Qualitative simulation of complex dynamic systems: An application to a marine powerplant under supervisory control
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Govindaraj, T.↗
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A Method for Flow Simulation About Complex Geometries Using Both Structured and Unstructured Grids
A computational fluid dynamics code which utilizes both structured and unstructured grids was developed. The objective of this study was to develop and demonstrate the ability of such a code to achieve solutions about complex geometries in two dimensions. An unstructured grid generator and flow solver were incorporated into the PARC2D structured flow solver. This new unstructured grid generator capability allows for easier generation and manipulation of complex grids. Several examples of the grid generation capabilities are provided. The coupling of different grid topologies and the manipulation of individual grids is shown. Also, grids for realistic geometries, a NACA 0012 airfoil and a wing/nacelle installation, were created. The flow over a NACA 0012 airfoil was used as a test case for the flow solver. Eight separate cases were run. They were both the inviscid and viscous solutions for two freestream Mach numbers and airfoil angle of attacks of 0 to 3.86 degrees. The Mach numbers chosen were for a subsonic case, Mach 0.6, and a case where supersonic regions and a shock wave exists, Mach 0.8. These test case conditions were selected to match experimentally obtained data for code comparison. The results show that the code accurately predicts the flow field for all cases.
Debonis, James R.↗
A Fast Algorithm for Massively Parallel, Long-Term, Simulation of Complex Molecular Dynamics Systems
The advances in theory and computing technology over the last decade have led to enormous progress in applying atomistic molecular dynamics (MD) methods to the characterization, prediction, and design of chemical, biological, and material systems,.
Algorithm Molecular Dynamics Systems Parallel Plat↗
A Framework for Parallel Unstructured Grid Generation for Complex Aerodynamic Simulations
A framework for parallel unstructured grid generation targeting both shared memory multi-processors and distributed memory architectures is presented. The two fundamental building-blocks of the framework consist of: (1) the Advancing-Partition (AP) method used for domain decomposition and (2) the Advancing Front (AF) method used for mesh generation. Starting from the surface mesh of the computational domain, the AP method is applied recursively to generate a set of sub-domains. Next, the sub-domains are meshed in parallel using the AF method. The recursive nature of domain decomposition naturally maps to a divide-and-conquer algorithm which exhibits inherent parallelism. For the parallel implementation, the Master/Worker pattern is employed to dynamically balance the varying workloads of each task on the set of available CPUs. Performance results by this approach are presented and discussed in detail as well as future work and improvements.
Zagaris, George↗
Atomistic Simulations of Complex DNA DSBs and the Interactions with Ku70/80 Heterodimer
Compared to DNA with simple DSBs, the complex lesions can enhance the hydrogen bonds opening rate at the DNA terminus, and increase the mobility of the whole duplex. Binding of Ku drastically reduces the structural disruption and flexibility caused by the complex lesions. In all complex DSBs systems, the binding of DSB terminus with Ku70 is softened while the binding of the middle duplex with Ku80 is tightened. Binding of Ku promotes the rigidity of DNA duplexes, due to the clamp structure of the inner surface of the rings of Ku70/80.
Hu, Shaowen↗
Increased Complexity in Simulating Measurement Systems
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Honnold, Philip↗
SimTool - An object based approach to simulation construction
The creation and maintenance of large complex simulations can be a difficult and error prone task. A number of interactive and automated tools have been developed to aid in simulation construction and maintenance. Many of these tools are based upon object oriented analysis and design concepts. One such tool, SimTool, is an object based integrated tool set for the development, maintenance, and operation of large, complex and long lived simulations. This paper discusses SimTool's object based approach to simulation design, construction and execution. It also discusses the services provided to various levels of SimTool users to assist them in a wide range of simulation tasks. Also, with the aid of an implemented and working simulation example, this paper discusses SimTool's key design and operational features. Finally, this paper presents a condensed discussion of SimTool's Entity-Relationship-Attribute (ERA) modeling approach.
Crues, Edwin Z.↗
A Fly-Through Mission Strategy Targeting Peptide as a Signature of Chemical Evolution and Possible Life in Enceladus Plumes
In situ detection of organic molecules in the extraterrestrial environment provides a key step towards better understanding the variety and the distribution of building blocks of life and it may ultimately lead to finding extraterrestrial life within the Solar System. Here we present combined results of two separate experiments that enable us to realize such in situ life signature detection from the deep habitats of the "Ocean World": a hydrothermal reactor experiment simulating complex organic synthesis and a simulated fly-through capture experiment of organic-bearing microparticles using silica aerogels, followed by subsequent analysis. Both experiments employ peptide as a plausible organics existing in Encleadus plume particles produced in its subsurface ocean. Recent laboratory hydrothermal experiments and a theoretical model on silica saturation indicated an on going hydrothermal reactions in subsurface Enceladus ocean. Given the porous chondritic origin of the core, it is likely that organic compounds originated by radiation chemistry such as amino acid precursors could have been provided, leached, and altered through widespread water-rock interactions. By using the same laboratory experimental setup from the latest water-rock interaction study, we performed amino acid polymerization experiments for 144 days and monitored the organic complexity changing over time. So far over 3,000 peaks up to the size of greater than 600 MW were observed through the analysis of capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS) with an indication of amino acid derivatives and short peptides. Generally abiotic polymerization of enantiomeric amino acids results in forming stereoisomeric peptides with identical molecular weight and formula as opposed to homochiral biopolymers. Assuming Enceladus plume particles may contain a mixture of stereoisomeric peptides, we were able to distinguish 16 of the 17 stereoisomeric tripeptides as a test sample using capillary electrophoresis (CE) under optimized conditions. We further conducted Enceladus plume fly-through capture experiment by accelerating peptides soaked in rock particles up to a speed of 5.7 km/s and capturing with originally developed hydrophobic silica aerogels. Direct peptide extraction with acetonitrile-water followed by CE analysis led to detection of only but two stereoisomeric acidic peptide peaks, presenting the first run-through hypervelocuty impact sample analysis targeting peptides as key molecule to to understand the ongoing astrobiology on Enceladus.
organic molecules↗
The Effect of Temperature on the Preservation of Volatile-Rich Lunar Samples
Introduction. The Moon’s south pole is a high-priority target for human exploration and scientific study. This interest is, in part, due to the presence of Permanently Shadowed Regions (PSRs), which could contain high concentrations of unique volatiles at cryogenic temperatures [1]. Returned samples from PSRs may include a unique combination of rocks, regolith, and volatile species, providing unprecedented insights into the history of the Solar System and the potential for resource utilization on the Moon. However, because PSR samples are cryogenic up-on collection, lunar polar sample return will eventually require cold stowage for the journey from the Moon to Earth. Without cold stowage, PSR sample return will likely result in phase changes and chemical reactions within the volatile component of the sample, which would negatively impact the resulting scientific studies of those samples. This abstract summarizes the initial results from an ongoing characterization of analog PSR samples at a range of temperatures, with the goal of defining the temperatures needed for a flight cold stowage freezer. Background. Based on remote sensing observations of the Moon [2], south polar PSRs range in temperature from ~120K for small and/or shallow PSRs to ~20K at the most extreme locations in large, deep PSRs. A range of volatiles have been hypothesized to exist at the surface or subsurface of the lunar poles [3-5 and others]. This hypothesis was verified when the LCROSS mission impacted the <50-K PSR in the crater Cabeus, detecting a range of volatiles from water to low condensation temperature species such as H2S and methane [6]. Species such as H2S and ammonia (also detected by LCROSS) are also highly reactive, and increase the likelihood of chemical reactions at elevated (non-cryogenic) temperatures. At the Johnson Space Center’s Planetary Exploration and Astromaterials Research Laboratory (JSC-PEARL), we have developed a volatile-bearing lunar simulant that incorporates several of the species detected by LCROSS [Table 1] mixed cryogenically with the USGS Lunar Highlands Type (LHT) regolith simulant. The new volatile-regolith simulant will be used to assess the degree of sample alteration at room temperature, -20°C, -80°C, and -196°C (liquid nitro-gen), over a two-week period. Room temperature samples represent those likely to be returned during initial missions without cold stowage, -20°C provides an analog to Apollo cold curated samples, -80°C is the temperature of multiple flight payload freezers (e.g., MELFI), and -196°C is analogous to lunar PSRs. Two weeks is an approximation of the time between sample collection and Earth return for initial Artemis missions. Over this period of time, sample head-space gases will be analyzed using a Universal Gas Analyzer (UGA, a type of mass spectrometer) coupled with a Baratron pressure sensor. After testing, the regolith component of the simulant will be purged of volatiles and preserved for future electron beam and/or FTIR analysis. Experimental Procedure. Volatile-regolith simulants will be produced as an initial homogenous batch; this batch will then be distributed into aliquots (gas chromatography/GC vials or cryo vials), ensuring that each sample has the same starting composition and conditions [Fig. 1]. In addition to the “full” simulant shown in Table 1, less complex simulant compositions will be used as baseline and control samples [Table 2]. Aliquots will be produced in triplicate for each simulant composition, storage temperature, and date of sampling. Headspace gases in all Day 0 samples will be analyzed by the UGA immediately. Cold storage samples for future analytical days will be placed in freezers appropriate to their target temperatures (-20°C, -80°C, -196°C). For ambient-temperature samples, regolith and regolith-water samples will be stored in a fume hood, while the full simulant will be stored in a sealed Parr vessel for safety; no other simulants (RWCM/ RWCM+) will be stored at ambient temperature for this test. Samples will be analyzed by UGA in this manner on each Analysis Day outlined in Table 2. Analytical Data. The UGA measures the partial pressures in a single sample aliquot over a set mass range of 0-105 atomic mass units (AMU) [Figure 2]; this set range was selected to slightly exceed the mass of the highest-mass expected reaction product (H2SO4). The Baratron complements the UGA by measuring the total pressure in the headspace of a sample vial. Coupled together, the quantitative abundances of gases will be monitored throughout the test. UGA analyses of the triplicate samples for each storage temperature, day, and simulant composition will be averaged, and standard deviations for each will be calculated. The compositions of starting species (shown in Table 1) will be characterized as a function of time, and the presence of any new compounds (reaction products) will be monitored as well. Total pressures will be recorded for each sample analysis, and any samples that show signs of leakage (e.g., a significant reduction in pressure or simulant volatiles) will be discarded. Anticipated Results. Testing is planned to begin in January 2022. The resulting data will allow compositional and phase changes in the volatile component of the simulants to be determined. Both the reduction in initial compounds and the addition of reaction products are expected to be observed. In addition, the relative efficacy of the different temperatures at pre-serving the initial composition of the simulants will be quantified. Finally, the regolith component of each sample will be argon-purged and stored in a controlled environment for future laboratory analysis. Compositional and morphological changes in the regolith are expected for samples above 0°C. This test will be repeated three times over the course of 2022. Understanding the effect of temperature on both the volatile and regolith components of analog lunar materials will allow requirements for a cold stowage freezer to be developed. The implementation of cold stowage for lunar polar missions will maximize the preservation of returned samples, enabling ground-breaking lunar and Solar System volatiles science for decades to come.
J L Mitchell↗
Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects
Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.
climate↗
Propulsion Simulations with the Unstructured-Grid CFD Tool TetrUSS
A computational investigation has been completed to assess the capability of the NASA Tetrahedral Unstructured Software System (TetrUSS) for simulation of exhaust nozzle flows. Three configurations were chosen for this study: (1) a fluidic jet effects model, (2) an isolated nacelle with a supersonic cruise nozzle, and (3) a fluidic pitchthrust- vectoring nozzle. These configurations were chosen because existing data provided a means for measuring the ability of the TetrUSS flow solver USM3D for simulating complex nozzle flows. Fluidic jet effects model simulations were compared with structured-grid CFD (computational fluid dynamics) data at Mach numbers from 0.3 to 1.2 at nozzle pressure ratios up to 7.2. Simulations of an isolated nacelle with a supersonic cruise nozzle were compared with wind tunnel experimental data and structured-grid CFD data at Mach numbers of 0.9 and 1.2, with a nozzle pressure ratio of 5. Fluidic pitch-thrust-vectoring nozzle simulations were compared with static experimental data and structured-grid CFD data at static freestream conditions and nozzle pressure ratios from 3 to 10. A fluidic injection case was computed with the third configuration at a nozzle pressure ratio of 4.6 and a secondary pressure ratio of 0.7. Results indicate that USM3D with the S-A turbulence model provides accurate exhaust nozzle simulations at on-design conditions, but does not predict internal shock location at overexpanded conditions or pressure recovery along a boattail at transonic conditions.
Deere, Karen A.↗
Propulsion Simulations Using Advanced Turbulence Models with the Unstructured Grid CFD Tool, TetrUSS
A computational investigation has been completed to assess the capability of TetrUSS for exhaust nozzle flows. Three configurations were chosen for this study (1) an axisymmetric supersonic jet, (2) a transonic axisymmetric boattail with solid sting operated at different Reynolds number and Mach number, and (3) an isolated non-axisymmetric nacelle with a supersonic cruise nozzle. These configurations were chosen because existing experimental data provided a means for measuring the ability of TetrUSS for simulating complex nozzle flows. The main objective of this paper is to validate the implementation of advanced two-equation turbulence models in the unstructured-grid CFD code USM3D for propulsion flow cases. USM3D is the flow solver of the TetrUSS system. Three different turbulence models, namely, Menter Shear Stress Transport (SST), basic k epsilon, and the Spalart-Allmaras (SA) are used in the present study. The results are generally in agreement with other implementations of these models in structured-grid CFD codes. Results indicate that USM3D provides accurate simulations for complex aerodynamic configurations with propulsion integration.
Abdol-Hamid, Khaled S.↗
The need for enhanced initial moisture information in simulations of a complex summertime precipitation event
Initial simulations of the June 28, 1986 Cooperative Huntsville Meteorological Experiment case illustrate the need for mesoscale moisture information in a summertime situation in which deep convection is organized by weak large scale forcing. A methodology is presented for enhancing the initial moisture field from a combination of IR satellite imagery, surface-based cloud observations, and manually digitized radar data. The Mesoscale Atmospheric Simulation Model is utilized to simulate the events of June 28-29. This procedure insures that areas known to have precipitation at the time of initialization will be nearly saturated on the grid scale, which should decrease the time needed by the model to produce the observed Bonnie (a relatively weak hurricane that moved on shore two days before) convection. This method will also result in an initial distribution of model cloudiness (transmissivity) that is very similar to that of the IR satellite image.
Waight, Kenneth T., III↗
Simulating Operation of a Complex Sensor Network
Simulation Tool for ASCTA Microsensor Network Architecture (STAMiNA) ["ASCTA" denotes the Advanced Sensors Collaborative Technology Alliance.] is a computer program for evaluating conceptual sensor networks deployed over terrain to provide military situational awareness. This or a similar program is needed because of the complexity of interactions among such diverse phenomena as sensing and communication portions of a network, deployment of sensor nodes, effects of terrain, data-fusion algorithms, and threat characteristics. STAMiNA is built upon a commercial network-simulator engine, with extensions to include both sensing and communication models in a discrete-event simulation environment. Users can define (1) a mission environment, including terrain features; (2) objects to be sensed; (3) placements and modalities of sensors, abilities of sensors to sense objects of various types, and sensor false alarm rates; (4) trajectories of threatening objects; (5) means of dissemination and fusion of data; and (6) various network configurations. By use of STAMiNA, one can simulate detection of targets through sensing, dissemination of information by various wireless communication subsystems under various scenarios, and fusion of information, incorporating such metrics as target-detection probabilities, false-alarm rates, and communication loads, and capturing effects of terrain and threat.
Jennings, Esther↗
Bridging the gap between experiments and simulations using machine learning
The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Quantum Electrodynamics Coupled-Cluster at Scale: High-Performance Implementation for Complex Systems
Coupled-cluster theory (CC) is a highly accurate and versatile method for simulating complex interactions within quantum systems. The extension of CC theory to model mixed electron-photon processes with quantum electrodynamics (QED) has improved our capability to predict cavity-modified chemistry, a field where photons are used as cost-effective and eco-friendly alternatives to catalyze/inhibit chemical reactions. However, calculations with CC methods, even without incorporating QED effects, are often prohibitively expensive. Simulations of larger systems require scalable infrastructures that exist for traditional CC methods but not for QED-CC methods. As such, we present a GPU-enabled, high-performance, open-source implementation of the quantum electrodynamics coupled-cluster method with single and double excitations (QED-CCSD) within the ExaChem quantum chemistry software package. ExaChem relies on the Tensor Algebra for Many-body Methods (TAMM) infrastructure: a parallel heterogeneous tensor library designed to achieve scalable performance on modern heterogeneous supercomputing platforms. Furthermore, we discuss theoretical foundations, algorithmic details, and numerical benchmarks to showcase the larger systems that ExaChem can simulate and how the integration of photonic degrees-of-freedom alters their ground-state properties.
Basis sets↗
Evaluation of NASA's High-Resolution Global Composition Simulations: Understanding a Pollution Event in the Chesapeake Bay During the Summer 2017 OWLETS Campaign
Recirculation of pollutants due to a bay breeze effect is a key meteorological mechanism impacting air quality near urban coastal areas, but regional and global chemical transport models have historically struggled to capture this phenomenon. We present a case study of a high ozone (O3) episode observed over the Chesapeake Bay during the NASA Ozone Water-Land Environmental Transition Study (OWLETS) in summer 2017. OWLETS included a complementary suite of ground-based and airborne observations, with which we characterize the meteorological and chemical context of this event and develop a framework to evaluate model performance. Two publicly-available NASA global high-resolution coupled chemistry-meteorology models (CCMMs) are investigated: GEOS-CF and MERRA2-GMI. The GEOS-CF R squared value for comparisons between the NASA Sherpa C-23 aircraft measurements to the GEOS-CF resulted in good agreement (R squared: 0.67) on July 19th and fair agreement (R squared: 0.55) for July 20th. Compared to surface observations, we find the GEOS-CF product with a 25 x 25 km squared grid box, at an hourly (R squared: 0.62 to 0.87) and 15-minute (R squared: 0.64 to 0.87) interval for six regional sites outperforms the hourly nominally 50 x 50 km squared gridded MERRA2-GMI (R squared: 0.53 to 0.76) for four of the six sites, suggesting it is better capable of simulating complex chemical and meteorological features associated with ozone transport within the Chesapeake Bay airshed. When the GEOS-CF product was compared to the TOLNet LiDAR observations at both NASA Langley Research Center (LaRC) and the Chesapeake Bay Bridge Tunnel (CBBT), the median differences at LaRC were -6 to 8% and at CBBT were ± 7% between 400 to 2000 m ASL. This indicates that, for this case study, the GEOS-CF is able to simulate surface level ozone diurnal cycles and vertical ozone profiles at small scales between the surface level and 2000 m ASL. Evaluating global chemical model simulations at sub-regional scales will help air quality scientists understand the complex processes occurring at small spatial and temporal scales within complex surface terrain changes, simulating nighttime chemistry and deposition, and the potential to use global chemical transport simulations in support of regional and sub-regional field campaigns.
NASA Ozone Water-Land Environmental Transition Stu↗
A full-scope, high-fidelity simulator-based hardware-in-the-loop testbed for comprehensive nuclear power plant cybersecurity research
Nuclear power plant (NPP) cybersecurity research often relies on hardware-in-the-loop (HIL) testbeds that integrate real hardware components into simulated environments. These testbeds allow researchers to identify vulnerabilities, evaluate attack impacts, and test security measures in a controlled setting. Furthermore, previous HIL testbeds lacked fidelity to accurately represent real nuclear systems, limiting the scope of cybersecurity analysis. This study presents the creation of a HIL testbed, devised upon a full-scope, high-fidelity NPP simulator, to facilitate realistic and comprehensive cybersecurity research. To demonstrate its capabilities, the control logic for the steam generator water level was migrated from the simulator to an external programmable logic controller. As a practical application of the developed testbed, supply chain attack scenarios were simulated by injecting malicious code into the controller logic, and the effects of manipulating sensor inputs and control commands were observed. While this HIL testbed provides more detailed simulations, enhanced realism, and wider applicability compared to other options utilizing a less complex simulator, it is also more intricate and costly. For this reason, we include a detailed comparison with some alternative architectures to aid fellow researchers and practitioners in the selection of a suitable HIL architecture based on specific research objectives.
47 OTHER INSTRUMENTATION↗