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Sensitivity analysis and simulation studies performed for a satellite gravity gradiometer (SGG) mission, and ideas about necessary future numerical simulations are reviewed.
The properties of core turbulence and thermal transport are investigated for EAST high-β P (β P ~ 3.1) plasmas with dominant electron heating (Te/Ti > 1) via gyrokinetic simulation with the NLT code. Linear simulations identify that the electrostatic η e -driven trapped electron mode (η e -TEM) dominates in the core region (ρ< 0.7) and the ion temperature gradient (ITG) mode dominates in the out region (ρ≥ 0.7), consistent with the linear threshold analysis of micro-instabilities. Sensitivity analysis shows that the normalized electron density gradient (R/L ne ) and ITG (R/L Ti ) are two effective parameters to stabilize TEM instability. Nonlinear simulations are also carried out and compared with the experimental results, which show that the electron thermal internal transport barrier (ITB) in EAST high-β P plasma is determined by the TEM-induced turbulence. A higher zonal flow shearing rate is observed in the ITB region (0.2 < ρ < 0.34), which can regulate energy transport induced by TEM turbulence and facilitate the formation of e-ITB. A plausible positive feedback mechanism to mitigate turbulence transport and improve energy confinement via enhanced ion heating is proposed for future experiments.
The cost effectiveness of various propulsion technologies for shuttle-launched multimission modular spacecraft (MMS) missions was determined with special attention to the potential role of ion propulsion. The primary criterion chosen for comparison for the different types of propulsion technologies was the total propulsion related cost, including the Shuttle charges, propulsion module costs, upper stage costs, and propulsion module development. In addition to the cost comparison, other criteria such as reliability, risk, and STS compatibility are examined. Topics covered include MMS mission models, propulsion technology definition, trajectory/performance analysis, cost assessment, program evaluation, sensitivity analysis, and conclusions and recommendations.
Intensive research and development in Computational Fluid Dynamics (CFD) has recently produced many powerful CFD codes to simulate complex aerodynamic phenomena. However, in order to enhance the usefulness of these CFD codes for design practice, development of design sensitivity equations compatible to these codes becomes very important. This paper represents a part of such an effort to develop a sensitivity analysis methodology that enables the sensitivity equations to be implemented into existing CFD codes with minimal code modification. The methodology is based upon a preelimination procedure which accounts for consistently linearized boundary conditions. Formulations of both the direct differentiation and the adjoint variable methods will be presented in the paper.
A recently developed three-dimensional aerodynamic shape optimization procedure AeSOP(sub 3D) is described. This procedure incorporates some of the most promising concepts from the area of computational aerodynamic analysis and design, specifically, discrete sensitivity analysis, a fully implicit 3D Computational Fluid Dynamics (CFD) methodology, and 3D Bezier-Bernstein surface parameterizations. The new procedure is demonstrated in the preliminary design of supersonic delta wings. Starting from a symmetric clipped delta wing geometry, a Mach 1.62 asymmetric delta wing and two Mach 1. 5 cranked delta wings were designed subject to various aerodynamic and geometric constraints.
The Jupiter Icy Moons Orbiter (JIMO) Space System is part of the NASA's Prometheus Program. As part of the JIMO engineering team at NASA Glenn Research Center, the structural design of the JIMO Heat Rejection Subsystem (HRS) is evaluated. An initial goal of this study was to perform sensitivity analyses to determine the relative importance of the input variables on the structural responses of the radiator panel. The desire was to let the sensitivity analysis information identify the important parameters. The probabilistic analysis methods illustrated here support this objective. The probabilistic structural performance evaluation of a HRS radiator sandwich panel was performed. The radiator panel structural performance was assessed in the presence of uncertainties in the loading, fabrication process variables, and material properties. The stress and displacement contours of the deterministic structural analysis at mean probability was performed and results presented. It is followed by a probabilistic evaluation to determine the effect of the primitive variables on the radiator panel structural performance. Based on uncertainties in material properties, structural geometry and loading, the results of the displacement and stress analysis are used as an input file for the probabilistic analysis of the panel. The sensitivity of the structural responses, such as maximum displacement and maximum tensile and compressive stresses of the facesheet in x and y directions and maximum VonMises stresses of the tube, to the loading and design variables is determined under the boundary condition where all edges of the radiator panel are pinned. Based on this study, design critical material and geometric parameters of the considered sandwich panel are identified.
Polycrystalline diamond compact (PDC) bit use has been expanded to geothermal drilling applications due to their improved performance in drilling compared to roller cone bits. Multiple vibration models have been developed yet and there are limited use of field data to study the ability of numerical vibration model to predict recorded vibration in the field. The objective of this paper is to evaluate the capabilities of two different rock-bit interaction models with two different ROP formulations for predicting the actual behavior seen in the field. The first model (Model-I) considers the rock-bit interaction, incorporating the effect of subsurface formation characteristics. The second model (Model-II) is a PDC bit model that uses calculated threshold weight-on-cutter (WOCt) to study the interaction of the bit with the rock surface. The models, which couple the drill stem axial and torsional motions, are solved numerically using the fourth-order Runge-Kutta method and compared with recorded downhole drilling data from the Chocolate Mountain Aerial Gunnery Range (CMAGR) geothermal well located in California. An 8–½" PDC bit with 8 blades was modeled and vibration simulation was performed to investigate stick-slip vibrations at 1400 ft. depth. Simulation results using field data indicate that Model-II encounters a low range of stick-slip severity (SSV) as compared to Model-I, which is in the moderate range. Comparison with the field value for SSV shows Model-II is the best representative of the field case for this depth. The sensitivity analysis shows that results are sensitive to model constants making prediction of SSV from models uncertain.
This presentation showcases the work done under the ART work package in FY23 regarding optimization and sensitivity analysis of pebble-bed reactors. The slides include a description of the problem, the proposed methodology, the developed model used for analysis, and the methodologies for sensitivity and reduced-order modeling.
Due to the recent encouragement for loggers to integrate tracked processors into their conventional harvest equipment regime in the Southeastern United States, this study aimed to provide loggers with an estimate of the additional productivity and operating costs that could be incurred. Since tracked processors have rarely been operated in this region, a comparison study was conducted to determine differences in productivity and operating costs between a less experienced operator versus a more experienced operator. The study indicated the less experienced operator was able to produce 67 green tonnes/hour while the more experienced operator produced 80 green tonnes/hour. Costs per tonne for the less experienced operator were approximately $\$ $2.18/tonne at the end of year 1 and decreased to $\$$1.92/tonne at the end of year 5, depicting a $\$$0.26/tonne difference within 5 years. The more experienced operator incurred costs of $\$ $1.82/tonne at the end of year 1 and $\$ $1.61/tonne at the end of year 5 for a total difference of $\$ $0.21/tonne. A sensitivity analysis demonstrated that extreme fluctuations in fuel or maintenance/repair prices had less than a $\$ $0.20 increase in the final cost/ tonne. An increase in production rates from 65 to 125 tonnes decreased the cost/tonne $\$ $0.90/tonne, and increasing utilization rates from 50% to 85% decreased the cost per tonne of the processor by $\$ $0.78 per tonne. These results indicated that operators should be less concerned with changes in fuel and maintenance costs and be more concerned with their production and utilization rates.
Reaction parameters significantly affect oil production from shales by means of heating and in-situ upgrading. In this work, we perform numerical simulations of two chemical reaction models, which are mainly used in the research of kerogen pyrolysis and subsequent hydrocarbon decomposition in organic-rich porous media. They are the Braun and Burnham model and Wellington model. In these forward numerical simulations, we present the influence of the two reaction models on hydrocarbon production. The Braun and Burnham reaction model shows more vigorous kerogen and subsequent decomposition reactions and more hydrocarbon production than the Wellington model. A local sensitivity analysis identifies the reaction parameters with the highest influence on productivity, and the most sensitive outputs. A data-worth analysis identifies the most valuable observation data to be measured for the best prediction of total hydrocarbon production. We find that the most valuable observation data is the cumulative production of heavy oil in the Braun and Burnham model and of light oil in the Wellington model, respectively. Finally, once we determine the maximum allowable prediction uncertainty and the expected measurement uncertainty, the observation data to be measured for the minimization of prediction uncertainty can be obtained.
During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.
PyApprox is a Python-based one-stop-shop for probabilistic analysis of scientific numerical models. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent the most popular methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. Simple interfaces are provided for the most commonly-used algorithms to limit a user’s need to tune the various hyper-parameters of each algorithm. However, more advanced work flows that require customization of hyper-parameters is also supported. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of different algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in ground water.
The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States at the WNYNSC. NFS built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (640 metric tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations halted in 1972 and never restarted, leaving behind radioactive and chemical wastes. The reprocessing wastes include high-level waste (HLW), so U.S. Department of Energy (DOE) was compelled to complete certain waste management activities under the West Valley Demonstration Project (WVDP) Act of 1980, including decommissioning of WVDP facilities. As collaborating agencies, NYSERDA and the DOE are tasked with making decisions about decommissioning and risk reduction for the West Valley Site. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) model to assist the agencies in their decision making process for decommissioning the WVDP and WNYNSC. One important tool that is needed in order to inform the decision-making process is a science-based model of the contamination at the West Valley Site and its potential implications for human health and the environment. The West Valley PPA Model (the Model), developed using the GoldSim modeling software, is a tool intended to provide support for decision making with uncertainty, in a manner that is transparent, defensible, and robust. The PPA Model includes contaminant transport and health effects components, and is organized around geographically-organized contaminated facilities. These include the waste disposal areas licensed by the U.S. Nuclear Regulatory Commission and the State of New York, a waste tank farm for storage of HLW resulting from reprocessing operations, and several areas contaminated with radioactive and chemical constituents. The Model evaluates contaminant transport from these sources to points of exposure across the site and into receiving surface waters and sediments. Hypothetical people and wildlife are exposed to contamination at these locations, and the effects of these exposures are evaluated. Contaminant transport processes to be evaluated in the Model include groundwater and surface water transport, contaminant translocation by plants and animals, diffusion in subsurface air and water, and erosion of the Site. The evaluation of exposures to people in this preliminary model is limited to a resident farmer scenario, and ecological assessment is performed at the level of a screening analysis. The results of these preliminary evaluations inform future model developments. Model results are subjected to sensitivity analysis in order to determine those pathways and parameters that are most significant in influencing the results. This information allows analysts and decision makers to focus on those aspects of Site behavior and processes that contribute most to overall uncertainty. With this information, the Supplemental Environmental Impact Statement (SEIS) can inform defensible decisions regarding remediation of the Site. (authors)
This work develops an amplitude method for the photothermal (PT) technique to analyze the amplitude of the thermal radiation signal from the surface of a multilayered film sample. The thermal conductivity of any individual layer in the sample can be thereby determined. Chemical vapor deposited SiC film samples (sample 1 to 3: 2.5 to 3.5 μm thickness) with different ratios of Si to C and thermally oxidized SiO{sub 2} film (500 nm thickness) on silicon substrates are studied using the amplitude method. The determined thermal conductivity based on the amplitude method is 3.58, 3.59, and 2.59 W/m⋅K for sample 1 to 3 with ±10% uncertainty. These results are verified by the phase shift method, and sound agreement is obtained. The measured thermal conductivity (k) of SiC is much lower than the value of bulk SiC. The large k reduction is caused by the structure difference revealed by Raman spectroscopy. For the SiO{sub 2} film, the thermal conductivity is measured to be 1.68 ± 0.17 W/m⋅K, a little higher than that obtained by the phase shift method: 1.31 ± 0.06 W/m⋅K. Sensitivity analysis of thermal conductivity and interfacial resistance is conducted for the amplitude method. Its weak-sensitivity to the thermal contact resistance, enables the amplitude method to determine the thermal conductivity of a film sample with little effect from the interface thermal resistance between the film and substrate. The normalized amplitude ratio at a high frequency to that at a low frequency provides a reliable way to evaluate the effusivity ratio of the film to that of the substrate.
We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude $\in$ [0, 1]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. Finally, the approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.
ABSTRACT: Effective management of induced seismicity is critical for safe and sustainable CO2 storage. This study evaluates fault slippage risks in the Delaware Basin (Texas) and Basin and Range Province (Utah), integrating geological, operational, and geomechanical parameters to assess fault stability and seismic hazard mitigation. In the Delaware Basin, two sites were analyzed under an injection rate of 20,000 bbl/day over 25 years. One site showed low fault slip risk, while the other exhibited higher reactivation potential due to proximity to critically stressed faults. Sensitivity analysis revealed that increased pore pressure significantly heightened slip potential, highlighting the necessity of precise pressure control and real-time monitoring. In the Basin and Range Province, fault stability was evaluated at Neck of the Desert, Escalante Desert, Parowan, and Beaver sites under injection rates of 8,750 bbl/day per site over 30 years. Minimal fault slip risk was observed at Neck of the Desert and Escalante Desert sites, whereas Parowan and Beaver sites exhibited elevated slip potential due to semi-critically stressed faults sensitive to modest pore pressure increases. The findings demonstrate that fault slippage analysis, combined with sensitivity analysis of pore pressure and friction coefficients, is essential for understanding seismic risks. Continuous monitoring, adaptive injection management, and rigorous geomechanical analysis are key strategies for minimizing induced seismicity in CO2 sequestration projects.
In this study, several uncertainty quantification and sensitivity analysis methods are used to determine the most sensitive geometric and material input parameters of a cantilevered pipeline conveying fluid when uncertainty is introduced to the system at the onset of instability. The full nonlinear equations of motion are modeled using the extended Hamilton’s principle and then discretized using Galerkin’s method. A parametric study is first performed, and the Morris elementary effects are calculated to obtain a preliminary understanding of how the onset speed changes when each parameter is introduced to a ± 5% uncertainty. Then, four different input uncertainty distributions, mainly, uniform and Gaussian distribution, are chosen to investigate how input distributions affect uncertainty in the output. A convergence analysis is used to determine the number of samples needed to maintain simulation accuracy while saving the most computational time. Then, Monte Carlo simulations are run, and the output distributions for each input distribution at ± 1%, ± 3% and ± 5% input uncertainty range are found and discussed. Additionally, the Pearson correlation coefficients are evaluated for different uncertainty ranges. A final Monte Carlo study is performed in which single parameters are held constant while all others still have uncertainty. Overall, the flow speed at the onset of instability is the most sensitive to changes in the outer diameter of the pipe.
Material Recovery Facilities (MRFs) are crucial players in achieving a circular economy. MRFs receive complex waste streams and separate valuable recyclables from these mixtures. This study conducts techno-economic analysis (TEA) to estimate the net present value (NPV) and life cycle assessment (LCA) to estimate different environmental impacts of a commercial scale standalone, single-stream MRF to assess the economic feasibility and environmental impacts of recovering valuable recyclables from an MRF processing 120,000 tonnes per year (t/y). The TEA employs a discounted cash flow rate of return (DCFROR) analysis over a 20-year facility lifetime, along with a sensitivity analysis on the impact of different operating and economic parameters. Results show that the total fixed cost of building the MRF facility is $\$ $23 MM, and the operating cost is $\$ $45.48/tonne. Further the NPV of the MRF can vary from $\$ $3.57 MM to $\$ $60 MM, while 100-year global warming potential can range from 5.98 to 8.53 kg carbon dioxide equivalents (CO2-eq) per tonne of MSW. We have also found that MSW composition (arising from regional effects) significantly impacts costs, 100-year global warming potential, and other impact categories such as acidification potential, eutrophication potential, ecotoxicity, ozone depletion, photochemical oxidation, carcinogenic effects, and non-carcinogenic effects. Sensitivity and uncertainty analysis indicate that waste composition and market prices significantly impact the profitability of the MRF, and the waste composition mostly impacts global warming potential. Our analysis also indicates that facility capacity, fixed capital cost, and waste tipping fees are vital parameters that affect the economic viability of MRF operations.