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NASA NTRS1987

Modal sensitivity for structural systems with repeated frequencies

Repeated or closely packed modal frequencies are common physical occurrences for vibrating structures which are complex or possess multi-planes of symmetry. The computation of the sensitivity to structural modifications for these frequencies and mode shapes is made difficult by the fact that the mode shapes are not unique, since any linear combination of eigenvectors corresponding to a repeated eigenvalue is also an eigenvector. The work of Chen and Pan is extended, who used modal expansion techniques for accommodating the sensitivity analysis of structures with repeated eigenvalues. Starting with a discussion of the physical significance of sensitivity analysis for repeated frequency modes, a derivation is presented of the governing equations for the derivatives of a repeated eigenvalue. This is followed with a small example to illustrate the results. An efficient computation procedure, based upon an expansion of Nelson's ideas for large banded systems, is then proposed for systems with repeated or closely spaced eigenvalues.

Ojalvo, I. U.↗
DOE OSTI2021

Stochastic simulations of temporally and spatially variable Fe cycling within a floodplain aquifer

This data package contains model input and output files for local and global sensitivity analysis (SA) of a reactive transport model simulating redox cycling within a floodplain aquifer. The reaction network is implemented in CrunchFlow and focuses on spatially and temporally heterogeneous Fe cycling, though it contains several other species and redox pathways (37 aqueous species and 7 minerals in total). The package contains data from 600 Monte Carlo simulations (for global SA) and 104 one-at-a-time simulations (for local SA). It includes all input files required to run the CrunchFlow model, as well as limited model output – .csv files containing time series of primary species concentrations at 21 grid cells within the domain. These time series files contain the following species: H+, CO32-, SO42-, Cl-, Ca2+, Mg2+, Na+, K+, Fe(II), Fe(III), HS-, CH3COO-, O2(aq), S(aq), and Br-.These data were used to demonstrate the application of a novel form of global SA, distance-based generalized sensitivity analysis (DGSA) to a reactive transport model. The 2D model simulates the export of reduced species from pockets of fine-grained, organic-rich sediments embedded in a coarse sand aquifer. Stochastic simulations jointly varied 17 key model input parameters across several orders of magnitude, including the spatial variability of each parameter. DGSA on the model results reveal that the amplitude and variability of exported Fe(II) are most sensitive to the interaction between sand permeability and individual reaction rates. By contrast, the propagation of reducing conditions downgradient from the fine-grained lenses depends most heavily on the rate of dissolved organic carbon and sulfate release from the fines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Surrogate Models for TREAT Transient Calculations

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) is currently preparing for resumption of operation after more than 20 years in a long-term standby state. In parallel with preparation for operations and a new transient testing program, the DOENE Advanced Modeling and Simulation program (NEAMS) has been supporting the development of three-dimensional transient multi-physics modeling capabilities within the reactor physics application MAMMOTH. Recently, the MAMMOTH reactor physics team has begun development of uncertainty and sensitivity analysis for the TREAT core. This research will be used to support the analysis of reactor operation and experiment planning. Traditional approaches, i.e., Monte Carlo simulations, for such tasks require a large number of model evaluations under both steady state and transient conditions. However, exhaustive sets of sampling runs are simply not practical for TREAT since evaluations of each model can take a substantial amount of time. A solution to this problem is to build a surrogate model employing a smaller number of model evaluations. The surrogate model is designed to provide an accurate and fast approximation to the detailed TREAT model. In this manner, the surrogate model can be used in place of the TREAT transient model for uncertainty and sensitivity analysis at a fraction of the computational cost. The high dimensional model representation (HDMR) method is employed here for approximating the responses of interest in terms of functions of lower dimensions. This method has been implemented in RAVEN and all TREAT transient calculations are performed using the multiphysics code MAMMOTH. In addition, a code interface has also been developed in order to couple RAVEN with MAMMOTH. The surrogate model is then tested and compared for TREAT startup testing program Transient Test no. 15 core, and its accuracy is measured by its ability to estimate the mean, coefficient of variance, skewness, excess kurtosis and probability density function of the outputs of Transient Test no. 15 model, as well as root mean square error metric.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

SU-F-T-03: Radiobiological Evaluation of a Directional Brachytherapy Device Surgically Implanted Following EBRT

Purpose: Preceding surgical implantation following external-beam radiotherapy (EBRT) delivery, a radiobiological evaluation was performed for a new LDR Pd-103 directional brachytherapy device (CivaSheet). As this was the first case with the device used in combination with EBRT, there was concern to determine the appropriate prescription dose. Methods: The radiobiological model of Dale (1985, 1989) was used for a permanent LDR implant including radioactive decay. The biological effective dose (BED) was converted to the equivalent dose in 2 Gy fractions (EQD2) for comparison with EBRT prescription expectations. Given IMRT delivery of 50.4 Gy, an LDR brachytherapy dose of approximately 15–20 Gy EQD2 was desired. To be specific to the treatment site (leiomyosarcoma T2bN0M0, grade 2 with R1 surgical margin), the radiobiological model required several radiobiological parameters with values taken from the literature. A sensitivity analysis was performed to determine their relative importance on the calculated BED and subsequent EQD2. The Pd-103 decay constant (λ=0.0017 h{sup −1}) was also used. DVHs were prepared for pre- and post-surgical geometries to glean the possible and realized implant geometric configuration. DVHs prepared in VariSeed9 were converted to BEDVHs and subsequently EQD2 values for each volume-element. Results: For a physical dose of 28 Gy to a 0.5 cm depth, BED=21.7 Gy and EQD2=17.6 Gy, which was near the center of the desired EQD2 range. Tumor bed (CTV=4 cm{sup 3}) coverage was 99.2% with 48 sources implanted. In order of decreasing importance from the sensitivity analysis, the radiobiological parameters were α=0.25 Gy{sup −1}, T{sub POT}=23 days, α/β=8.6 Gy, and T=1.5 h. Percentage variations in these values produced EQD2 variations of 40%, 20%, 18%, and 1%, respectively. Conclusion: This radiobiological evaluation indicated that prescription dose may be determined for comparison with the desired EQD2, and that radiobiologicalparameter uncertainties produce smaller EQD2 differences than had physical dose been simply added to EBRT dose. Research support provided in part by CivaTech Oncology, Inc. for Dr. Rivard.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1983

Extrapolation of optimum design based on sensitivity derivatives

In many applications, it is useful to have information regarding the effect of the perturbations of the design problem constants (parameters) on the optimum solution of the problem. Sobieszczanski-Sobieski et al. (1981) have, therefore, proposed to utilize an optimum sensitivity analysis as a structural design tool to assess the parameter perturbation effects by extrapolations based on optimum sensitivity derivatives. The present investigation is concerned with an example of optimum sensitivity analysis involving a composite panel. The case was selected in connection with the nonlinearity of the constraints and, the tendency for a set of active constraints to change when the parameters are disturbed. Under these adverse conditions, sensitivity derivatives are used in linear extrapolations to extend optimization information over a limited range of variation for a typical parameter.

Barthelemy, J.-F. M.↗
DOE OSTI2016

Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics

We demonstrate that centered likelihood ratio estimators for the sensitivity indices of complex stochastic dynamics are highly efficient with low, constant in time variance and consequently they are suitable for sensitivity analysis in long-time and steady-state regimes. These estimators rely on a new covariance formulation of the likelihood ratio that includes as a submatrix a Fisher information matrix for stochastic dynamics and can also be used for fast screening of insensitive parameters and parameter combinations. The proposed methods are applicable to broad classes of stochastic dynamics such as chemical reaction networks, Langevin-type equations and stochastic models in finance, including systems with a high dimensional parameter space and/or disparate decorrelation times between different observables. Furthermore, they are simple to implement as a standard observable in any existing simulation algorithm without additional modifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

The financial attractiveness assessment of large waste management projects registered as clean development mechanism

Highlights: • Projects are not financially attractive without registration as CDMs. • WM benchmarks and indicators are converging and reducing in variance. • A sensitivity analysis reveal that revenue has more of an effect on the financial results. • Results indicate that an extensive database would reduce WM project risk and capital costs. • Disclosure standards would make information more comparable worldwide. - Abstract: This study illustrates the financial analyses for demonstration and assessment of additionality presented in the project design (PDD) and enclosed documents of the 431 large Clean Development Mechanisms (CDM) classified as the ‘waste handling and disposal sector’ (13) over the past ten years (2004–2014). The expected certified emissions reductions (CER) of these projects total 63.54 million metric tons of CO{sub 2}eq, where eight countries account for 311 projects and 43.36 million metric tons. All of the projects declare themselves ‘not financially attractive’ without CER with an estimated sum of negative results of approximately a half billion US$. The results indicate that WM benchmarks and indicators are converging and reducing in variance, and the sensitivity analysis reveals that revenues have a greater effect on the financial results. This work concludes that an extensive financial database with simple standards for disclosure would greatly diminish statement problems and make information more comparable, reducing the risk and capital costs of WM projects.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Quantitative analysis of treatment process time and throughput capacity for spot scanning proton therapy

Purpose: To determine the patient throughput and the overall efficiency of the spot scanning system by analyzing treatment time, equipment availability, and maximum daily capacity for the current spot scanning port at Proton Therapy Center Houston and to assess the daily throughput capacity for a hypothetical spot scanning proton therapy center. Methods: At their proton therapy center, the authors have been recording in an electronic medical record system all treatment data, including disease site, number of fields, number of fractions, delivered dose, energy, range, number of spots, and number of layers for every treatment field. The authors analyzed delivery system downtimes that had been recorded for every equipment failure and associated incidents. These data were used to evaluate the patient census, patient distribution as a function of the number of fields and total target volume, and equipment clinical availability. The duration of each treatment session from patient walk-in to patient walk-out of the spot scanning treatment room was measured for 64 patients with head and neck, central nervous system, thoracic, and genitourinary cancers. The authors retrieved data for total target volume and the numbers of layers and spots for all fields from treatment plans for a total of 271 patients (including the above 64 patients). A sensitivity analysis of daily throughput capacity was performed by varying seven parameters in a throughput capacity model. Results: The mean monthly equipment clinical availability for the spot scanning port in April 2012–March 2015 was 98.5%. Approximately 1500 patients had received spot scanning proton therapy as of March 2015. The major disease sites treated in September 2012–August 2014 were the genitourinary system (34%), head and neck (30%), central nervous system (21%), and thorax (14%), with other sites accounting for the remaining 1%. Spot scanning beam delivery time increased with total target volume and accounted for approximately 30%–40% of total treatment time for the total target volumes exceeding 200 cm{sup 3}, which was the case for more than 80% of the patients in this study. When total treatment time was modeled as a function of the number of fields and total target volume, the model overestimated total treatment time by 12% on average, with a standard deviation of 32%. A sensitivity analysis of throughput capacity for a hypothetical four-room spot scanning proton therapy center identified several priority items for improvements in throughput capacity, including operation time, beam delivery time, and patient immobilization and setup time. Conclusions: The spot scanning port at our proton therapy center has operated at a high performance level and has been used to treat a large number of complex cases. Further improvements in efficiency may be feasible in the areas of facility operation, beam delivery, patient immobilization and setup, and optimization of treatment scheduling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Kinetic Analysis of Competitive Electrocatalytic Pathways: New Insights into Hydrogen Production with Nickel Electrocatalysts

The hydrogen production electrocatalyst Ni(PPh2NPh2)22+ (1) is capable of traversing multiple electrocatalytic pathways. When using dimethylformamidium, DMF(H)+, the mechanism of formation of H2 catalyzed by 1 changes from an ECEC to an EECC mechanism as the potential approaches the Ni(I/0) couple. Two recent electrochemical methods, current-potential analysis and foot-of-the-wave analysis (FOWA), were performed on 1 to measure the detailed chemical kinetics of the competing ECEC and EECC pathways. A sensitivity analysis was performed on the electrochemical methods using digital simulations to gain a better understanding of their strengths and limitations. Notably, chemical rate constants were significantly underestimated when not accounting for electron transfer kinetics, even when electron transfer was fast enough to afford a reversible non-catalytic wave. The EECC pathway of 1 was found to be faster than the ECEC pathway under all conditions studied. Using buffered DMF: DMF(H)+ mixtures led to an increase in the catalytic rate constant (kobs) of the EECC pathway, but kobs for the ECEC pathway did not change when using buffered acid. Further kinetic analysis of the ECEC path revealed that added base increases the rate of isomerization of the exo-protonated Ni(0) isomers to the catalytically active endo-isomers, but decreases the net rate of protonation of Ni(I). FOWA on 1 did not provide accurate rate constants due to incomplete reduction of the exo-protonated Ni(I) intermediate at the foot of the wave, but FOWA could be used to estimate the reduction potential of this previously undetected intermediate. This research was supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. Pacific Northwest National Laboratory is operated by Battelle for the U.S. Department of Energy.

electrocatalysis↗
DOE OSTI2024

Bayesian calibration and uncertainty quantification of a rate-dependent cohesive zone model for polymer interfaces

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.

42 ENGINEERING↗
DOE OSTI2015

Local measurement of thermal conductivity and diffusivity

Simultaneous measurement of local thermal diffusivity and conductivity is demonstrated on a range of ceramic samples. This was accomplished by measuring the temperature field spatial profile of samples excited by an amplitude modulated continuous wave laser beam. A thin gold film is applied to the samples to ensure strong optical absorption and to establish a second boundary condition that introduces an expression containing the substrate thermal conductivity. The diffusivity and conductivity are obtained by comparing the measured phase profile of the temperature field to a continuum based model. A sensitivity analysis is used to identify the optimal film thickness for extracting the both substrate conductivity and diffusivity. Proof of principle studies were conducted on a range of samples having thermal properties that are representatives of current and advanced accident tolerant nuclear fuels. It is shown that by including the Kapitza resistance as an additional fitting parameter, the measured conductivity and diffusivity of all the samples considered agreed closely with the literature values. A distinguishing feature of this technique is that it does not require a priori knowledge of the optical spot size which greatly increases measurement reliability and reproducibility.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

An optimization model for collection, haul, transfer, treatment and disposal of infectious medical waste: Application to a Greek region

Highlights: • We developed an optimization model to minimize cost of infectious waste management. •Two different software were used for model solution. • Model optimizes locations and capacities of treatment plants and transfer stations. • The higher percentage of total cost was due to construction of the treatment plant. • Sensitivity analysis with respect to fuel cost and waste production was conducted. - Abstract: The objective of this work was to develop an optimization model to minimize the cost of a collection, haul, transfer, treatment and disposal system for infectious medical waste (IMW). The model calculates the optimum locations of the treatment facilities and transfer stations, their design capacities (t/d), the number and capacities of all waste collection, transport and transfer vehicles and their optimum transport path and the minimum IMW management system cost. Waste production nodes (hospitals, healthcare centers, peripheral health offices, private clinics and physicians in private practice) and their IMW production rates were specified and used as model inputs. The candidate locations of the treatment facilities, transfer stations and sanitary landfills were designated, using a GIS-based methodology. Specifically, Mapinfo software with exclusion criteria for non-appropriate areas was used for siting candidate locations for the construction of the treatment plant and calculating the distance and travel time of all possible vehicle routes. The objective function was a non-linear equation, which minimized the total collection, transport, treatment and disposal cost. Total cost comprised capital and operation costs for: (1) treatment plant, (2) waste transfer stations, (3) waste transport and transfer vehicles and (4) waste collection bins and hospital boxes. Binary variables were used to decide whether a treatment plant and/or a transfer station should be constructed and whether a collection route between two or more nodes should be followed. Microsoft excel software was used as installation platform of the optimization model. For the execution of the optimization routine, two completely different software were used and the results were compared, thus, resulting in higher reliability and validity of the results. The first software was Evolver, which is based on the use of genetic algorithms. The second one was Crystal Ball, which is based on Monte Carlo simulation. The model was applied to the Region of East Macedonia – Thrace in Greece. The optimum solution resulted in one treatment plant located in the sanitary landfill area of Chrysoupolis, required no transfer stations and had a total management cost of 38,800 €/month or 809 €/t. If a treatment plant is sited in the most eastern part of the Region, i.e., the industrial area of Alexandroupolis, the optimum solution would result in a transfer station of 23 m{sup 3}, located near Kavala General Hospital, and a total cost of 39,800 €/month or 831 €/t. A sensitivity analysis was conducted and two alternative scenarios were optimized. In the first scenario, a 15% rise in fuel cost and in the second scenario a 25% rise in IMW production were considered. At the end, a cost calculation in €/t/km for every type of vehicle used for haul and transfer was conducted. Also, the cost of the whole system was itemized and calculated in €/t/km and €/t. The results showed that the higher percentage of the total cost was due to the construction of the treatment plant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices

Variance-based sensitivity analysis provides a quantitative measure of how uncertainty in a model input contributes to uncertainty in the model output. Such sensitivity analyses arise in a wide variety of applications and are typically computed using Monte Carlo estimation, but the many samples required for Monte Carlo to be sufficiently accurate can make these analyses intractable when the model is expensive. This paper presents a multifidelity approach for estimating sensitivity indices that leverages cheaper low-fidelity models to reduce the cost of sensitivity analysis while retaining accuracy guarantees via recourse to the original, expensive model. This paper develops new multifidelity estimators for variance and for the Sobol' main and total effect sensitivity indices. We discuss strategies for dividing limited computational resources among models and specify a recommended strategy. Results are presented for the Ishigami function and a convection-diffusion-reaction model that demonstrate up to $10\times$ speedups for fixed convergence levels. Finally, for the problems tested, the multifidelity approach allows inputs to be definitively ranked in importance when Monte Carlo alone fails to do so.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

A Reliability Model to Assess Reliability of Shuttle Derived Launch Vehicle and Next Generation Vehicles

One important thrust in NASA s space exploration vision is to improve the safety and reliability of future launch vehicles. A quantitative reliability model is an essential technical tool to evaluate launch vehicle reliability and to enhance launch vehicle configuration down selection and detailed designs. A quantitative reliability model that supports NASA's thrust has been developed and implemented in an MS Excel spreadsheet. The model addresses key reliability parameters, and provides for sensitivity analysis of various vehicle designs. This paper presents the specific elements of the model and examples of selected sensitivity analysis results. Reliability input parameters in the model include the following: 1. Main propulsion element failure probability; 2. Propulsion element catastrophic failure fraction (Cf). This is defined as the ratio of probability of uncontained failures over sum of probability of uncontained failures and contained failures); 3. Engine-out design vs. no-engine-out design; 4. Engine with redlines vs. no redlines; 5. Different levels of health management system implementation; 6. Launch Escape System (LES) reliability; and 7. Power level correlation with reliability. The sensitivity results will illustrate the impact of each of the above parameters on several reliability metrics: Loss of Mission (LOM), LOV (Loss of Vehicle) and Loss of Crew (LOC).

Huang, Zhao↗
DOE OSTI2014

Cost-Effectiveness Analysis of Intensity Modulated Radiation Therapy Versus 3-Dimensional Conformal Radiation Therapy for Anal Cancer

Purpose: To compare the cost-effectiveness of intensity modulated radiation therapy (IMRT) and 3-dimensional conformal radiation therapy (3D-CRT) for anal cancer and determine disease, patient, and treatment parameters that influence the result. Methods and Materials: A Markov decision model was designed with the various disease states for the base case of a 65-year-old patient with anal cancer treated with either IMRT or 3D-CRT and concurrent chemotherapy. Health states accounting for rates of local failure, colostomy failure, treatment breaks, patient prognosis, acute and late toxicities, and the utility of toxicities were informed by existing literature and analyzed with deterministic and probabilistic sensitivity analysis. Results: In the base case, mean costs and quality-adjusted life expectancy in years (QALY) for IMRT and 3D-CRT were $32,291 (4.81) and $28,444 (4.78), respectively, resulting in an incremental cost-effectiveness ratio of $128,233/QALY for IMRT compared with 3D-CRT. Probabilistic sensitivity analysis found that IMRT was cost-effective in 22%, 47%, and 65% of iterations at willingness-to-pay thresholds of $50,000, $100,000, and $150,000 per QALY, respectively. Conclusions: In our base model, IMRT was a cost-ineffective strategy despite the reduced acute treatment toxicities and their associated costs of management. The model outcome was sensitive to variations in local and colostomy failure rates, as well as patient-reported utilities relating to acute toxicities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Design and execution of a verification, validation, and uncertainty quantification plan for a numerical model of left ventricular flow after LVAD implantation

Left ventricular assist devices (LVADs) are implantable pumps that act as a life support therapy for patients with severe heart failure. Despite improving the survival rate, LVAD therapy can carry major complications. Particularly, the flow distortion introduced by the LVAD in the left ventricle (LV) may induce thrombus formation. While previous works have used numerical models to study the impact of multiple variables in the intra-LV stagnation regions, a comprehensive validation analysis has never been executed. The main goal of this work is to present a model of the LV-LVAD system and to design and follow a verification, validation and uncertainty quantification (VVUQ) plan based on the ASME V&V40 and V&V20 standards to ensure credible predictions. The experiment used to validate the simulation is the SDSU cardiac simulator, a bench mock-up of the cardiovascular system that allows mimicking multiple operation conditions for the heart-LVAD system. The numerical model is based on Alya, the BSC’s in-house platform for numerical modelling. Alya solves the Navier-Stokes equation with an Arbitrary Lagrangian-Eulerian (ALE) formulation in a deformable ventricle and includes pressure-driven valves, a 0D Windkessel model for the arterial output and a LVAD boundary condition modeled through a dynamic pressure-flow performance curve. The designed VVUQ plan involves: (a) a risk analysis and the associated credibility goals; (b) a verification stage to ensure correctness in the numerical solution procedure; (c) a sensitivity analysis to quantify the impact of the inputs on the four quantities of interest (QoIs) (average aortic root flow $Q^{avg}_{Ao}$, maximum aortic root flow $Q^{max}_{Ao}$, average LVAD flow $Q^{avg}_{VAD}$, and maximum LVAD flow $Q^{max}_{VAD}$; (d) an uncertainty quantification using six validation experiments that include extreme operating conditions. Numerical code verification tests ensured correctness of the solution procedure and numerical calculation verification showed a grid convergence index (GCI)95% <3.3%. The total Sobol indices obtained during the sensitivity analysis demonstrated that the ejection fraction, the heart rate, and the pump performance curve coefficients are the most impactful inputs for the analysed QoIs. The Minkowski norm is used as validation metric for the uncertainty quantification. It shows that the midpoint cases have more accurate results when compared to the extreme cases. The total computational cost of the simulations was above 100 [core-years] executed in around three weeks time span in Marenostrum IV supercomputer. This work details a novel numerical model for the LV-LVAD system, that is supported by the design and execution of a VVUQ plan created following recognised international standards. We present a methodology demonstrating that stringent VVUQ according to ASME standards is feasible but computationally expensive.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Herbaceous Feedstock 2018 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations, and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible, and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2018 Herbaceous SOT aligned feedstock logistic design with current biorefinery’s design capacity utilized by biochemical conversion platform. Currently biochemical conversion platform utilizes a 725,000 dry ton/year biorefiney design for the techno economic analysis. Hence, feedstock delivered cost in the 2018 Herbaceous SOT is calculated based on biorefinery’s 725,000 dry ton design capacity instead of 800, 000 dry ton capacity utilized in the 2017 Herbaceous SOT. Biomass availabilities in this SOT were updated to year 2018 data from the 2016 Billion-Ton Report (BT16) (DOE 2016a), with the exception of switchgrass, for which the 2018 Herbaceous SOT utilized the 2019 switchgrass availability data from BT16. The BT16 report (DOE 2016a) does not project switchgrass availability in 2018; the soonest switchgrass is available in the BT16 report is 2019. Therefore, availability of switchgrass for this analysis was that projected for 2019. The 2018 Herbaceous SOT incorporates same technologies utilized in the 2017 Herbaceous SOT. However, a sensitivity analysis is performed to understand the impact of variation of process parameters on those technologies on feedstock logistic cost. New R&D data that shows the variations of process parameters affecting process performance is incorporated in the 2018 SOT to measure the variations in delivered feedstock cost. The 2018 Herbaceous SOT has also provided projected delivered feedstock of 2022 design case based on near term technical target under BETO funded R&D project. Finally, updated biorefinery size of 725,000 dry ton/year was incorporated within least-cost formulation model to select optimal siting and depot scales during optimization of the least cost blend. This modification to the optimization algorithm allows the trade-off between the cost of increased supply radius and the savings from selecting biomass from higher producing counties to be assessed. Such optimization has also showed the economic benefit of decentralized depots in comparison to centralized preprocessing co-located with the biorefinery by decoupling the biorefinery and feedstock locations. The 2018 Herbaceous SOT report documents the current modeled cost of a herbaceous feedstock supply system (from harvest to the pretreatment reactor throat, including grower payment) for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2018 Herbaceous SOT predicts a modeled delivered feedstock cost of $83.67/dry ton (2016$); this is a $0.23/dry ton (2016$) decrease from the 2017 Herbaceous SOT. The modification of biorefinery’s designed capacity and increased projected biomass availability in the same supply shed contributed to this modeled cost reduction. The least-cost formulation model to optimally site and scale local distributed preprocessing depots also contributed to the cost reduction by considering county-level grower payment and distance from the biorefinery as variables in the optimization algorithm. Sensitivity analysis on various process parameters that affect delivered feedstock cost in the 2018 Herbaceous SOT shows that the delivered cost could varies from $80.45-$88.83/dry ton. The top factors that causes such variations are: effective baling rate, bale density, hammer mill throughput, interest rate and storage dry matter loss.

09 BIOMASS FUELS↗