNL) are available for the acceleration work. Laika, Maxar conjunction prediction dashboard software, and Bluesim, Maxar orbital propagation software, are expected to be the first software in the acceleration lane. Laike and Bluesim are to be provided by the sponsor, and output will be made accessible through its dashboard. Deliverables will follow a gated schedule to the sponsor. ORNL will provide progressively more robust viewable conjunction assessments from both modelled and actual ephemerides.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Radiological Recovery Logistics Tool - 20161

Argonne is building and testing a tool, the Radiological Recovery Logistics Tool (RRLT), that can be used during the response and recovery from a radiological or nuclear incident to effectively allocate appropriate commercial and public works equipment to mitigate, remove, and contain radiological contamination. The requirements for this tool - as well as development of the resulting software - is overseen by a steering committee of stakeholders from DHS's National Urban Security Technology Laboratory (NUSTL), the Federal Emergency Management Agency (FEMA), and the Environmental Protection Agency (EPA). One essential requirement is for RRLT to support the efficient and appropriate allocation of resources for a radiological response. Subsequent discussions between ANL and stakeholders have solidified the nature of this support to include identification of the types of resources to be allocated. The study reported in this paper has both factored fundamental concepts and connections out of this identification process and created a Knowledge Base detailing support goals, response scenarios, and efficacy information on dozens of equipment types. In short, RRLT will dynamically apply these findings to situational conditions surrounding contamination incidents. RRLT's Domain, the model of elements, ideas and relationships with which the tool will work, draws concepts from technical reports and stakeholder vocabularies to connect response goals and scenarios to types of equipment that offer utility towards those goals in those scenarios. RRLT's Knowledge Base will contain details on dozens of equipment types and facilitate the operator's discovery and consumption of these details most pertinent to a dynamically selected subset of goals. The core of its Domain Model is based on a report authored by this team. This report [1] contains a comprehensive list of proposed equipment to accomplish various missions or scenarios that might arise after a large-scale radiological contamination incident in an urban environment or critical infrastructure. The report divides potential response and recovery efforts into five support goals: Survey and monitoring of the contaminated area; Mitigation of received dose to first responders: Decontamination (gross and final) of buildings, vehicles, roadways, parks, and other surfaces: Waste management of solid waste generated during recovery operations: and Containment of wastewater and other waste generated during the response and recovery phases. RRLT's development is driven by use cases. A use case is an intention with which a user approaches the software. Use cases are grouped into delivery increments to schedule development, testing, and presentation to stakeholders. This model partitions the system into seven increments: User Arrival and Authentication, Search and Navigation, Equipment Recommendation, Plan Management, Content Management, and Expanded Access. Once a user 15 authenticated, RRLT will present the user with a dashboard that allows them to explore or search RRLT's content. The dashboard will also include a 'Plan' panel for collecting decisions and relevant observations about an incident at hand to facilitate development of an equipment list. RRLT will offer three general modes of access to items in the knowledge base: - Keyword search for direct discovery of items, - Navigation along predetermined paths from recovery goal towards equipment types, and - Interactive guidance towards equipment types by an autonomous software agent: the Equipment Recommendation Wizard. This presentation will detail progress in the development of the RRLT and also discuss opportunities for those interested in providing feedback on its content and functionality. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Co-Simulation Model for Optimal Wind-Hydro Coordination Using Wind Farm Control Dynamics

The growing share of Variable Renewable Energy sources (VRES) in power systems presents challenges for regula- tors, grid operators and energy producers. The VRES’ operation has limited flexibility in their operations, as they are highly dependent on ambient environments. To address these challenges, decision-makers must consider multiple objectives, among these are revenue, power system services and mechanical load on wind turbines. Coordinated operation of power plants and different wind farm control strategies are examples of measures that can benefit these objectives. This study proposes a Multi-Objective Linear Programming (MOLP) model to simulate the optimum operation of wind and hydropower plants that share limited transmission capacity. Further, wind farm control dynamics are included to obtain realistic output power and accumulated damage. From this, a case study based on a relevant location in Norway is presented to analyze the improved effect of wind- hydro coordination and wind farm control in achieving the objectives of accumulated wind turbine damage and total revenue of the hybrid power system. In addition, the study considers the potential advantages of adding a variable-speed pump to the hydropower plant. The results demonstrate that by considering multiple objectives in the optimization, one may achieve better overall performance of the objectives. By utilizing the flexibility of hydro storage, the decision maker may adjust to obtain the most desired outcome. Moreover, the added flexibility of utilizing a pump for hydro storage shows great improvements for the combined revenue of the power plants and reduced curtailment of the wind farm. However, less impact is observed from using a variable speed pump compared to a fixed speed pump.

13 HYDRO ENERGY↗
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Performance analysis of integrated Nuclear-Solar Energy system sharing same molten salt thermal energy storage

Advanced nuclear reactors may be deployed with integrated thermal energy storage to improve flexibility and maximize revenue. This presents opportunities for thermal integration with concentrating solar power (CSP) to generate component synergies and/or improve performance. Here in this study, a computational model is developed for an integrated nuclear and CSP system that both share the same molten salt thermal energy storage (TES). Optimized dispatch schedules are developed subject to various market conditions, and the ratio of the nuclear to CSP thermal output is also varied. Performance of the combined system is compared to separate nuclear and solar plants, to determine if there is an overall benefit that can be derived from sharing the TES. Given sufficient volatility in electricity prices (e.g., under CAISO market conditions), synergies of up to 8% in net revenue are observed as a result of sharing the same TES, primarily due to improved revenue from enabling operation of the turbine closer to its design point, as well as being able to better take advantage of higher electricity prices. The synergy benefits are largest when the nuclear and solar plants have similar thermal output. However, when prices are less volatile the opposite behavior can be observed and it can be preferable to operate the nuclear plant as a baseload generator.

14 SOLAR ENERGY↗
DOE OSTI2023

Driving Next-Generation Workflows from the Data Plane

We observe the emergence of a new generation of scientific workflows that process data produced at a sustained rate by scientific instruments and large scale numerical simulations. This data is consumed by multiple analysis, visualization, or Machine Learning components not only to enable inference and justify the scientific program, but also to monitor and steer the evolution of these experiments. In such workflows, moving intermediate data efficiently is key to performance, more than efficiently scheduling computational tasks. However, most traditional workflow management systems focus on optimizing task scheduling and then deal with data management, assuming a “move little, compute for long” model, which makes them unfit to the efficient management of this new generation of workflows. Therefore, we advocate for a new way to manage scientific workflows. We propose to consider an efficiently and independently managed data plane that can store and stream data. Workflows compute components, in the application plane can then interact with the data plane, abstracted from complexities of data management. Then, the role of a workflow management system would become that of a control plane that allows users to connect services together to execute the workflow and manages connections between the application and data planes. In this position paper, we characterize several next-generation workflow motifs and describe how their interaction with the data plane is a challenge to traditional workflow management systems. Then, we express a set of requirements that a workflow management system should meet to efficiently manage next-generation workflows at different scales. Based on these requirements, we expose our vision of driving next-generation workflows from the data plane and list remaining open challenges.

Suter, Fred↗
DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock". Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗
DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach: Preprint

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock''. Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗
DOE OSTI2024

Design and Characterization of the Engineering Model of the Spectrometer Onboard LuSEE‐Night

Abstract The Lunar Surface Electromagnetics Explorer—Night, LuSEE‐Night, is a low‐frequency radio astronomy experiment that will explore the cosmic Dark Ages signal on the radio‐quiet farside of the Moon. The LuSEE‐Night carries a radio frequency spectrometer consisting of a set of antennas, analog and digital processing electronics, and will be launched by NASA's Commercial Lunar Payload Services in 2025. The spectrometer is designed to observe the spectrum of the radio sky in the 0.5−50 MHz band. The engineering model (EM) of the four‐channel spectrometer has been developed. The EM has been characterized for linearity, gain, noise, and their temperature dependence, confirming that the EM meets all the requirements for LuSEE‐Night. Three mitigation techniques have been implemented and verified to suppress self‐induced electromagnetic interference. The flight model of the spectrometer is currently being developed and is scheduled to be shipped to the integration site in early 2024.

Tamura, Emi↗
DOE OSTI2022

The Early Career Research Program Annual Progress Report: Using ARM Data and Multiscale Models to Advance the Understanding of Liquid-Phase Cloud Response to Aerosol Perturbation over Ocean and Land

This report is for the first fiscal year of this ECRP project. This ECRP project aims to study the response of liquid-phase clouds to aerosol perturbation and reduce the related uncertainty in the DOE Earth system model. During this fiscal year, this project mainly made progress on the tasks about observational analyses for Objective 1; and successfully set up two Large-Eddy Simulation (LES) models for Objective 2. A postdoctoral researcher and a graduate student summer intern are on board and fully dedicated to this project. In addition to internal collaboration within LLNL, this project has routinely maintained external collaboration with scientists from universities, NASA Langley Research Center, and other DOE national laboratories. For the next reporting period, we plan to complete the tasks scheduled for the second year. Furthermore, we will promote the findings to communities of interest and proactively seek new collaborations to increase the impact of this project.

54 ENVIRONMENTAL SCIENCES↗