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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

RegularizedOptimization.jl: A Julia framework for regularized and nonsmooth optimization

RegularizedOptimization.jl is a Julia package that implements families of quadratic regularization and trust-region methods for solving the nonsmooth optimization problem $^{\textrm{minimize}}_{𝑥∈ℝ^𝑛}$ 𝑓(𝑥) + ℎ(𝑥) subject to 𝑐(𝑥) = 0, (1) where 𝑓 ∶ ℝ 𝑛 → ℝ and 𝑐 ∶ ℝ 𝑛 → ℝ 𝑚 are continuously differentiable, and ℎ ∶ ℝ 𝑛 → ℝ∪{+∞} is lower semi-continuous. The nonsmooth objective ℎ can be a regularizer, such as a sparsity inducing penalty, model simple constraints, such as 𝑥 belonging to a simple convex set, or can be a combination of both. All 𝑓, ℎ, and 𝑐 can be nonconvex. RegularizedOptimization.jl provides a modular and extensible framework for solving (1), and developing novel solvers. Currently, the following solvers are implemented: • Trust-region solvers TR and TRDH (Aravkin et al., 2022; Leconte & Orban, 2025) • Quadratic regularization solvers R2, R2DH and R2N (Aravkin et al., 2022; Diouane, Habiboullah, et al., 2024) • Levenberg-Marquardt solvers LM and LMTR (Aravkin et al., 2024) used when 𝑓 is a least-squares residual. • Augmented Lagrangian solver AL (De Marchi et al., 2023). All solvers rely on first derivatives of 𝑓 and 𝑐, and optionally on their second derivatives in the form of Hessian-vector products. If second derivatives are not available, quasi-Newton approximations can be used. In addition, the proximal mapping of the nonsmooth part ℎ, or adequate models thereof, must be evaluated. At each iteration, a step is computed by solving a subproblem of the form (1) inexactly, in which 𝑓, ℎ, and 𝑐 are replaced with appropriate models around the current iterate. The solvers R2, R2DH, and TRDH are particularly well suited to solve the subproblems, though they are general enough to solve (1). All solvers are allocation-free, so re-solves incur no additional allocations. To illustrate our claim of extensibility, a first version of the AL solver was implemented by an external contributor. Furthermore, a nonsmooth penalty approach, described in Diouane, Gollier, et al. (2024), is currently being developed, that relies on the library to efficiently solve the subproblems.

Gollier, Maxence [Polytechnique Montréal, QC (Cana↗
DOE OSTI2021

Robust Predictive Control for Modular Solid-State Transformer With Reduced DC Link and Parameter Mismatch

This paper presents the analysis and implementation of a predictive control method for dc-link regulation and voltage balance in a cascaded modular reduced dc-link solid-state transformer (SST). Passive components like bulky dc links limit the power density of power converters, especially medium-voltage (MV) SST. Reduced dc-link or low-inertia converters can dramatically reduce the size, cost, and weight by tolerating larger dc-link ripples and improve the reliability with electrolytic capacitor-less dc link. However, a small dc link leads to tight coupling between the input and the output stages, which is a challenge for control design. In stacked low-inertia converters (SLIC), the low-inertia converter modules are stacked for MV applications, resulting in coupling between the modules and making the control more challenging. A new model predictive control method which can achieve deadbeat regulation on the dc link without weighting factors has been proposed to address this novel problem. This paper focuses on analyzing the condition of the low-inertia dc link up to 80% ripple, the robustness of the control under parameter mismatches, high-order terms, and important implementation issues such as model-based sampling and computation delay compensation. Significantly, the high-order terms are introduced because of the large dc-link ripple. These high-order terms are unique to the SLIC and negligible in conventional high-inertia converters. A discrete-time large-signal model is built to capture the dc-link’s nonlinear dynamics, and the eigenvalues of a small-signal Jacobian matrix are analyzed with Floquet theory to evaluate stability, using the modular soft-switching solid-state transformer (M-S4T) as an example of the SLIC. Simulation and experimental results of an MVDC M-S4T verify the analysis and the predictive control method. Finally, the general application of the predictive control to low-inertia converters is compared against a conventional PI controller using a reduced dc-link active-front-end (AFE) rectifier as an example.

14 SOLAR ENERGY↗
DOE OSTI2020

Volume I. Introduction to DUNE

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2022

High Fidelity CFD Simulations Supporting the KP-FHR

Kairos Power, LLC, is developing its version of the Fluoride-cooled High-temperature Reactor, the KP-FHR. The design uses a pebble bed core with fluoride salt as a coolant. The pebbles used in the KP-FHR have a diameter of 4 cm, with a shell fuel region where TRISO particles are embedded. A Pebble bed core design is adopted by several Gen IV reactors, They boast many benefits, such as fuel integrity, highly efficient heat transfer, and passive safety. However, it is challenging to accurately predict temperature and flow inside a pebble bed. Traditional approaches use the porous media model, which regards the pebble bed as a continuous medium, but with different temperature fields representing different levels, such as the fluid temperature, pebble surface temperature, and pebble center temperature. Empirical heat transfer correlations are adopted to calculate the heat transfer coefficient between different phases. However, empirical correlations are usually validated with experimental data, which usually lacks detail inside the pebble bed. The available experimental data is also generally at a high Reynolds number, which falls outside of the conditions of KP-FHR. Explicit computational fluid dynamics (CFD) simulations of randomly packed pebble beds have only become feasible recently. This is thanks to the rapid development of computational power and scalable algorithms. In this work, we used the Spectral Element Method (SEM) CFD code NekRS to simulate the randomly packed pebble bed in a cylindrical container. NekRS, which is the GPU variant of Nek5000, but refactored to utilize the computational power of GPUs using the OCCA library to run on hybrid architecture high performance computing systems. It was initially developed with the libParamunal library, but truncated and tuned for large-scale turbulence simulation. As a result, the SEM reaches higher precision with the same degrees of freedom by using a high-order Lagrange polynomial basis distributed on Gauss-Lobatto-Legendre quadrature inside each element, compared to lower-order methods, such the Finite Volume Method and Finite Element Method. The report is divided into five parts. We start with a general discussion of the pebble bed reactor, along with a specific investigation into the KP-FHR. The second part presents the numerical methodology. In the third part, we study a modular pebble bed with 1741 pebbles in a container of 7 pebble-diameter radius. Beyond LES simulations done by NekRS, we also leveraged the thermal radiation model in OpenFOAM to study heat transfer under no-forced-flow scenarios. Then, in the fourth part we simulated a pebble bed similar to the size of the Hermes Test Reactor. The total number of pebbles is in these simulations is 34,374. The container radius is 14 pebble-diameters. Finally, the report concludes in part five, with a discussion of future work.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Software Architecture and Hierarchy of the Nasa Multiscale Analysis Tool

The NASA Multiscale Analysis Tool (NASMAT) serves as a state-of-the-art, “plug and play,” software package which utilizes multiscale recursive micromechanics as a platform for massively multiscale modeling for hierarchical materials and structures subjected to thermomechanical loads on high performance computing systems. This paper is intended to give an overview of the design of NASMAT and how the design supports modularity, upgradability and maintainability, interoperability, and utility. First, the software architecture and hierarchy will be explored. Details on each of the 11 NASMAT procedures and the arrangement of NASMAT data will be presented. Finally, application program interfaces (APIs) that were developed to facilitate the communication of NASMAT with other programs will be described.

multiscale modeling↗
NASA NTRS2000

The Numerical Propulsion System Simulation: An Overview

Advances in computational technology and in physics-based modeling are making large-scale, detailed simulations of complex systems possible within the design environment. For example, the integration of computing, communications, and aerodynamics has reduced the time required to analyze major propulsion system components from days and weeks to minutes and hours. This breakthrough has enabled the detailed simulation of major propulsion system components to become a routine part of designing systems, providing the designer with critical information about the components early in the design process. This paper describes the development of the numerical propulsion system simulation (NPSS), a modular and extensible framework for the integration of multicomponent and multidisciplinary analysis tools using geographically distributed resources such as computing platforms, data bases, and people. The analysis is currently focused on large-scale modeling of complete aircraft engines. This will provide the product developer with a "virtual wind tunnel" that will reduce the number of hardware builds and tests required during the development of advanced aerospace propulsion systems.

Lytle, John K.↗
DOE OSTI2019

Flat entanglement spectra in fixed-area states of quantum gravity

We use the Einstein-Hilbert gravitational path integral to investigate gravitational entanglement at leading order O(1/G). We argue that semiclassical states prepared by a Euclidean path integral have the property that projecting them onto a subspace in which the Ryu-Takayanagi or Hubeny-Rangamani-Takayanagi surface has definite area gives a state with a flat entanglement spectrum at this order in gravitational perturbation theory. This means that the reduced density matrix can be approximated as proportional to the identity to the extent that its Renyi entropies Sn are independent of n at this order. The n-dependence of Sn in more general states then arises from sums over the RT/HRT-area, which are generally dominated by different values of this area for each n. This provides a simple picture of gravitational entanglement, bolsters the connection between holographic systems and tensor network models, clarifies the bulk interpretation of algebraic centers which arise in the quantum errorcorrecting description of holography, and strengthens the connection between bulk and boundary modular Hamiltonians described by Jafferis, Lewkowycz, Maldacena, and Suh.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Quantifying particle movement in a spout-fluidized bed with irregular feedstock morphology

Here, the spout-bed fluidization behavior of nonspherical, 140 μm SiC feedstock was quantified via particle image velocimetry for varying gas distributor geometries. A bench-scale, room-temperature fluidization setup was assembled to model a 50 mm fluidized bed chemical vapor deposition (FB-CVD) system, and fluidized bed motion was captured using a high-speed camera. Modular tips with varying inlet geometries were 3D printed and tested on the bench-scale rig using identical feedstock and gas flow rates in the range of 3.0–9.0 L/min. Fluidization behavior was quantified by extracting parameters of the bed velocity, frequency, dead time, and other measurements, which were ranked for each inlet geometry configuration tested. The results from this work demonstrate that changing the path of inlet gas flow can significantly change the hydrodynamics within a spout-fluidized bed under identical feedstock, loading, and flow rate conditions, potentially enabling experimental control of particle fluidization behavior for a given condition. Moreover, composite rankings of fluidization behavior for unique distributor geometries hold potential to guide the design of FB-CVD experiments for various engineering and scientific applications.

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

Liquid-induced topological transformations of cellular microstructures

The fundamental topology of cellular structures—the location, number and connectivity of nodes and compartments—can profoundly affect their acoustic, electrical5, chemical, mechanical and optical properties, as well as heat, fluid and particle transport. Approaches that harness swelling, electromagnetic actuation and mechanical instabilities in cellular materials have enabled a variety of interesting wall deformations and compartment shape alterations, but the resulting structures generally preserve the defining connectivity features of the initial topology. Achieving topological transformation presents a distinct challenge for existing strategies: it requires complex reorganization, repacking, and coordinated bending, stretching and folding, particularly around each node, where elastic resistance is highest owing to connectivity. Here we introduce a two-tiered dynamic strategy that achieves systematic reversible transformations of the fundamental topology of cellular microstructures, which can be applied to a wide range of materials and geometries. Our approach requires only exposing the structure to a selected liquid that is able to first infiltrate and plasticize the material at the molecular scale, and then, upon evaporation, form a network of localized capillary forces at the architectural scale that ‘zip’ the edges of the softened lattice into a new topological structure, which subsequently restiffens and remains kinetically trapped. Reversibility is induced by applying a mixture of liquids that act separately at the molecular and architectural scales (thus offering modular temporal control over the softening–evaporation–stiffening sequence) to restore the original topology or provide access to intermediate modes. Guided by a generalized theoretical model that connects cellular geometries, material stiffness and capillary forces, we demonstrate programmed reversible topological transformations of various lattice geometries and responsive materials that undergo fast global or localized deformations. In conclusion, we then harness dynamic topologies to develop active surfaces with information encryption, selective particle trapping and bubble release, as well as tunable mechanical, chemical and acoustic properties.

42 ENGINEERING↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1973

Active optics simulation system

The active optics simulation system (AOSS) is a set of computer programs and associated software to be used in the development, design, and evaluation of a primary mirror control system for a large space telescope, (e.g., the tentatively proposed 3-meter telescope). The mathematical models of component subsystems and the solution of the physical processes that occur within the mirror surface control system were obtained, and based on these models AOSS simulates the behavior of the entire mirror surface control system as well as the behavior of the component subsystems. The program has a modular structure so that any subsystem module can be replaced or modified with minimum disruption of the rest of the simulation program.

Chi, C. H.↗
NASA NTRS2003

Design for Verification: Enabling Verification of High Dependability Software-Intensive Systems

Strategies to achieve confidence that high-dependability applications are correctly implemented include testing and automated verification. Testing deals mainly with a limited number of expected execution paths. Verification usually attempts to deal with a larger number of possible execution paths. While the impact of architecture design on testing is well known, its impact on most verification methods is not as well understood. The Design for Verification approach considers verification from the application development perspective, in which system architecture is designed explicitly according to the application's key properties. The D4V-hypothesis is that the same general architecture and design principles that lead to good modularity, extensibility and complexity/functionality ratio can be adapted to overcome some of the constraints on verification tools, such as the production of hand-crafted models and the limits on dynamic and static analysis caused by state space explosion.

Mehlitz, Peter C.↗
NASA NTRS1975

Main memory unit

The development of a main memory unit (MMU) for the space ultrareliable module computer (SUMC) model HTC is discussed. The design, fabrication, and test of basic memory modules (BMM) which were to be used in the design and construction of the MMU are described. The BMM was designed from state-of-the-art technologies which included large scale integration devices mounted and interconnected on a substrate to form a functional module to be utilized in the MMU development. A SUMC memory system design study is discussed which addressed itself to the BMM design and analysis to be conducted to determine the most efficient organization of the BMM in order to establish such modularity features as: word length expandability without redesign, high reliability, and fault tolerance. One MMU was designed, fabricated, tested, and delivered which will be electrical and mechanically compatible with the hybrid technology computer (HTC) model of the SUMC family of computers. The MMU will contain a storage capacity of 8196 36 bit words which includes a parity bit for each 8 bit byte of data.

Source record↗
NASA NTRS1992

Designer's unified cost model

A conceptual and preliminary designers' cost prediction model has been initiated. The model will provide a technically sound method for evaluating the relative cost of different composite structural designs, fabrication processes, and assembly methods that can be compared to equivalent metallic parts or assemblies. The feasibility of developing cost prediction software in a modular form for interfacing with state of the art preliminary design tools and computer aided design programs is being evaluated. The goal of this task is to establish theoretical cost functions that relate geometric design features to summed material cost and labor content in terms of process mechanics and physics. The output of the designers' present analytical tools will be input for the designers' cost prediction model to provide the designer with a data base and deterministic cost methodology that allows one to trade and synthesize designs with both cost and weight as objective functions for optimization. The approach, goals, plans, and progress is presented for development of COSTADE (Cost Optimization Software for Transport Aircraft Design Evaluation).

Freeman, William T.↗
DOE OSTI2020

Development of the Intelligent, Preventive Infrared (IR) Inspection System Housed in Hybrid Robotic Platforms

Robots and robotic systems that are designed for inspection, environmental study, and health and safety aid are becoming an increasing necessity. However, there is a number of challenges that accompany robots that are designed for these specific applications. These challenges include: navigating compact, enclosed spaces, travelling over multiple terrains and large obstacles, using the proper sensing and detection methods to assess an environment, and the use of lightweight and durable materials. The robotic platforms currently in development look at all of these challenges and attempt to overcome them. These designs specific use of hybrid robotic platforms, or platform that utilizes soft and rigid materials, allows for a more flexible platform and makes environments more navigable. To further improve the navigation of the platforms and environmental assessment, a novel infrared detection system is housed in the platforms to create a robot that can be used for the applications listed above and more. Objectives: Further develop two types of robotic platforms that utilize additive manufacturing, soft materials, and rigid materials. Continue the development of an intelligent and inhibitory infrared detection system based on an artificial intelligence (AI) algorithm. Continued study and fabrication of active soft materials designed for both sensing and actuation in hybrid robotic systems. Improve additive manufacturing fabrication to design rigid and semi-rigid components for hybrid robotic platforms. Transformable Wheel Robotic Platform: The chassis, wheels, tires and inspection system housing use different additive manufacturing techniques for fabrication. Continued work with additive manufacturing has lead to studies in metal-based printing and modular design and manufacturing. The new platform design with integrated electrical component printed. This will allow integration of the sensor housing onto the platform. Electrical components are being tested for battery life and performance. To improve this performance, such as integration of Lithium Polymer (LiPo) batteries. Snake Robotic Platform: The main focus of the development has centered around liquid-based soft actuators. That act on the principles of electrostatic and hydraulic actuation. A liquid dielectric sits between two compliant electrodes, contained by a flexible polymer shell. The electrodes and film gradually collapse toward each other from one corner of the electrode to the other. When the electrodes and film close together, a majority of the fluid is pushed into the area not covered by an electrode. A thin layer of the liquid dielectric remains between the electrode. The actuators will be stacked to cause large displacement, and move the linkages. The chassis of this platform uses purely additively manufactured linkages. Intelligent, Preventive IR Inspection System: Development of the AI for the system has lead to using a Scikit-Learn which assists in creating predictive models based on Regression, clustering, classification etc. To improve the infrared thermometry for low emissivity sources, work on the fabrication of a tandem photoconductive infrared thermometer was a main focus. Distance-Voltage-Temperature response data has been collected in the range of 7 cm - 100 cm and 200-400 deg. C. Modifications were made to the existing test bench to have a better control over the data. Automated data collection was realized using a Python code and an Arduino controlled stepper motor to increase the sample rate. A protective enclosure has been built around the setup to minimize the effect of the environment on the measurements. To better predict temperature, different AI models are being optimized. The regression model, LARS showed a high accuracy but had convergence issues and only works for the current test set-up. Results: The Transformable Wheel Robot has developed into a more flexible and modular platform. With the improvements to the current work, effort on the tire or soft gripper design been a large focus. The soft grippers will be interchange able to allow for increased performance in identified terrain types. The development of the liquid-based actuators allows the snake robotic platform to achieve the goals of being flexible and able to navigate confined spaces. However, there is room for improvement. Optimization work is currently being done in COMSOL Multiphysics. With the current IR system set-up the LARS model perfectly predicts the data; however, considering the mobility aspect of the project other models will allow for an optimized system. Testing of other model types is currently being done.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

A global coupled ensemble data assimilation system using the Community Earth System Model and the Data Assimilation Research Testbed

In this paper, we present a description of the CESM/DART ensemble coupled data assimilation (DA) system based on the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. The CESM/DART should be viewed as a flexible system to support the DA needs of the CESM research community and not as a static reanalysis product. In this implementation of the CESM/DART, conventional insitu observations of the ocean and atmosphere are assimilated into the respective component models of the CESM using a 30-member ensemble adjustment Kalman filter (EAKF). CESM/DART is run in a “weakly coupled” configuration wherein observations native to each climate system component only directly impact the state vector for that component. Information is passed between components indirectly through the short-term coupled model forecasts that provide the EAKF background ensemble. This system leverages previous ensemble DA development for the Community Atmosphere Model and Parallel Ocean Program models using the DART EAKF. The CESM/DART project is a step towards providing increasingly useful DA capabilities for the CESM research community. Results are presented for our prototype 12-year reanalysis, run from 1970 to mid 1982. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system in the well-observed Northern Hemisphere. A collection of monthly average variables, climate mode indices, observation diagnostics and snapshots of synoptic weather in the ocean and atmosphere are compared to established datasets, showing especially good agreement in the Northern Hemisphere. A discussion of the CESM/DART as a modular, community facility and the benefits and challenges associated with this vision is also included.

54 ENVIRONMENTAL SCIENCES↗