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A series of coatings was developed for the space shuttle type silica fiber insulation system and characterized for optical and physical properties. Reentry simulation tests were run using a radiant panel and also using a hypersonic plasma arc. The coatings produced had improved physical and optical properties as well as greater reuse capability over the GE version of the JSC-0042 coating.
Simulation of complex physical systems behavior in launch vehicle system optimization program - digital computer program
A multiphase computational fluid dynamics code (ARICC-3D) is presented and results of two simulations are discussed. The numerical framework of the CFD code is reviewed as well as some of the two-phase physical submodels. The simulations performed include a single coaxial element injector and a multielement injector using LOX/Hydrogen reactants. The single element injector simulation verified the interaction among the code's submodels. The multielement injector simulation transient results include the chamber response to a transverse pressure wave with and without a chamber baffle. The results of these simulations demonstrate the current capabilities and their limitations to model complex two-phase combustion phenomena. Possible ways to exceed these limitations are suggested.
A preliminary design study is presented for a non-nuclear test facility which uses ohmic heating to replicate the thermal hydraulic characteristics of solid core nuclear reactor fuel element passages. The basis for this testing capability is a recently commissioned nuclear thermal rocket environments simulator, which uses a high-power, multi-gas, wall-stabilized constricted arc-heater to produce high-temperature pressurized hydrogen flows representative of reactor core environments, excepting radiation effects. Initially, the baseline test fixture for this non-nuclear environments simulator was configured for long duration hot hydrogen exposure of small cylindrical material specimens as a low cost means of evaluating material compatibility. It became evident, however, that additional functionality enhancements were needed to permit a critical examination of thermal hydraulic effects in fuel element passages. Thus, a design configuration was conceived whereby a short tubular material specimen, representing a fuel element passage segment, is surrounded by a backside resistive tungsten heater element and mounted within a self-contained module that inserts directly into the baseline test fixture assembly. With this configuration, it becomes possible to create an inward directed radial thermal gradient within the tubular material specimen such that the wall-to-gas heat flux characteristics of a typical fuel element passage are effectively simulated. The results of a preliminary engineering study for this innovative concept are fully summarized, including high-fidelity multi-physics thermal hydraulic simulations and detailed design features.
In a proof-of-principle effort to demonstrate the feasibility of magnetically shielded (MS) Hall thrusters, an existing laboratory thruster has been modified with the guidance of physics-based numerical simulation. When operated at a discharge power of 6-kilowatts the modified thruster has been designed to reduce the total energy and flux of ions to the channel insulators by greater than 1 and greater than 3 orders of magnitude, respectively. The erosion rates in this MS thruster configuration are predicted to be at least 2-4 orders of magnitude lower than those in the baseline (BL) configuration. At such rates no detectable erosion is expected to occur.
Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.
This simulation study evaluates the response of a 21-cm radiometer, measuring the radiation emitted by a bare soil, to varying accumulations of rain. It is shown that correlations between the decrease in emissivity after a rain storm and the total accumulation depend strongly on the physical characteristics of the soil which affect its capacity to hold water. These are primarily soil texture and pre-rain soil moisture. A method is also discussed which would use the numerical models with remotely sensed microwave brightness and surface physical temperatures, along with conventional weather data, to estimate the total accumulation.
Here, this study investigates the reaction of hydrogen (H 2 ) with pyrite (FeS 2 ), focusing on how temperature and the presence of water influence the reaction pathways and kinetics. Utilizing computational molecular simulations and kinetic analyses, we explore the impact of these factors on the formation of hydrogen sulfide (H 2 S) and related species. First, grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations reveal that physical H 2 adsorption occurs in distinct layers on the pyrite surface. In addition, increased temperatures reduce the absolute adsorption capacities. Reactive MD simulations demonstrate that H 2 interacts differently with pyrite under varying conditions. At 298 K, H 2 reacts with pyrite to form HS – , leading to the formation of HS – through covalent bonding with sulfur of pyrite. However, no H 2 S is produced at this temperature, suggesting that a kinetic barrier (i.e., activation energy) may prevent this reaction. At higher temperatures, H 2 S production significantly increases. The presence of water introduces additional complexity to the reaction mechanism. Unlike dry conditions, water enhances H 2 S generation, even at low temperatures. Water also facilitates the formation of additional products, such as SOH, indicating a more intricate chemical environment on the pyrite surface. Our findings identify the association of HS – ions to form H 2 S as the rate-limiting step, with temperature influencing this process. This finding suggests that while the presence of water can create a more dynamic reaction environment, the overall mechanisms leading to H 2 S formation remain consistent. These outcomes suggest the need for developing targeted strategies to manage and control H 2 S emissions within the context of underground hydrogen storage.
Physical and chemical weathering processes that might be important on Mars are reviewed, and the limited observations, including relevant Viking results and laboratory simulations, are summarized. Physical weathering may have included rock splitting through growth of ice, salt or secondary silicate crystals in voids. Chemical weathering probably involved reactions of minerals with water, oxygen, and carbon dioxide, although predicted products vary sensitively with the abundance and physical form postulated for the water. On the basis of kinetics data for hydration of rock glass on earth, the fate of weathering-rind formation on glass-bearing Martian volcanic rocks is tentatively estimated to have been on the order of 0.1 to 4.5 cm/Gyr; lower rates would be expected for crystalline rocks.
Mechanisms such as ice-shelf hydrofracturing and ice-cliff collapse may rapidly increase discharge from marine-based ice sheets. Here, we link a probabilistic framework for sea-level projections to a small ensemble of Antarctic ice-sheet (AIS) simulations incorporating these physical processes to explore their influence on global-mean sea-level (GMSL) and relative sea-level (RSL). We compare the new projections to past results using expert assessment and structured expert elicitation about AIS changes. Under high greenhouse gas emissions (Representative Concentration Pathway [RCP] 8.5), median projected 21st century GMSL rise increases from 79 to 146 cm. Without protective measures, revised median RSL projections would by 2100 submerge land currently home to 153 million people, an increase of 44 million. The use of a physical model, rather than simple parameterizations assuming constant acceleration of ice loss, increases forcing sensitivity: overlap between the central 90% of simulations for 2100 for RCP 8.5 (93-243 cm) and RCP 2.6 (26-98 cm) is minimal. By 2300, the gap between median GMSL estimates for RCP 8.5 and RCP 2.6 reaches >10 m, with median RSL projections for RCP 8.5 jeopardizing land now occupied by 950 million people (versus 167 million for RCP 2.6). The minimal correlation between the contribution of AIS to GMSL by 2050 and that in 2100 and beyond implies current sea-level observations cannot exclude future extreme outcomes. The sensitivity of post-2050 projections to deeply uncertain physics highlights the need for robust decision and adaptive management frameworks.
The 1-D axially dispersed plug flow model is a mathematical model widely used for the simulation of adsorption processes. Lumped mass transfer coefficients such as the Glueckauf linear driving force (LDF) term and the axial dispersion coefficient are generally obtained by fitting simulation results to the experimental breakthrough test data. An approach is introduced where these parameters, along with the only free parameter in the energy balance equations, are individually fit to specific test data that isolates the appropriate physics. It is shown that with this approach this model provides excellent simulation results for the C02 on zeolite SA sorbent/sorbate system; however, for the H20 on zeolite SA system, non-physical deviations from constant pattern behavior occur when fitting dispersive experimental results with a large axial dispersion coefficient. A method has also been developed that determines a priori what values of the LDF and axial dispersion terms will result in non-physical simulation results for a specific sorbent/sorbate system when using the one-dimensional axially dispersed plug flow model. A relationship between the steepness of the adsorption equilibrium isotherm as indicated by the distribution factor, the magnitude of the axial dispersion and mass transfer coefficient, and the resulting non-physical behavior is derived. This relationship is intended to provide a guide for avoiding non-physical behavior by limiting the magnitude of the axial dispersion term on the basis of the mass transfer coefficient and distribution factor.
We present an efficient data-driven regression approach for constructing reduced-order models (ROMs) of reaction-diffusion systems exhibiting pattern formation. The ROMs are learned non-intrusively from available training data of physically accurate numerical simulations. The method can be applied to general nonlinear systems through the use of polynomial model form, while not requiring knowledge of the underlying physical model, governing equations, or numerical solvers. The process of learning ROMs is posed as a low-cost least-squares problem in a reduced-order subspace identified via Proper Orthogonal Decomposition (POD). Numerical experiments on classical pattern-forming systems–including the Schnakenberg and Mimura–Tsujikawa models–demonstrate that higher-order surrogate models significantly improve prediction accuracy while maintaining low computational cost. The proposed method provides a flexible, non-intrusive model reduction framework, well suited for the analysis of complex spatio-temporal pattern formation phenomena.
A high-fidelity neutronics model of the Oak Ridge National Laboratory High Flux Isotope Reactor (HFIR) with the low-enriched uranium (LEU) high-density silicide dispersion Optimized fuel design was updated and analyzed to generate reactor physics-based metrics to support follow-on thermal hydraulic and transient analyses of this design. The Python HFIR Analysis and Measurement Engine (PHAME) was also updated to enhance the automation capabilities of the framework developed and maintained to perform these reactor physics modeling and simulation efforts. The automated framework significantly increases the efficiency and reproducibility to design and thoroughly analyzes HFIR LEU core designs, changes, and uncertainties. Reactor physics metrics evaluated include but are not limited to fuel depletion, cycle length, fission rate density distributions, axial power peaking factors, kinetics data, reactivity coefficients, control element worths, heat deposition rates, and decay heat. These neutronics results provide essential input to follow-on steady state thermal, thermal hydraulic and reactor transient analyses, which are subject of other reports. The Optimized design operates at 95 MW to maintain HFIR’s current highly enriched uranium core performance level at 85 MW.
Detector simulation is a key component of physics analysis and related activities in CMS. In the upcoming High Luminosity LHC era, simulation will be required to use a smaller fraction of computing in order to satisfy resource constraints. At the same time, CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which requires significantly more resources to simulate than the existing CMS calorimeters. This computing challenge motivates the use of generative machine learning models as surrogates to replace full physics-based simulation. We study the application of state-of-the-art diffusion models to simulate particle showers in the CMS HGCal. We will discuss methods to overcome the challenges posed by the high-dimensional, irregular geometry of the HGCal. The quality of the showers produced by the diffusion model will be assessed by comparison to the full GEANT4-based simulation. The increase in simulation throughput will be quantified and methods to accelerate the diffusion model inference will also be discussed.
This presentation describes the plans for ice crystal icing and supercooled water icing tests to be conducted in Fall/Winter 2021 at the NASA Icing Research Tunnel. A test article, whose geometry is representative of an inter-compressor duct and strut region of a turbofan engine, has been designed and fabricated for the study of ice crystal icing physics tests. The Simulated Inter-compressor Duct Research Model (SIDRM) will have the ability to heat surfaces to simulate the warm surfaces of the inter-compressor duct within an engine. The test article is instrumented with heaters, heat flux gauges, thermocouples, and pressure taps to measure icing behavior. The aim of these tests is to generate ice shapes on the SIDRM test article under well-characterized conditions. The results of the testing with the SIDRM model will be used to provide icing physics and validation data for development of the engine icing simulation capabilities in GlennICE, the 3D ice prediction tool. This is intended to be a presentation only, and does not have an accompanying paper.
A numerical method to simulate high Reynolds number jet flows was formulated and applied to gain a better understanding of the flow physics. Large-eddy simulation was chosen as the most promising approach to model the turbulent structures due to its compromise between accuracy and computational expense. The filtered Navier-Stokes equations were developed including a total energy form of the energy equation. Subgrid scale models for the momentum and energy equations were adapted from compressible forms of Smagorinsky's original model. The effect of using disparate temporal and spatial accuracy in a numerical scheme was discovered through one-dimensional model problems and a new uniformly fourth-order accurate numerical method was developed. Results from two- and three-dimensional validation exercises show that the code accurately reproduces both viscous and inviscid flows. Numerous axisymmetric jet simulations were performed to investigate the effect of grid resolution, numerical scheme, exit boundary conditions and subgrid scale modeling on the solution and the results were used to guide the three-dimensional calculations. Three-dimensional calculations of a Mach 1.4 jet showed that this LES simulation accurately captures the physics of the turbulent flow. The agreement with experimental data was relatively good and is much better than results in the current literature. Turbulent intensities indicate that the turbulent structures at this level of modeling are not isotropic and this information could lend itself to the development of improved subgrid scale models for LES and turbulence models for RANS simulations. A two point correlation technique was used to quantify the turbulent structures. Two point space correlations were used to obtain a measure of the integral length scale, which proved to be approximately 1/2 D(sub j). Two point space-time correlations were used to obtain the convection velocity for the turbulent structures. This velocity ranged from 0.57 to 0.71 U(sub j).
This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance.
Three procedures to guide selection of an efficient modal basis in a nonlinear random response analysis are examined. One method is based only on proper orthogonal decomposition, while the other two additionally involve smooth orthogonal decomposition. Acoustic random response problems are employed to assess the performance of the three modal basis selection approaches. A thermally post-buckled beam exhibiting snap-through behavior, a shallowly curved arch in the auto-parametric response regime and a plate structure are used as numerical test articles. The results of the three reduced-order analyses are compared with the results of the computationally taxing simulation in the physical degrees of freedom. For the cases considered, all three methods are shown to produce modal bases resulting in accurate and computationally efficient reduced-order nonlinear simulations.