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This paper addresses the formal equivalence between the Mach-Zehnder interferometer, the Ramsey spectroscope, and a specific quantum logical gate. Based on this equivalence we introduce the quantum Rosetta Stone, and we describe a projective measurement scheme for generating the desired correlations between the interferometric input states in order to achieve Heisenberg-limited sensitivity.
Examined is sensitivity of separation extent, wall pressure and heating to variation of primary input flow parameters, such as Mach and Reynolds numbers and shock strength, for 2D and Axisymmetric Hypersonic Shock Wave Turbulent Boundary Layer interactions obtained by Navier-Stokes methods using the SST turbulence model. Baseline parametric sensitivity response is provided in part by comparison with vetted experiments, and in part through updated correlations based on free interaction theory concepts. A recent database compilation of hypersonic 2D shock-wave/turbulent boundary layer experiments extensively used in a prior related uncertainty analysis provides the foundation for this updated correlation approach, as well as for more conventional validation. The primary CFD method for this work is DPLR, one of NASA's real-gas aerothermodynamic production RANS codes. Comparisons are also made with CFL3D, one of NASA's mature perfect-gas RANS codes. Deficiencies in predicted separation response of RANS/SST solutions to parametric variations of test conditions are summarized, along with recommendations as to future turbulence approach.
The Orion Multi-Purpose Crew Vehicle will one day carry astronauts to the Moon and beyond, and Orion's heatshield is a critical component in ensuring their safe return to Earth. The Orion heatshield is the structural component responsible for absorbing the intense heating environment caused by re-entry to Earth's atmosphere. The heatshield is primarily composed of Avcoat, an ablative material that is consumed during the re-entry process. Ablation is primarily characterized by two processes: pyrolysis and recession. The decomposition of in-depth virgin material is known as pyrolysis. Recession occurs when the exposed surface of the heatshield reacts with the surrounding flow. The Orion heatshield design was changed from an individually filled Avcoat honeycomb to a molded block Avcoat design. The molded block Avcoat heatshield relies on an adhesive bond to keep it attached to the capsule. In some locations on the heatshield, the integrity of the adhesive bond cannot be verified. For these locations, a mechanical retention device was proposed. Avcoat ablation was modelled in CHAR and the in-depth virgin material temperatures were used in a Thermal Desktop model of the mechanical retention device. The retention device was analyzed and shown to cause a large increase in the maximum bondline temperature. In order to study the impact of individual ablation modelling parameters on the heatshield sizing process, a Monte Carlo simulation of the sizing process was proposed. The simulation will give the sensitivity of the ablation model to each of its input parameters. As part of the Monte Carlo simulation, statistical uncertainties on material properties were required for Avcoat. Several properties were difficult to acquire uncertainties for: the pyrolysis gas enthalpy, non-dimensional mass loss rate (B´c), and Arrhenius equation parameters. Variability in the elemental composition of Avcoat was used as the basis for determining the statistical uncertainty in pyrolysis gas enthalpy and B´c. A MATLAB program was developed to allow for faster, more accurate and automated computation of Arrhenius reaction parameters. These parameters are required for a material model to be used in the CHAR ablation analysis program. This MATLAB program, along with thermogravimetric analysis (TGA) data, was used to generate uncertainties on the Arrhenius parameters for Avcoat. In addition, the TGA fitting program was developed to provide Arrhenius parameters for the ablation model of the gap filler material, RTV silicone.
This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%-82% and 60%-68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBSRMSE was approx.54%of that seen in the EnBS, while for snow courses the PBSRMSE was approx.79%of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.
The spatial distribution of relative humidity with respect to ice (RHI) in the boreal wintertime tropical tropopause layer (TTL, is asymptotically Equal to 14-18 km) over the Pacific is examined with the measurements provided by the NASA Airborne Tropical TRopopause EXperiment. We also compare the measured RHI distributions with results from a transport and microphysical model driven by meteorological analysis fields. Notable features in the distribution of RHI versus temperature and longitude include (1) the common occurrence of RHI values near ice saturation over the western Pacific in the lower to middle TTL; (2) low RHI values in the lower TTL over the central and eastern Pacific; (3) common occurrence of RHI values following a constant mixing ratio in the middle to upper TTL (temperatures between 190 and 200 K); (4) RHI values typically near ice saturation in the coldest airmasses sampled; and (5) RHI values typically near 100% across the TTL temperature range in air parcels with ozone mixing ratios less than 50 ppbv. We suggest that the typically saturated air in the lower TTL over the western Pacific is likely driven by a combination of the frequent occurrence of deep convection and the predominance of rising motion in this region. The nearly constant water vapor mixing ratios in the middle to upper TTL likely result from the combination of slow ascent (resulting in long residence times) and wave-driven temperature variability. The numerical simulations generally reproduce the observed RHI distribution features, and sensitivity tests further emphasize the strong influence of convective input and vertical motions on TTL relative humidity.
Characterization of atmospheric variability is crucial to the design of the Entry, Descent, and Landing (EDL) activities at Mars. The distribution of dust in the Martian atmosphere has a significant impact on the atmospheric structure and variability. Information obtained through reconstruction of EDL data from past missions can be used in studying the performance of a model used in preflight atmospheric characterization for future missions. We use the Mars Weather Research and Forecasting (MarsWRF) General Circulation Model to simulate the atmospheric temperature during the landing time periods of Mars Exploration Rovers MER-A and MER-B in 2004. The multi-scale capability of MarsWRF is useful for conducting high- resolution nested simulation centered at the landing site. This version of MarsWRF includes a high spectral accuracy k-distribution radiative transfer model. We use the IR dust opacity profile data from the Thermal Emission spectrometer (TES) limb scan data as well as the TES Nadir data to prescribe the dust opacity distribution. Data from MY 26, which corresponds to the year of MER landing, and data from a climatology based on the median of 4 years (MY24 to MY27) of TES data are used. Model temperature profiles are compared with the reconstructions from EDL data and TES observations. Sensitivity of the model simulation to variations in the input opacity data are shown.
The air ingress phenomena under the HTR conditions represent the important area of nuclear safety. Best practice guidelines for the use of CFD in nuclear safety applications are mainly based on water cooled reactors while in HTR the gas is used as a coolant. The ambition of this paper is to define the best recommendations and description of needed settings of CFD models solving the separate effects of air ingress accident phenomena. The JAERI experiments deal with phenomena of air ingress during the early stages of the accident. Experiments investigate the basic features of the flow behavior of the gas mixture and regard the phenomena of the molecular diffusion and the natural convection of the gas mixture with the graphite oxidation reaction. The separate effects simulations of air ingress accident were performed using CFD code ANSYS Fluent. Whole graphite oxidation process including all three main chemical reactions was successfully modeled. The new findings and useful information were gathered to be extrapolated to the integral effects level for the following phase. The role of the computational mesh and all important input parameters were discussed in detail and many in sensitivity studies were performed. (authors)
In this work we present a rate-dependent cohesive zone model for the fracture of polymeric interfaces and performs a Bayesian calibration, an uncertainty quantification, and a sensitivity analysis for the model. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. Inverse calibration of parameters for such complex models through trial and error is challenging due to the large number of parameters of the model. Moreover, the calibrated parameter values are often non-unique and uncertain when the available experimental data is limited. To tackle this challenge, we employ a Bayesian calibration approach to identify parameters from experimental data, the resulting parameters significantly enhance the accuracy of the model. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. The advantages of the Bayesian calibration over a deterministic parameter fit are demonstrated. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced, which significantly improves the model’s prediction. Finally, the total uncertainty of the model is quantified in a predictive setting. A sensitivity analysis is performed to assess how changes in the input variables of the model affect the peak load, facilitating the identification of a concise set of highly influential parameters. The present approach can be used for calibration and uncertainty quantification for other complex computational mechanics models. It should also facilitate the designing of interface materials under uncertainty.
Abstract Redox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.
Radiological probabilistic performance assessments (PPAs) employ highly complex computer models to simulate the release of radionuclides from wastes in an engineered disposal facility, migration of these radionuclides over time, and resulting radiological dose to potentially exposed individuals. An iterative approach to PPA modeling is described to help define the appropriate complexity of the computer model. An initial model is developed that addresses all relevant processes and media, but that employs preliminary statistical parameter distributions to minimize the initial effort expended. Where appropriate, transport and exposure submodels with simplifying assumptions are also used in the initial model. Different assumptions and boundaries may be explored with the initial model to assess their importance in constraining model results. Exploration of the results of the initial model are used to determine the benefits of model refinements, and to guide such refinements to focus on specific areas of the model that most influence the model outputs of interest. This approach promotes efficiency and model parsimony, but does not compromise defensibility when performed in a transparent manner. Key model boundaries for a PPA include the time frame of the modeling (temporal boundaries), where future human receptors may be located (spatial boundaries), and what types of land uses and activities may exist in the future (exposure scenarios). Simplified transport and exposure models can be useful in helping decision makers and stakeholders establish such boundaries by analyzing the consequences of selecting different bounds and understanding which assumptions and boundaries have the greatest influence on the magnitude and distribution of model results. Pathway analysis and parameter sensitivity analysis performed on PPA model results are key components of the iterative modeling approach. The objective of pathway analysis is to identify the critical radionuclides and pathways contributing to radiation dose during the modeling performance period. An environmental 'pathway' is described by the connections from a waste package or cell in the disposal facility to a future human exposure event. These connections are commonly visualized using graphical means. A simple example of a potentially complete pathway for radon-222 is: radium-containing waste → radon emanation → upwards diffusion in cap material → diffusion/advection into a future building → inhalation exposure to a building occupant Parameter sensitivity analysis is conducted on the results of a PPA to identify those specific model input parameters whose distribution of values contributes the most to the distribution of target model output, such as annual radiation dose. Pathway and sensitivity analyses on initial modeling results provide information for guiding subsequent model development, and possibly interim decisions, by informing questions tied to the modeling objectives and performance criteria. For example, questions related to radiological doses from existing disposals may include: 1. Is radiation dose from radiological exposures either obviously negligible or obviously unacceptable based on initial modeling results? 2. If dose is not obviously negligible or unacceptable relative to applicable performance criteria, what model refinements would reduce uncertainty in the results to better inform decisions? (authors)
The framework for a LOCA toolkit for the U.S. light water reactors (LOTUS) is outlined. LOTUS allows for the coupling of the core design code VERA-CS with the fuel performance code FRAPCON such that uncertainties can be propagated consistently. An analysis of inherent model discrepancies, confirms that all differences are either negligible or the focus on immediate future work. Monte Carlo sampling is performed on 15 inputs for a depletion case of one fuel assembly. Both uncertainty quantification (UQ) and sensitivity analysis (SA) are performed on the figures of merit (FOM) of minimum departure from nucleate boiling ratio (MDNBR), maximum fuel centerline temperature (MFCT), and gap conductance at peak power (GCPP). Pearson correlation coefficient, Sobol sensitivity indices, and the density-based, delta moment independent measure are used as SA metrics. All measures yield consistent rankings of inputs matching past work.
To optimize the performance of any system, the sensitivity derivatives of the system's output variables with respect to its input variables must be readily available. It is also desirable that these derivatives be inexpensive to calculate as the optimization process requires many evaluations of the output variables and their derivatives. Optimization methods that have been developed for use in automated structural design programs may not be extended for use in integrated multidisciplinary design programs until adequate means of calculating accurate sensitivity derivatives of complex, internally coupled systems have been developed. Until the development of the Global Sensitivity Equations (GSE), the only method of determining the sensitivity derivatives of coupled systems has been by using finite differences. Analytical or semi-analytical derivatives do not exist as there is no analytical solution to the coupled problem. Also, difficulties arise because the finite difference method is expensive as the system has to iterate to a converged solution for each incremental input variable. The method may not be accurate, and the choice of the input variable increment may cause the difference in the output variable to be insignificant compared to computer numerical error if the choice is too small, or the process may not predict the true value of the output variable if the increment is too large. The GSE allow the system's sensitivity derivatives to be calculated as functions of the component subsystem's (local) sensitivity derivatives. These local sensitivity derivatives are calculated from specifically decoupled subsystems, whereas the GSE account for total system coupling. Since the subsystems are decoupled, it may be possible for the local derivatives to be calculated by analytical or semi-analytical methods, which generally reduce cost and improve accuracy. Several academic problems have been solved using GSE and have demonstrated encouraging results.
A model of net primary production (NPP), decomposition, and nitrogen cycling in tundra ecosystems has been developed. The adjoint technique is used to study the sensitivity of the computed annual net CO2 flux to perturbation in initial conditions, climatic inputs, and model's main parameters describing current seasonal CO2 exchange in wet sedge tundra at Barrow, Alaska. The results show that net CO2 flux is most sensitive to parameters characterizing litter chemical composition and more sensitive to decomposition parameters than to NPP parameters. This underlines the fact that in nutrient-limited ecosystems, decomposition drives net CO2 exchange by controlling mineralization of main nutrients. The results also indicate that the short-term (1 year) response of wet sedge tundra to CO2-induced warming is a significant increase in CO2 emission, creating a positive feedback to atmosphreic CO2 accumulation. However, a cloudiness increase during the same year can severely alter this response and lead to either a slight decrease or a strong increase in emitted CO2, depending on its exact timing. These results demonstrate that the adjoint method is well suited to study systems encountering regime changes, as a single run of the adjoint model provides sensitivities of the net CO2 flux to perturbations in all parameters and variables at any time of the year. Moreover, it is shown that large errors due to the presence of thresholds can be avoided by first delimiting the range of applicability of the adjoint results.
Recent pre-launch measurements performed on the Joint Polar Satellite System (JPSS) J1 Visible Infrared Imaging Radiometer Suite (VIIRS) using the National Institute of Standards and Technology (NIST) Traveling Spectral Irradiance and Radiance Responsivity Calibrations Using Uniform Sources (T-SIRCUS) monochromatic source have provided wavelength dependent polarization sensitivity for select spectral bands and viewing conditions. Measurements were made at a number of input linear polarization states (twelve in total) and initially at thirteen wavelengths across the bandpass (later expanded to seventeen for some cases). Using the source radiance information collected by an external monitor, a spectral responsivity function was constructed for each input linear polarization state. Additionally, an unpolarized spectral responsivity function was derived from these polarized measurements. An investigation of how the centroid, bandwidth, and detector responsivity vary with polarization state was weighted by two model input spectra to simulate both ground measurements as well as expected on-orbit conditions. These measurements will enhance our understanding of VIIRS polarization sensitivity, improve the design for future flight models, and provide valuable data to enhance product quality in the post-launch phase.
The performance of Orion flight software, especially its GNC software, is being analyzed by running Monte Carlo simulations of Orion spacecraft flights. The simulated performance is analyzed for conformance with flight requirements, expressed as performance constraints. Flight requirements include guidance (e.g. touchdown distance from target) and control (e.g., control saturation) as well as performance (e.g., heat load constraints). The Monte Carlo simulations disperse hundreds of simulation input variables, for everything from mass properties to date of launch. We describe in this paper a sensitivity analysis tool ("Critical Factors Tool" or CFT) developed to find the input variables or pairs of variables which by themselves significantly influence satisfaction of requirements or significantly affect key performance metrics (e.g., touchdown distance from target). Knowing these factors can inform robustness analysis, can inform where engineering resources are most needed, and could even affect operations. The contributions of this paper include the introduction of novel sensitivity measures, such as estimating success probability, and a technique for determining whether pairs of factors are interacting dependently or independently. The tool found that input variables such as moments, mass, thrust dispersions, and date of launch were found to be significant factors for success of various requirements. Examples are shown in this paper as well as a summary and physics discussion of EFT-1 driving factors that the tool found.
There exist many methods for sensitivity analysis readily available to the practitioner. While each seeks to help the modeler answer the same general question – How do sources of uncertainty or changes in the model inputs relate to uncertainty in the output? – different methods are associated with different assumptions, constraints, and required resources, leading to conclusions that may vary in interpretability and level of detail. Thus, it is crucial that the practitioner selects the desired sensitivity analysis method judiciously, making sure to match the selected approach to the specifics of their problem and to their desired objectives. In this chapter, we provide a practical overview of a collection of widely used, widely available sensitivity analysis methods. We focus on global sensitivity approaches, which seek to characterize how uncertainty in the model output may be allocated to sources of uncertainty in model inputs across the entire input space. Generally, this will require the practitioner to specify a probability distribution over the input space. On the other hand, methods for local sensitivity analysis do not require this specification but they have more limited utility, providing insight into sources of uncertainty associated only with a particular, specified location in the input space. Our hope is that this chapter may serve as a decision-making tool for practitioners, helping to guide the selection of a sensitivity analysis approach that will best fit their needs. To support this goal, we have selected a suite of approaches to cover, which, while not exhaustive, we believe provides a flexible and robust sensitivity analysis toolkit. All methods included are widely used and available in standard software packages.
One of the key trade studies that must be considered when studying any new space transportation hardware is whether to go reusable or expendable. An analysis is presented here for such a trade relative to a proposed Liquid Rocket Booster which is being studied at MSFC. The assumptions or inputs to the trade were developed and integrated into a model that compares the Life-Cycle Costs of both a reusable LRB and an expendable LRB. Sensitivities were run by varying the input variables to see their effect on total cost. In addition a Monte-Carlo simulation was run to determine the amount of cost risk that may be involved in a decision to reuse or expend.
The paper provides simulation data of previous work by the author in developing a model for estimating detectability of crack-like flaws in radiography. The methodology is being developed to help in implementation of NASA Special x-ray radiography qualification, but is generically applicable to radiography. The paper describes a method for characterizing X-ray detector resolution for crack detection. Applicability of ASTM E 2737 resolution requirements to the model are also discussed. The paper describes a model for simulating the detector resolution. A computer calculator application, discussed here, also performs predicted contrast and signal-to-noise ratio calculations. Results of various simulation runs in calculating x-ray flaw size parameter and image contrast for varying input parameters such as crack depth, crack width, part thickness, x-ray angle, part-to-detector distance, part-to-source distance, source sizes, and detector sensitivity and resolution are given as 3D surfaces. These results demonstrate effect of the input parameters on the flaw size parameter and the simulated image contrast of the crack. These simulations demonstrate utility of the flaw size parameter model in setting up x-ray techniques that provide desired flaw detectability in radiography. The method is applicable to film radiography, computed radiography, and digital radiography.