Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Simulators”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Risk Reduction and Training using Simulation Based Tools - 12180

Process Modeling and Simulation (M and S) has been used for many years in manufacturing and similar domains, as part of an industrial engineer's tool box. Traditionally, however, this technique has been employed in small, isolated projects where models were created from scratch, often making it time and cost prohibitive. Newport News Shipbuilding (NNS) has recognized the value of this predictive technique and what it offers in terms of risk reduction, cost avoidance and on-schedule performance of highly complex work. To facilitate implementation, NNS has been maturing a process and the software to rapidly deploy and reuse M and S based decision support tools in a variety of environments. Some examples of successful applications by NNS of this technique in the nuclear domain are a reactor refueling simulation based tool, a fuel handling facility simulation based tool and a tool for dynamic radiation exposure tracking. The next generation of M and S applications include expanding simulation based tools into immersive and interactive training. The applications discussed here take a tool box approach to creating simulation based decision support tools for maximum utility and return on investment. This approach involves creating a collection of simulation tools that can be used individually or integrated together for a larger application. The refueling simulation integrates with the fuel handling facility simulation to understand every aspect and dependency of the fuel handling evolutions. This approach translates nicely to other complex domains where real system experimentation is not feasible, such as nuclear fuel lifecycle and waste management. Similar concepts can also be applied to different types of simulation techniques. For example, a process simulation of liquid waste operations may be useful to streamline and plan operations, while a chemical model of the liquid waste composition is an important tool for making decisions with respect to waste disposition. Integrating these tools into a larger virtual system provides a tool for making larger strategic decisions. The key to integrating and creating these virtual environments is the software and the process used to build them. Although important steps in the direction of using simulation based tools for nuclear domain, the applications described here represent only a small cross section of possible benefits. The next generation of applications will, likely, focus on situational awareness and adaptive planning. Situational awareness refers to the ability to visualize in real time the state of operations. Some useful tools in this area are Geographic Information Systems (GIS), which help monitor and analyze geographically referenced information. Combined with such situational awareness capability, simulation tools can serve as the platform for adaptive planning tools. These are the tools that allow the decision maker to react to the changing environment in real time by synthesizing massive amounts of data into easily understood information. For the nuclear domains, this may mean creation of Virtual Nuclear Systems, from Virtual Waste Processing Plants to Virtual Nuclear Reactors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Parallel Simulation of Quantum Networks with Distributed Quantum State Management

Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues for building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. As the size of the simulated networks increases, however, sequential execution becomes time-consuming. Parallel execution presents a suitable method for scalable simulations of large-scale quantum networks, but the unique attributes of quantum information create unexpected challenges. In this work, we identify requirements for parallel simulation of quantum networks and develop the first parallel discrete-event quantum network simulator by modifying the existing serial simulator SeQUeNCe. Our contributions include the design and development of a quantum state manager (QSM) that maintains shared quantum information distributed across multiple processes. We also optimize our parallel code by minimizing the overhead of the QSM and decreasing the amount of synchronization needed among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network topology using 2 to 128 processes. We also observe an efficiency greater than 0.5 for up to 32 processes in a linear network topology of the same size and with the same workload. We repeat this evaluation with a randomized workload on a caveman network. We also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We have released the parallel SeQUeNCe simulator as an open source tool alongside the existing sequential version.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ASPIRES: Airport Shuttle Planning and Improved Routing Event-driven Simulation

Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. Here, the simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2012

Taming Wild Horses: The Need for Virtual Time-based Scheduling of VMs in Network Simulations

The next generation of scalable network simulators employ virtual machines (VMs) to act as high-fidelity models of traffic producer/consumer nodes in simulated networks. However, network simulations could be inaccurate if VMs are not scheduled according to virtual time, especially when many VMs are hosted per simulator core in a multi-core simulator environment. Since VMs are by default free-running, on the outset, it is not clear if, and to what extent, their untamed execution affects the results in simulated scenarios. Here, we provide the first quantitative basis for establishing the need for generalized virtual time scheduling of VMs in network simulators, based on an actual prototyped implementations. To exercise breadth, our system is tested with multiple disparate applications: (a) a set of message passing parallel programs, (b) a computer worm propagation phenomenon, and (c) a mobile ad-hoc wireless network simulation. We define and use error metrics and benchmarks in scaled tests to empirically report the poor match of traditional, fairness-based VM scheduling to VM-based network simulation, and also clearly show the better performance of our simulation-specific scheduler, with up to 64 VMs hosted on a 12-core simulator node.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Performance evaluation of an agent-based occupancy simulation model

Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Assessment of a Detailed Biomass Pyrolysis Kinetic Scheme in Multiscale Simulations of a Single-Particle Pyrolyzer and a Pilot-Scale Entrained Flow Pyrolyzer

A detailed biomass pyrolysis kinetic scheme was assessed in the multiscale simulations of a single-particle pyrolyzer with slow pyrolysis and a pilot-scale entrained flow pyrolyzer with fast pyrolysis. The detailed kinetic scheme of biomass pyrolysis developed by the CRECK group consists of 32 reactions and 58 species. A multiscale simulation model was developed, where the CRECK kinetics was employed to simulate biomass pyrolysis reactions, a one-dimensional particle model was utilized to simulate the intraparticle transport phenomena, and the particle-in-cell (PIC) model was employed to simulate the hydrodynamics. The multiscale model was first applied to simulate a single-particle pyrolysis experiment. The simulation with nonisothermal particles matched the experimental data better than the simulation with isothermal particles. Then the multiscale model was applied to simulate the pilot-scale entrained flow pyrolyzer. In this case, the simulation with isothermal particles matched the experimental data better than the simulation with nonisothermal particles. The reason for this difference might be that the kinetics itself already partially included the intraparticle transport effect as it was fitted using both TGA data (slow pyrolysis of small size biomass) and fluidized bed data (fast pyrolysis of relatively large size biomass). This study provides some insights into biomass pyrolysis kinetics development and pyrolyzer multiscale simulation for a future study.

09 BIOMASS FUELS↗
DOE OSTI2019

A comparison of explosion energies for simulated and observed core-collapse supernovae

ABSTRACT There are now 20 multidimensional core-collapse supernova (CCSN) simulations that explode. However, these simulations have explosion energies that are a few times 1050 erg, not 1051 erg. In this manuscript, we compare the inferred explosion energies of these simulations and observations of 40 SN IIP. Assuming a lognormal distribution, the mean explosion energy for these observations is $\mu _{\rm obs} = -0.23^{+0.08}_{-0.12}$ ($\log _{10}(E/10^{51}\, {\rm erg})$) and the width is $\sigma _{\rm obs} = 0.52^{+0.09}_{-0.08}$. Only three CCSN codes have sufficient simulations to compare with observations: CHIMERA, CoCoNuT-FMT, and FORNAX. Currently, FORNAX has the largest sample of simulations. The two-dimensional FORNAX simulations show a correlation between explosion energy and progenitor mass, ranging from linear to quadratic, Esim ∝ M1 − 2; this correlation is consistent with inferences from observations. In addition, we infer the ratio of the observed-to-simulated explosion energies, Δ = log10(Eobs/Esim). For the CHIMERA set, Δ = 0.25 ± 0.07; for CoCoNuT-FMT, Δ = 0.49 ± 0.07; for FORNAX2D, Δ = 0.62 ± 0.06, and for FORNAX3D, Δ = 0.85 ± 0.07. On average, the simulations are less energetic than inferred energies from observations (Δ ≈ 0.6), but we also note that the variation among the simulations [max(Δ) − min(Δ) ≈ 0.6] is as large as this average offset. This suggests that further improvements to the simulations could resolve the discrepancy. Furthermore, both the simulations and observations are heavily biased. In this preliminary comparison, we model these biases, but to more reliably compare the explosion energies, we recommend strategies to unbias both the simulations and observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models

We describe a new framework that allows users to explore and steer ensembles of energy systems simulations by coupling multiple energy models and interactive visualization through a dataflow API. Through the visual interface, users can interactively explore complex parameter spaces populated by hundreds, or thousands, of simulation runs and interactively spawn new simulations to 'fill in' regions of interest in the parameter space. The computational and visualization capabilities reside within a general-purpose dataflow architecture for connecting producers of multidimensional timeseries data, such as energy simulations, with consumers of that data, whether they be visualizations, statistical analyses, or datastores. Fast computation and agile dataflow can enhance the engagement with energy simulations, allowing users to populate the parameter space in real time. However, many energy simulations are far too slow to provide an interactive response. To support interactive feedback, we are creating reduced-form simulations developed through machine learning techniques, which provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost. These reduced-form simulations have response times on the order of seconds, suitable for real-time human-in-the-loop design and analysis. The approximation methods apply to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. Such reduced-form representations do not replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and exploration for large ensembles of simulations. The improved understanding, facilitated by the reduced-form models, dataflow API, and visualization tools, allows researchers to better allocate computational resources to capture informative relationships within the system as well as provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Rapid Simulation of Unconventional Reservoirs by Multidomain Multiresolution Modeling Based on the Diffusive Time of Flight

Summary The fast marching method (FMM)-based rapid flow simulation has been shown to accelerate simulation efficiency by orders of magnitude by transforming 3D simulation to equivalent 1D simulation using the concept of the “diffusive time of flight” (DTOF). However, the 1D transformation does not directly apply to multiwell problems. In this paper, we propose a novel DTOF-based multidomain multiresolution discretization scheme to accelerate multiwell simulation of unconventional reservoirs. Our method formulates multiwell simulation problems based on the DTOF which displays the pressure front propagation in unconventional reservoirs. The DTOF contours are used to partition the reservoir into local and shared domains. A local domain is where the flow is dominated by a single well, and the shared domain is where the fluid flow is influenced by multiple wells. The DTOF contours expand independently in local domains and interfere in the shared domain. After the partitioning, each domain is discretized using a multiresolution scheme whereby the original 3D fine mesh is preserved near the wells to account for detailed physics including gravity, and the rest of the domain is discretized into 1D mesh based on the DTOF contours to alleviate the simulation workload. The power and efficacy of our approach are demonstrated using synthetic and field-scale simulation models with different degrees of geologic and well-completion complexity. The simulation results, number of active cells, and computation time for the proposed discretization scheme are compared with the original high-fidelity 3D model for each case. The results show that the proposed method is suitable for multiwell simulation problems in unconventional reservoirs and can accelerate flow simulations by orders of magnitude with minimal loss of accuracy. The novelty of this work is the creation of DTOF-derived multiresolution discretization with local and shared domains to simplify and accelerate the calculation of subsurface flow problems, especially in unconventional reservoirs. Our workflow can be easily interfaced with commercial simulators, making it suitable for large-scale field applications.

Engineering↗
DOE OSTI2011

Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE)

This report is based on a SC/EERE Workshop to Identify Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE), held March 3, 2011, to determine strategic focus areas that will accelerate innovation in engine design to meet national goals in transportation efficiency. The U.S. has reached a pivotal moment when pressures of energy security, climate change, and economic competitiveness converge. Oil prices remain volatile and have exceeded $100 per barrel twice in five years. At these prices, the U.S. spends $1 billion per day on imported oil to meet our energy demands. Because the transportation sector accounts for two-thirds of our petroleum use, energy security is deeply entangled with our transportation needs. At the same time, transportation produces one-quarter of the nation’s carbon dioxide output. Increasing the efficiency of internal combustion engines is a technologically proven and cost-effective approach to dramatically improving the fuel economy of the nation’s fleet of vehicles in the near- to mid-term, with the corresponding benefits of reducing our dependence on foreign oil and reducing carbon emissions. Because of their relatively low cost, high performance, and ability to utilize renewable fuels, internal combustion engines—including those in hybrid vehicles—will continue to be critical to our transportation infrastructure for decades. Achievable advances in engine technology can improve the fuel economy of automobiles by over 50% and trucks by over 30%. Achieving these goals will require the transportation sector to compress its product development cycle for cleaner, more efficient engine technologies by 50% while simultaneously exploring innovative design space. Concurrently, fuels will also be evolving, adding another layer of complexity and further highlighting the need for efficient product development cycles. Current design processes, using “build and test” prototype engineering, will not suffice. Current market penetration of new engine technologies is simply too slow—it must be dramatically accelerated. These challenges present a unique opportunity to marshal U.S. leadership in science-based simulation to develop predictive computational design tools for use by the transportation industry. The use of predictive simulation tools for enhancing combustion engine performance will shrink engine development timescales, accelerate time to market, and reduce development costs, while ensuring the timely achievement of energy security and emissions targets and enhancing U.S. industrial competitiveness. In 2007 Cummins achieved a milestone in engine design by bringing a diesel engine to market solely with computer modeling and analysis tools. The only testing was after the fact to confirm performance. Cummins achieved a reduction in development time and cost. As important, they realized a more robust design, improved fuel economy, and met all environmental and customer constraints. This important first step demonstrates the potential for computational engine design. But, the daunting complexity of engine combustion and the revolutionary increases in efficiency needed require the development of simulation codes and computation platforms far more advanced than those available today. Based on these needs, a Workshop to Identify Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE) convened over 60 U.S. leaders in the engine combustion field from industry, academia, and national laboratories to focus on two critical areas of advanced simulation, as identified by the U.S. automotive and engine industries. First, modern engines require precise control of the injection of a broad variety of fuels that is far more subtle than achievable to date and that can be obtained only through predictive modeling and simulation. Second, the simulation, understanding, and control of these stochastic in-cylinder combustion processes lie on the critical path to realizing more efficient engines with greater power density. Fuel sprays set the initial conditions for combustion in essentially all future transportation engines; yet today designers primarily use empirical methods that limit the efficiency achievable. Three primary spray topics were identified as focus areas in the workshop: The fuel delivery system, which includes fuel manifolds and internal injector flow, The multi-phase fuel–air mixing in the combustion chamber of the engine, and The heat transfer and fluid interactions with cylinder walls. Current understanding and modeling capability of stochastic processes in engines remains limited and prevents designers from achieving significantly higher fuel economy. To improve this situation, the workshop participants identified three focus areas for stochastic processes: Improve fundamental understanding that will help to establish and characterize the physical causes of stochastic events, Develop physics-based simulation models that are accurate and sensitive enough to capture performance-limiting variability, and Quantify and manage uncertainty in model parameters and boundary conditions. Improved models and understanding in these areas will allow designers to develop engines with reduced design margins and that operate reliably in more efficient regimes. All of these areas require improved basic understanding, high-fidelity model development, and rigorous model validation. These advances will greatly reduce the uncertainties in current models and improve understanding of sprays and fuel–air mixture preparation that limit the investigation and development of advanced combustion technologies. The two strategic focus areas have distinctive characteristics but are inherently coupled. Coordinated activities in basic experiments, fundamental simulations, and engineering-level model development and validation can be used to successfully address all of the topics identified in the PreSICE workshop. The outcome will be: New and deeper understanding of the relevant fundamental physical and chemical processes in advanced combustion technologies, Implementation of this understanding into models and simulation tools appropriate for both exploration and design, and Sufficient validation with uncertainty quantification to provide confidence in the simulation results. These outcomes will provide the design tools for industry to reduce development time by up to 30% and improve engine efficiencies by 30% to 50%. The improved efficiencies applied to the national mix of transportation applications have the potential to save over 5 million barrels of oil per day, a current cost savings of $500 million per day.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Differentiable hybrid neural network approach for enhancing reactor dynamics simulations

Reactor dynamics simulations provide essential insights into the time-dependent behavior of nuclear reactors under various operating conditions. However, high-fidelity simulations can be computationally intensive, requiring significant computational resources. Here, to address this challenge, this study employs a differentiable hybrid model that utilizes neural networks as a corrector to enhance the performance of a low-fidelity simulation, aligning its predictions with those of a high-fidelity simulation. Low-fidelity and high-fidelity simulations were obtained by adjusting the mesh size in the System Dynamics Analysis Tool. The differentiable hybrid model was trained in two approaches: time-step-wise and sequence-wise. It was then applied to simulate various transients in a molten salt reactor. Its performance was evaluated by comparing its responses to transients against those of the high-fidelity simulation. An additional approach was performed using a data-driven model to correct the low-fidelity simulation. In comparison, the differentiable hybrid model showed significant improvements in transient prediction, effectively addressing the limitations of the low-fidelity simulations. The results highlighted the robustness of the differentiable hybrid model in both training approaches. It delivered simulations that were at least 3.8 times faster than high-fidelity models. In the time-step-wise approach, it achieved at least a 39% improvement in accuracy. In the sequence-wise approach, it showed at least an 81% accuracy improvement over the full transient. This approach offers a promising path for improving computational efficiency without compromising accuracy in nuclear reactor simulations, making it suitable for real-time digital twin applications.

42 - ENGINEERING↗
DOE OSTI2012

Multi-physics nuclear reactor simulator for advanced nuclear engineering education

Multi-physics nuclear reactor simulator, which aims to utilize for advanced nuclear engineering education, is being introduced to Nagoya Univ.. The simulator consists of the 'macroscopic' physics simulator and the 'microscopic' physics simulator. The former performs real time simulation of a whole nuclear power plant. The latter is responsible to more detail numerical simulations based on the sophisticated and precise numerical models, while taking into account the plant conditions obtained in the macroscopic physics simulator. Steady-state and kinetics core analyses, fuel mechanical analysis, fluid dynamics analysis, and sub-channel analysis can be carried out in the microscopic physics simulator. Simulation calculations are carried out through dedicated graphical user interface and the simulation results, i.e., spatial and temporal behaviors of major plant parameters are graphically shown. The simulator will provide a bridge between the 'theories' studied with textbooks and the 'physical behaviors' of actual nuclear power plants. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

Pebble-bed pebble motion: Simulation and Applications

Pebble bed reactors (PBR) have moving graphite fuel pebbles. This unique feature provides advantages, but also means that simulation of the reactor requires understanding the typical motion and location of the granular flow of pebbles. This report presents a method for simulation of motion of the pebbles in a PBR. A new mechanical motion simulator, PEBBLES, efficiently simulates the key elements of motion of the pebbles in a PBR. This model simulates gravitational force and contact forces including kinetic and true static friction. It's used for a variety of tasks including simulation of the effect of earthquakes on a PBR, calculation of packing fractions, Dancoff factors, pebble wear and the pebble force on the walls. The simulator includes a new differential static friction model for the varied geometries of PBRs. A new static friction benchmark was devised via analytically solving the mechanics equations to determine the minimum pebble-to-pebble friction and pebble-to-surface friction for a five pebble pyramid. This pyramid check as well as a comparison to the Janssen formula was used to test the new static friction equations. Because larger pebble bed simulations involve hundreds of thousands of pebbles and long periods of time, the PEBBLES code has been parallelized. PEBBLES runs on shared memory architectures and distributed memory architectures. For the shared memory architecture, the code uses a new O(n) lock-less parallel collision detection algorithm to determine which pebbles are likely to be in contact. The new collision detection algorithm improves on the traditional non-parallel O(n log(n)) collision detection algorithm. These features combine to form a fast parallel pebble motion simulation. The PEBBLES code provides new capabilities for understanding and optimizing PBRs. The PEBBLES code has provided the pebble motion data required to calculate the motion of pebbles during a simulated earthquake. The PEBBLES code provides the ability to determine the contact forces and the lengths of motion in contact. This information combined with the proper wear coefficients can be used to determine the dust production from mechanical wear. These new capabilities enhance the understanding of PBRs, and the capabilities of the code will allow future improvements in understanding.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Low-mass molecular dynamics simulation: A simple and generic technique to enhance configurational sampling

Highlights: • Reducing atomic masses by 10-fold vastly improves sampling in MD simulations. • CLN025 folded in 4 of 10 × 0.5-μs MD simulations when masses were reduced by 10-fold. • CLN025 folded as early as 96.2 ns in 1 of the 4 simulations that captured folding. • CLN025 did not fold in 10 × 0.5-μs MD simulations when standard masses were used. • Low-mass MD simulation is a simple and generic sampling enhancement technique. - Abstract: CLN025 is one of the smallest fast-folding proteins. Until now it has not been reported that CLN025 can autonomously fold to its native conformation in a classical, all-atom, and isothermal–isobaric molecular dynamics (MD) simulation. This article reports the autonomous and repeated folding of CLN025 from a fully extended backbone conformation to its native conformation in explicit solvent in multiple 500-ns MD simulations at 277 K and 1 atm with the first folding event occurring as early as 66.1 ns. These simulations were accomplished by using AMBER forcefield derivatives with atomic masses reduced by 10-fold on Apple Mac Pros. By contrast, no folding event was observed when the simulations were repeated using the original AMBER forcefields of FF12SB and FF14SB. The results demonstrate that low-mass MD simulation is a simple and generic technique to enhance configurational sampling. This technique may propel autonomous folding of a wide range of miniature proteins in classical, all-atom, and isothermal–isobaric MD simulations performed on commodity computers—an important step forward in quantitative biology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Quasi-brittle porous material: Simulated effect of stochastic air void structure on compressive strength

Highlights: • A simulation procedure to study the effect of porosity on the compressive strength of a quasi-brittle material is introduced • Based on the simulated results, the relationship between compressive strength and air void fraction (porosity) is presented. • A linear relationship was achieved between the cubic root of porosity and the simulated compressive strength. The effect of porosity comprised of spherical air voids on the compressive strength of quasi-brittle material was studied via simulations. The simulated porous structures were based on pore size distributions of two mortar samples measured by X-ray microtomography. While the simulation method set practical limits on the size of sample, the base of the statistics was established by simulating 128 small structures generated by sampling from pore structures of two mortars. By studying the application of the classical strength-porosity formulas to the simulated data, a new simple model was formed. A linear relationship was achieved between the cubic root of air void fraction (porosity) and the simulated compressive strength. The reasons for scattering of simulated strength around fitted trend remained unresolved in this study; no clear dependence on pore number or other distribution properties was observed. With the presented simulation approach, the dependence of compressive strength on porosity is achieved independently of disturbances that occur in experimental studies creating understanding of compressional behavior of low porosity materials.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Is the Calcite-Water Interface Understood? Direct Comparisons of Molecular Dynamics Simulations with Specular X-ray Reflectivity Data

New insights into the structure of the calcite-water interface are obtained through direct model-independent comparison of multiple classical molecular dynamics (MD) simulations with high-resolution specular X-ray reflectivity (XR) data. This set of comparisons, with four different state-of-the-art force fields (including two non-polarizable, one polarizable, and one reactive force field), reveal new insights into the absolute accuracy of the simulated structures and the uniqueness of the XR-derived structural results. These four simulations, while qualitatively similar, have visibly distinct interfacial structure, and are distinguished through a quantitative comparison of the XR signals calculated from these simulations with experimental XR data. The results demonstrate that the simulated calcite-water interface structures, as a whole, are not consistent with the XR data (i.e., within their precision and accuracy). This disagreement is largely due to the simulation of the calcite lattice. The simulated interfacial water profiles show substantially different levels of agreement with the XR data. Of these, the rigid-ion model (RIM) simulations show the best consistency with the experimental XR data. Further model-dependent comparisons of the structural parameters that describe the interfacial structure (derived from both the MD simulations and the XR data) provide further insight into the sources of differences between these two approaches. Using the new insights from the RIM simulations, new structures of the calcite-water interface consistent with both the experimental data and the simulation are identified and compared to recent results.

Calcite-water interface↗
DOE OSTI2021

Transmission-and-Distribution Dynamic Co-simulation Framework for Distributed Energy Resource Frequency Response

The rapid deployment of distributed energy resources (DERs) in distribution networks has made it challenging to balance the transmission system and stabilize frequency. DERs have the ability to provide frequency regulation services; however, existing frequency dynamic simulation tools - which were developed mainly for the transmission system - lack the capability to simulate distribution network dynamics with high penetrations of DERs. Although electromagnetic transient simulation tools can simulate distribution network dynamics, the computation efficiency limits their use for large-scale transmission-and-distribution (T&D) co-simulation. This paper presents an efficient open-source T&D dynamic co-simulation framework for DER frequency response based on the HELICS platform and off-the-shelf T&D simulators. The challenge of synchronizing the simulation time between the transmission network and the DERs in the distribution network is solved through the detailed modeling of DERs in frequency dynamic models while DER power flow models are also preserved in the distribution networks, thereby respecting local voltage constraints when dispatching DER power for frequency response. DER frequency response (primary and secondary) is simulated in case studies to validate the proposed framework. Last, the accuracy of the proposed co-simulation model is benchmarked, and a large T&D system simulation (2k transmission and 1M distribution nodes) is presented to demonstrate the efficiency and effectiveness of the overall framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Westinghouse waste simulation and optimization software tool

Applications for dynamic simulation can be found in virtually all areas of process engineering. The tangible benefits of using dynamic simulation can be seen in tighter design, smoother start-ups and optimized operation. Thus, proper implementation of dynamic simulation can deliver substantial benefits. These benefits are typically derived from improved process understanding. Simulation gives confidence in evidence based decisions and enables users to try out lots of 'what if' scenarios until one is sure that a decision is the right one. In radioactive waste treatment tasks different kinds of waste with different volumes and properties have to be treated, e.g. from NPP operation or D and D activities. Finding a commercially and technically optimized waste treatment concept is a time consuming and difficult task. The Westinghouse Waste Simulation and Optimization Software Tool will enable the user to quickly generate reliable simulation models of various process applications based on equipment modules. These modules can be built with ease and be integrated into the simulation model. This capability ensures that this tool is applicable to typical waste treatment tasks. The identified waste streams and the selected treatment methods are the basis of the simulation and optimization software. After implementing suitable equipment data into the model, process requirements and waste treatment data are fed into the simulation to finally generate primary simulation results. A sensitivity analysis of automated optimization features of the software generates the lowest possible lifecycle cost for the simulated waste stream. In combination with proven waste management equipments and integrated waste management solutions, this tool provides reliable qualitative results that lead to an effective planning and minimizes the total project planning risk of any waste management activity. It is thus the ideal tool for designing a waste treatment facility in an optimum manner, taking account of the detailed waste stream and specific requirements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗