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DOE OSTI2024

Sensitivity of Simulations of Double-detonation Type Ia Supernovae to Integration Methodology

Abstract We study the coupling of hydrodynamics and reactions in simulations of the double-detonation model for Type Ia supernovae. When assessing the convergence of simulations, the focus is usually on spatial resolution; however, the method of coupling the physics together as well as the tolerances used in integrating a reaction network also play an important role. In this paper, we explore how the choices made in both coupling and integrating the reaction portion of a simulation (operator/Strang splitting versus the simplified spectral deferred corrections method we introduced previously) influences the accuracy, efficiency, and nucleosynthesis of simulations of double detonations. We find no need to limit reaction rates or reduce the simulation time step to the reaction timescale. The entire simulation methodology used here is GPU-accelerated and made freely available as part of the Castro simulation code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2012

Development of a dynamic simulator for a natural gas combined cycle (NGCC) power plant with post-combustion carbon capture

The AVESTAR Center located at the U.S. Department of Energy’s National Energy Technology Laboratory and West Virginia University is a world-class research and training environment dedicated to using dynamic process simulation as a tool for advancing the safe, efficient and reliable operation of clean energy plants with CO{sub 2} capture. The AVESTAR Center was launched with a high-fidelity dynamic simulator for an Integrated Gasification Combined Cycle (IGCC) power plant with pre-combustion carbon capture. The IGCC dynamic simulator offers full-scope Operator Training Simulator (OTS) Human Machine Interface (HMI) graphics for realistic, real-time control room operation and is integrated with a 3D virtual Immersive Training Simulator (ITS), thus allowing joint control room and field operator training. The IGCC OTS/ITS solution combines a “gasification with CO{sub 2} capture” process simulator with a “combined cycle” power simulator into a single high-performance dynamic simulation framework. This presentation will describe progress on the development of a natural gas combined cycle (NGCC) dynamic simulator based on the syngas-fired combined cycle portion of AVESTAR’s IGCC dynamic simulator. The 574 MW gross NGCC power plant design consisting of two advanced F-class gas turbines, two heat recovery steam generators (HRSGs), and a steam turbine in a multi-shaft 2x2x1 configuration will be reviewed. Plans for integrating a post-combustion carbon capture system will also be discussed.

03 NATURAL GAS↗

DOE OSTI2014

Workshop on Modeling and Simulation of Systems and Applications, August 13-14, 2014, University of Washington, Seattle

In our efforts to continue the momentum toward quantitative understanding of the performance, power, and reliability of systems and applications, the modeling and simulation (hereafter ModSim) community gathered for its annual Workshop on Modeling & Simulation of Systems and Applications, held August 13-14, 2014 in Seattle. The 2014 Workshop on Modeling & Simulation of Systems and Applications, or ModSim 2014, focused on three critical technological areas: 1. Integrated Modeling and Simulation of Performance, Power, and Reliability; 2. Standards, Integration, and Interoperability of ModSim Methodologies and Tools; and 3. Advances in Core Modeling and Simulation Methods and Tools. The two-day meeting featured 21 presentations (based on peer-reviewed submissions) delivered over six parallel sessions, and there were six invited talks. Discussions aimed toward consolidating and directing this diverse technical community were especially beneficial as we continue to map out the future and form of ModSim research and development. This report presents a summary of the presentations, discussions, and findings culled from ModSim 2014. Each of the three critical technology areas are presented, structured as major research directions, gaps, cross-pollination, and path forward.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Efficient simulation of press hardening process through integrated structural and CFD analyses

Press hardened steel parts are being increasingly used in automotive structures for their higher strength to meet safety standards while reducing vehicle weight to improve fuel consumption. However, manufacturing of sheet metal parts by press hardening process to achieve desired properties is extremely challenging as it involves complex interaction of plastic deformation, metallurgical change, thermal distribution, and fluid flow. Numerical simulation is critical for successful design of the process and to understand the interaction among the numerous process parameters to control the press hardening process in order to consistently achieve desired part properties. Until now there has been no integrated commercial software solution that can efficiently model the complete process from forming of the blank, heat transfer between the blank and tool, microstructure evolution in the blank, heat loss from tool to the fluid that flows through water channels in the tools. In this study, a numerical solution based on Altair HyperWorks® product suite involving RADIOSS®, a non-linear finite element based structural analysis solver and AcuSolve®, an incompressible fluid flow solver based on Galerkin Least Square Finite Element Method have been utilized to develop an efficient solution for complete press hardening process design and analysis. RADIOSS is used to handle the plastic deformation, heat transfer between the blank and tool, and microstructure evolution in the blank during cooling. While AcuSolve is used to efficiently model heat loss from tool to the fluid that flows through water channels in the tools. The approach is demonstrated through some case studies.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Approaches for Autonomous Peer to Peer Communication: For Resilient Community Microgrids

The final phase of the Islandable Communications task of the SECURE project consisted of integrating the islandable communications network into a microgrid simulation at NREL. Initially integration was planned for a microgrid implemented as a mix of simulation and physical hardware. As the project evolved, the microgrid became fully simulated. The simulation environment was structured to break out the communication paths that would pass over a communication network in a field demonstration. The Islandable Communication network was integrated into the simulation environment, allowing both a commercial cellular carrier and a Private LTE network to be evaluated. The microgrid simulation showed differences in performance and behavior when operating over the wireless networks (compared to operating over wired Ethernet in the lab). These differences point to further research opportunities to better understand how microgrid communication carried over real-world or impaired networks affects the microgrid system behavior.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2013

Two-Way Integration of WRF and CCSM for Regional Climate Simulations

Under the support of the DOE award DE-SC0004670, we have successfully developed an integrated climate modeling system by nesting Weather Research and Forecasting (WRF) model within the Community Climate System Model (CCSM) and the ensuing new generation Community Earth System Model (CESM). The integrated WRF/CESM system is intended as one method of global climate modeling with regional simulation capabilities. It allows interactive dynamical regional downscaling in the computational flow of present or future global climate simulations. This capability substantially simplifies the process of dynamical downscaling by avoiding massive intermediate model outputs at high frequency that are typically required for offline regional downscaling. The inline coupling also has the advantage of higher temporal resolution for the interaction between regional and global model components. With the aid of the inline coupling, a capability has also been developed to ingest other global climate simulations (by CESM or other models), which otherwise may not have necessary intermediate outputs for regional downscaling, to realize their embedded regional details. It is accomplished by relaxing the global atmospheric state of the integrated model to that of the source simulations with an appropriate time scale. This capability has the potential to open a new venue for ensemble regional climate simulations using a single modeling system. Furthermore, this new modeling system provides an effective modeling framework for the studies of physical and dynamical feedbacks of regional weather phenomena to the large scale circulation. The projected uses of this capability include the research of up-scaling effect of regional weather system, and its use as an alternative physical representation of sub-scale processes in coarser-resolution climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Towards Z-Next: The Integration of Theory, Experiments, and Computational Simulation in a Bayesian Data Assimilation Framework

Making reliable predictions in the presence of uncertainty is critical to high-consequence modeling and simulation activities, such as those encountered at Sandia National Laboratories. Surrogate or reduced-order models are often used to mitigate the expense of performing quality uncertainty analyses with high-fidelity, physics-based codes. However, phenomenological surrogate models do not always adhere to important physics and system properties. This project develops surrogate models that integrate physical theory with experimental data through a maximally-informative framework that accounts for the many uncertainties present in computational modeling problems. Correlations between relevant outputs are preserved through the use of multi-output or co-predictive surrogate models; known physical properties (specifically monotoncity) are also preserved; and unknown physics and phenomena are detected using a causal analysis. By endowing surrogate models with key properties of the physical system being studied, their predictive power is arguably enhanced, allowing for reliable simulations and analyses at a reduced computational cost.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Integrated Cost and Schedule using Monte Carlo Simulation of a CPM Model - 12419

This discussion of the recommended practice (RP) 57R-09 of AACE International defines the integrated analysis of schedule and cost risk to estimate the appropriate level of cost and schedule contingency reserve on projects. The main contribution of this RP is to include the impact of schedule risk on cost risk and hence on the need for cost contingency reserves. Additional benefits include the prioritizing of the risks to cost, some of which are risks to schedule, so that risk mitigation may be conducted in a cost-effective way, scatter diagrams of time-cost pairs for developing joint targets of time and cost, and probabilistic cash flow which shows cash flow at different levels of certainty. Integrating cost and schedule risk into one analysis based on the project schedule loaded with costed resources from the cost estimate provides both: (1) more accurate cost estimates than if the schedule risk were ignored or incorporated only partially, and (2) illustrates the importance of schedule risk to cost risk when the durations of activities using labor-type (time-dependent) resources are risky. Many activities such as detailed engineering, construction or software development are mainly conducted by people who need to be paid even if their work takes longer than scheduled. Level-of-effort resources, such as the project management team, are extreme examples of time-dependent resources, since if the project duration exceeds its planned duration the cost of these resources will increase over their budgeted amount. The integrated cost-schedule risk analysis is based on: - A high quality CPM schedule with logic tight enough so that it will provide the correct dates and critical paths during simulation automatically without manual intervention. - A contingency-free estimate of project costs that is loaded on the activities of the schedule. - Resolves inconsistencies between cost estimate and schedule that often creep into those documents as project execution proceeds. - Good-quality risk data that are usually collected in risk interviews of the project team, management and others knowledgeable in the risk of the project. The risks from the risk register are used as the basis of the risk data in the risk driver method. The risk driver method is based in the fundamental principle that identifiable risks drive overall cost and schedule risk. - A Monte Carlo simulation software program that can simulate schedule risk, burn WM2012 rate risk and time-independent resource risk. The results include the standard histograms and cumulative distributions of possible cost and time results for the project. However, by simulating both cost and time simultaneously we can collect the cost-time pairs of results and hence show the scatter diagram ('football chart') that indicates the joint probability of finishing on time and on budget. Also, we can derive the probabilistic cash flow for comparison with the time-phased project budget. Finally the risks to schedule completion and to cost can be prioritized, say at the P-80 level of confidence, to help focus the risk mitigation efforts. If the cost and schedule estimates including contingency reserves are not acceptable to the project stakeholders the project team should conduct risk mitigation workshops and studies, deciding which risk mitigation actions to take, and re-run the Monte Carlo simulation to determine the possible improvement to the project's objectives. Finally, it is recommended that the contingency reserves of cost and of time, calculated at a level that represents an acceptable degree of certainty and uncertainty for the project stakeholders, be added as a resource-loaded activity to the project schedule for strategic planning purposes. The risk analysis described in this paper is correct only for the current plan, represented by the schedule. The project contingency reserve of time and cost that are the main results of this analysis apply if that plan is to be followed. Of course project managers have the option of re-planning and re-scheduling in the face of new facts, in part by mitigating risk. This analysis identifies the high-priority risks to cost and to schedule, which assist the project manager in planning further risk mitigation. Some project managers reject the results and argue that they cannot possibly be so late or so overrun. Those project managers may be wasting an opportunity to mitigate risk and get a more favorable outcome. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗

DOE OSTI2012

Simulation of Photovoltaic Power Output for the Salt River Project Integration Study

One year of power output was simulated at one-minute intervals for each of fourteen hypothetical utility-scale photovoltaic power plants and for the aggregate power output from a large number of distribution-connected photovoltaic systems. For utility-scale plants, the simulation first constructs one-year time series of global horizontal irradiance at one-minute intervals at each plant location, and a performance model translates irradiance and weather information to AC output power. Distribution-connected photovoltaic systems comprise a variety of system configurations: residential-scale rooftop systems at various tilts; commercial-scale flat-roof mounted systems; and commercial-scale ground-mounted tracked systems. For distribution-connected PV systems, the simulation estimates the time series of spatially-averaged irradiance for the region containing the systems, and the performance model is employed to estimate power aggregate power from all systems. The simulation results are validated by comparing statistics for the time series of irradiance with statistics for measured irradiance within the region.

14 SOLAR ENERGY↗

DOE OSTI2012

Constraining the Influence of Natural Variability to Improve Estimates of Global Aerosol Indirect Effects in a Nudged Version of the Community Atmosphere Model 5

Natural modes of variability on many timescales influence aerosol particle distributions and cloud properties such that isolating statistically significant differences in cloud radiative forcing due to anthropogenic aerosol perturbations (indirect effects) typically requires integrating over long simulations. For state-of-the-art global climate models (GCM), especially those in which embedded cloud-resolving models replace conventional statistical parameterizations (i.e. multi-scale modeling framework, MMF), the required long integrations can be prohibitively expensive. Here an alternative approach is explored, which implements Newtonian relaxation (nudging) to constrain simulations with both pre-industrial and present-day aerosol emissions toward identical meteorological conditions, thus reducing differences in natural variability and dampening feedback responses in order to isolate radiative forcing. Ten-year GCM simulations with nudging provide a more stable estimate of the global-annual mean aerosol indirect radiative forcing than do conventional free-running simulations. The estimates have mean values and 95% confidence intervals of -1.54 ± 0.02 W/m2 and -1.63 ± 0.17 W/m2 for nudged and free-running simulations, respectively. Nudging also substantially increases the fraction of the world’s area in which a statistically significant aerosol indirect effect can be detected (68% and 25% of the Earth's surface for nudged and free-running simulations, respectively). One-year MMF simulations with and without nudging provide global-annual mean aerosol indirect radiative forcing estimates of -0.80 W/m2 and -0.56 W/m2, respectively. The one-year nudged results compare well with previous estimates from three-year free-running simulations (-0.77 W/m2), which showed the aerosol-cloud relationship to be in better agreement with observations and high-resolution models than in the results obtained with conventional parameterizations.

aerosol↗

DOE OSTI2015

Improving the trust in results of numerical simulations and scientific data analytics

This white paper investigates several key aspects of the trust that a user can give to the results of numerical simulations and scientific data analytics. In this document, the notion of trust is related to the integrity of numerical simulations and data analytics applications. This white paper complements the DOE ASCR report on Cybersecurity for Scientific Computing Integrity by (1) exploring the sources of trust loss; (2) reviewing the definitions of trust in several areas; (3) providing numerous cases of result alteration, some of them leading to catastrophic failures; (4) examining the current notion of trust in numerical simulation and scientific data analytics; (5) providing a gap analysis; and (6) suggesting two important research directions and their respective research topics. To simplify the presentation without loss of generality, we consider that trust in results can be lost (or the results’ integrity impaired) because of any form of corruption happening during the execution of the numerical simulation or the data analytics application. In general, the sources of such corruption are threefold: errors, bugs, and attacks. Current applications are already using techniques to deal with different types of corruption. However, not all potential corruptions are covered by these techniques. We firmly believe that the current level of trust that a user has in the results is at least partially founded on ignorance of this issue or the hope that no undetected corruptions will occur during the execution. This white paper explores the notion of trust and suggests recommendations for developing a more scientifically grounded notion of trust in numerical simulation and scientific data analytics. We first formulate the problem and show that it goes beyond previous questions regarding the quality of results such as V&V, uncertainly quantification, and data assimilation. We then explore the complexity of this difficult problem, and we sketch complementary general approaches to address it. This paper does not focus on the trust that the execution will actually complete. The product of simulation or of data analytic executions is the final element of a potentially long chain of transformations, where each stage has the potential to introduce harmful corruptions. These corruptions may produce results that deviate from the user-expected accuracy without notifying the user of this deviation. There are many potential sources of corruption before and during the execution; consequently, in this white paper we do not focus on the protection of the end result after the execution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Development of HELICS-Based High-Performance Cyber-Physical Co-Simulation Framework for Distributed Energy Resources Applications

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications resulting in the typical smart grid cyber-physical system. To fully understand the interdependency between them, we propose to integrate the Network Simulator 3 (NS3) into the High Engine for Large-Scale Infrastructure Co-Simulation (HELICS), a new open-source, cyber-physical-energy co-simulation platform. This paper aims to the development and case study of the HELICS-based high performance distribution-communication co-simulation framework for the DER coordination. The novel co-simulation framework for the NS3 integrating into the HELICS is developed. The DER monitoring application about hybrid smart grid communication network design is simulated and validated on this proposed HELICS-based cyber-physical co-simulation platform.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2014

Enhanced stopping of macro-particles in particle-in-cell simulations

We derive an equation for energy transfer from relativistic charged particles to a cold background plasma appropriate for finite-size particles that are used in particle-in-cell simulation codes. Expressions for one-, two-, and three-dimensional particles are presented, with special attention given to the two-dimensional case. This energy transfer is due to the electric field of the wake set up in the background plasma by the relativistic particle. The enhanced stopping is dependent on the q{sup 2}/m, where q is the charge and m is the mass of the relativistic particle, and therefore simulation macro-particles with large charge but identical q/m will stop more rapidly. The stopping power also depends on the effective particle shape of the macro-particle. These conclusions are verified in particle-in-cell simulations. We present 2D simulations of test particles, relaxation of high-energy tails, and integrated fast ignition simulations showing that the enhanced drag on macro-particles may adversely affect the results of these simulations in a wide range of high-energy density plasma scenarios. We also describe a particle splitting algorithm which can potentially overcome this problem and show its effect in controlling the stopping of macro-particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2012

Best-estimate plus uncertainty thermal-hydraulic stability analysis of BWRs using TRACG code

Over the last decade, Boiling Water Reactor (BWR) power up-rates have increased plant rated power output significantly. Subsequent projects have expanded flow domains (e.g. MELLLA+) for operation at these higher power levels. This has resulted in an increase in the power to flow ratio in regions susceptible to reactor thermal-hydraulic instabilities. Since BWRs are susceptible to coupled thermal-hydraulic/nuclear oscillations when operating at these conditions, such oscillations must be prevented or reliably detected and suppressed. The Detect and Suppress Solution - Confirmation Density (DSS-CD) is the most sophisticated GEH BWR instability protection system ever employed. DSS-CD implements algorithms that monitor closely-spaced groups of Local Power Range Monitor (LPRM) detectors to detect periodic behavior typical of reactor instability events. This system is able to detect small, localized power variations in the core, distinguish between true instabilities and plant noise, and trip/scram the reactor while maintaining adequate safety margins. The combination of hardware, software, and system setpoints provides protection against violation of the Safety Limit Minimum Critical Power Ratio (SLMCPR) for anticipated oscillations. To support DSS-CD implementation, the TRACG system code is used to simulate events to confirm the capability of the DSS-CD solution for early oscillation detection and suppression. TRACG is a GEH proprietary version of the Transient Reactor Analysis Code (TRAC). TRACG includes a multi-dimensional, two-fluid model for the reactor thermal-hydraulics and a three-dimensional reactor kinetics model. The models are qualified to simulate a large variety of tests and reactor configurations, including thermal-hydraulic stability events. These features allow for detailed, best-estimate simulation of a wide range of BWR phenomena. A set of integrated TRACG event simulations for reasonably limiting anticipated events can be used to calculate the effect on the Minimum Critical Power Ratio (MCPR) performance. The purpose of the DSS-CD TRACG analysis is to confirm the inherent MCPR margin afforded by the solution design. This paper presents the Best Estimate Plus Uncertainty (BEPU) DSS-CD TRACG methodology and its application to BWR Thermal-Hydraulic (T-H) stability analyses. The statistical Code Scaling, Applicability and Uncertainty (CSAU) methodology (defined in NUREG/CR-5249) is used to calculate the MCPR uncertainty. The TRACG simulation includes a full core individual bundle model in which each fuel bundle is modeled as an individual T-H channel. The complete CSAU analysis of full core individual bundle model is an innovative solution represents the state-of-the-art stability analysis of BWRs and is the first ever full statistical analysis for stability safety analyses. The adoption of BEPU methodologies for stability analyses advances the understanding of the associated physical phenomena and maintains the safety of reactor plant operation in expanded operation domain with up-rated power. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

A Framework for Daylighting Optimization in Whole Buildings with OpenStudio

We present a toolkit and workflow for leveraging the OpenStudio (Guglielmetti et al. 2010) platform to perform daylighting analysis and optimization in a whole building energy modeling (BEM) context. We have re-implemented OpenStudio's integrated Radiance and EnergyPlus functionality as an OpenStudio Measure. The OpenStudio Radiance Measure works within the OpenStudio Application and Parametric Analysis Tool, as well as the OpenStudio Server large scale analysis framework, allowing a rigorous daylighting simulation to be performed on a single building model or potentially an entire population of programmatically generated models. The Radiance simulation results can automatically inform the broader building energy model, and provide dynamic daylight metrics as a basis for decision. Through introduction and example, this paper illustrates the utility of the OpenStudio building energy modeling platform to leverage existing simulation tools for integrated building energy performance simulation, daylighting analysis, and reportage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Trifan, Anda↗