Search NASA⌕ Search

SEARCH · Search NASA

Results for “Robustness analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-J-182: Investigation of Systems for Improved Accuracy in Clinical Y-90 Percent Delivered Calculations

Purpose: Y-90 Selective Internal Radiation Therapy (SIRT) is used to treat liver tumors, and by nature has variability in the percent of the intended dose that is actually delivered. To determine the quality of the administration, pre and post activity measurements are taken, and used to infer percent delivered. Vendor specifications indicate the use of an ion chamber to take these measurements. In our work, we investigated the accuracy of ion chambers, and compared them to other detector systems. Methods: We have built phantoms, phantom holders, and protocols, which allow us to measure our Y90 doses with varying apparatuses: a dose calibrator, a Geiger-counter, an ion chamber, a crystal based thyroid probe, and a gamma camera. We have set up a system that has enabled us to gather data by measuring clinical Y90 doses as they are used in the clinic using all of the instrumental methods. Five initial doses (25 measurements/acquisitions) have been taken at the time of this abstract submission. Results: Our initial results show that measurements acquired using scintillation based detectors (thyroid probe and gamma camera) correlate better with the gold standard (i.e. the dose calibrator). Pearson correlations between the dose calibrator measurements and the GM counter, Ion chamber, thyroid probe, and gamma camera were found to be 0.88, 0.83, 0.98, 0.99, respectively. More acquisitions and analysis are planned to determine the precision of the systems, as well as optimal energy window settings. Conclusion: It is likely that current standard practice can be improved using scintillation crystal based detectors. Such systems are more sensitive, can integrate signal, and can use energy discrimination. Furthermore, phantoms can be built to integrate with probe and gamma camera systems that are robust and provide reproducibility. Future work will include expanded acquisition and analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

FRIEDA: Flexible Robust Intelligent Elastic Data Management Framework

Scientific applications are increasingly using cloud resources for their data analysis workflows. However, managing data effectively and efficiently over these cloud resources is challenging due to the myriad storage choices with different performance, cost trade-offs, complex application choices and complexity associated with elasticity, failure rates in these environments. The different data access patterns for data-intensive scientific applications require a more flexible and robust data management solution than the ones currently in existence. FRIEDA is a Flexible Robust Intelligent Elastic Data Management framework that employs a range of data management strategies in cloud environments. FRIEDA can manage storage and data lifecycle of applications in cloud environments. There are four different stages in the data management lifecycle of FRIEDA – (i) storage planning, (ii) provisioning and preparation, (iii) data placement, and (iv) execution. FRIEDA defines a data control plane and an execution plane. The data control plane defines the data partition and distribution strategy, whereas the execution plane manages the execution of the application using a master-worker paradigm. FRIEDA also provides different data management strategies, either to partition the data in real-time, or predetermine the data partitions prior to application execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Constructing Data-Driven Predictions at the Far Detector for NOvA's Neutrino Oscillation Analysis.

NOvA, is a two-detector, long-baseline neutrino oscillation experiment located at Fermilab, Batavia, IL, USA. It is designed primarily to constrain neutrino oscillation parameters using $\nu_\mu \ (\bar{\nu}_\mu)$ disappearance and $\nu_e \ (\bar{\nu}_e)$ appearance data. The Neutrinos at Main Injector (NuMI) beamline at Fermilab provides a high purity 900 KW intense beam of neutrinos and anti-neutrinos to NOvA. The NOvA Near Detector, located 100m underground and 1km away from the beam source, observes the un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ and beam $\nu_e \ (\bar{\nu}_e)$ event spectrum. The Far Detector, located in Ash River, MN, USA, is 809 km from the ND and records the oscillated $\nu_e \ (\bar{\nu}_e)$ and the un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ event spectrum. NOvA uses a data-driven technique called extrapolation to predict the expected number of $\nu_\mu \ (\bar{\nu}_\mu)$ and $\nu_e \ (\bar{\nu}_e)$ events at the Far Detector using the Near Detector data. The use of data from a functionally equivalent Near Detector provides a powerful constraint on the systematic uncertainties in NOvA neutrino oscillation analyses. As NOvA continues to add data statistics, a robust constraint on systematics becomes more crucial for neutrino oscillation analysis. The details of the NOvA neutrino oscillation analysis framework and how it constrains dominant systematic uncertainties using the Near Detector data will be discussed in this poster.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate-related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Robust Data-Driven Predictive Run-to-Run Control for Automated Serial Sectioning

This letter presents a one-step predictive run-to-run controller (R2R-MPC) for the automation of mechanical serial sectioning (MSS), a destructive material analysis process. To address the inherent uncertainty and disturbances in the MSS process, a robust closed-loop approach is presented. Here, the robust R2R-MPC models the uncertainty of the MSS process using a linear differential inclusion. As an analytical model of the MSS process is unavailable, the differential inclusion is identified from historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to the control input which minimize the worst-case material removal errors. This optimization-based controller is combined with a run-to-run controller to provide integral action that rejects constant disturbances and tracks constant reference removal rates. To demonstrate the efficacy of our robust R2R-MPC, we present simulation results which compare the presented controller with a conventional non-robust R2R.

42 ENGINEERING↗
DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Superpixel-Augmented Endmember Detection for Hyperspectral Images

Superpixels are homogeneous image regions comprised of several contiguous pixels. They are produced by shattering the image into contiguous, homogeneous regions that each cover between 20 and 100 image pixels. The segmentation aims for a many-to-one mapping from superpixels to image features; each image feature could contain several superpixels, but each superpixel occupies no more than one image feature. This conservative segmentation is relatively easy to automate in a robust fashion. Superpixel processing is related to the more general idea of improving hyperspectral analysis through spatial constraints, which can recognize subtle features at or below the level of noise by exploiting the fact that their spectral signatures are found in neighboring pixels. Recent work has explored spatial constraints for endmember extraction, showing significant advantages over techniques that ignore pixels relative positions. Methods such as AMEE (automated morphological endmember extraction) express spatial influence using fixed isometric relationships a local square window or Euclidean distance in pixel coordinates. In other words, two pixels covariances are based on their spatial proximity, but are independent of their absolute location in the scene. These isometric spatial constraints are most appropriate when spectral variation is smooth and constant over the image. Superpixels are simple to implement, efficient to compute, and are empirically effective. They can be used as a preprocessing step with any desired endmember extraction technique. Superpixels also have a solid theoretical basis in the hyperspectral linear mixing model, making them a principled approach for improving endmember extraction. Unlike existing approaches, superpixels can accommodate non-isometric covariance between image pixels (characteristic of discrete image features separated by step discontinuities). These kinds of image features are common in natural scenes. Analysts can substitute superpixels for image pixels during endmember analysis that leverages the spatial contiguity of scene features to enhance subtle spectral features. Superpixels define populations of image pixels that are independent samples from each image feature, permitting robust estimation of spectral properties, and reducing measurement noise in proportion to the area of the superpixel. This permits improved endmember extraction, and enables automated search for novel and constituent minerals in very noisy, hyperspatial images. This innovation begins with a graph-based segmentation based on the work of Felzenszwalb et al., but then expands their approach to the hyperspectral image domain with a Euclidean distance metric. Then, the mean spectrum of each segment is computed, and the resulting data cloud is used as input into sequential maximum angle convex cone (SMACC) endmember extraction.

Thompson, David R.↗
DOE OSTI2016

AGR-1, AGR-2 and AGR-3/4 Dimensional Change Data Analysis

A series of Advanced Gas Reactor (AGR) experiments have been completed in the Advanced Test Reactor at Idaho National Laboratory in support of qualification and development of tristructural isotropic fuel. Each AGR test consists of multiple independently controlled and monitored capsules containing fuel compacts placed in a graphite cylinder. These capsules are instrumented with thermocouples embedded in the graphite, enabling temperature control. The fuel compacts are composed of fuel particles surrounded by a graphitic A3 matrix material. Dimensional change in AGR fuel compacts is vital because the swelling or shrinkage affects the size of the gas gaps that are used to control temperatures. Analysis of dimensional change in the AGR fuel compacts is needed to establish the variables directly relating to compact shrinkage. The variables initially identified for consideration were matrix density, compact density, fuel packing fraction, uranium loading, fuel particle diameter, cumulative fast neutron fluence, and volume average time average fuel temperature. In addition to the data available from the AGR experiments, the analysis included specimens formed from the same A3 matrix material used in Advanced Graphite Creep (AGC) experiments, which provide graphite creep data during irradiation for design and licensing purposes. The primary purpose of including the AGC specimens was to encompass dimensional behavior at zero packing fraction, zero uranium loading, and zero particle diameter. All possible combinations of first-order variable regressions were considered in the analysis. The study focused on identifying the best regression models for percent change in diameter, length, and volume. Bootstrap analysis was used to ensure the resulting regression models were robust and well-performing. The variables identified as very significant in predicting change in one or more dimensions (diameter, length, and volume) are volume average time average temperature, fast fluence, compact density, packing fraction and fuel particle diameter. Due to the presence of confounding effects between several variables, interpretation of these results is equivocal; the use of multiple statistical tests provides additional confidence in the conclusion.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Quantifying replication through repeated analysis of UVM-A, a liquid reference material for cosmogenic 10 Be and 26 Al studies

In this study, in-situ produced cosmogenic nuclide sample preparation and analysis is a complicated, multi-step process with numerous possible sources of error, many of which have not been robustly quantified. Here, we use a liquid reference material (UVM-A) to test whether the analytic precision associated with individual isotopic ratios (n = 96) measured at two Accelerator Mass Spectrometry facilities (Lawrence Livermore National Laboratory and Purdue Rare Isotope Measurement Laboratory) accurately represents the range of values determined for aliquots prepared in a single laboratory over a period of five years. We find that 10 Be/ 9 Be ratios measured at the different accelerator facilities (n = 26 and n = 70) have statistically indistinguishable central tendencies, matching to within 0.1 %. Based on this large dataset, we suggest preliminary consensus values for UVM-A of 1.45 ± 0.06 x 10 -13 for 10 Be/ 9 Be (mean, one standard deviation, n = 96) and 4.47 ± 0.22 x 10 -13 for 26 Al/ 27 Al (mean, one standard deviation, n = 27). For both 10 Be/ 9 Be and 26 Al/ 27 Al, the relative standard deviations of the measured ratios are similar to the reported analytic uncertainties of sample measurement, suggesting that sample preparation introduced little if any additional scatter beyond the uncertainty of isotopic analysis. This dataset demonstrates that robust sample preparation and measurement can generate isotope ratio data reproducible at the level of counting statistics.

58 GEOSCIENCES↗
DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗
DOE OSTI2017

Robust X-ray angular correlations for the study of meso-structures

As self-assembling nanomaterials become more sophisticated, it is becoming increasingly important to measure the structural order of finite-sized assemblies of nano-objects. These mesoscale clusters represent an acute challenge to conventional structural probes, owing to the range of implicated size scales (10 nm to several micrometres), the weak scattering signal and the dynamic nature of meso-clusters in native solution environments. The high X-ray flux and coherence of modern synchrotrons present an opportunity to extract structural information from these challenging systems, but conventional ensemble X-ray scattering averages out crucial information about local particle configurations. Conversely, a single meso-cluster scatters too weakly to recover the full diffraction pattern. Using X-ray angular cross-correlation analysis, it is possible to combine multiple noisy measurements to obtain robust structural information. This paper explores the key theoretical limits and experimental challenges that constrain the application of these methods to probing structural order in real nanomaterials. A metric is presented to quantify the signal-to-noise ratio of angular correlations, and it is used to identify several experimental artifacts that arise. In particular, it is found that background scattering, data masking and inter-cluster interference profoundly affect the quality of correlation analyses. A robust workflow is demonstrated for mitigating these effects and extracting reliable angular correlations from realistic experimental data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Design of Launch Vehicle Flight Control Systems Using Ascent Vehicle Stability Analysis Tool

A launch vehicle represents a complicated flex-body structural environment for flight control system design. The Ascent-vehicle Stability Analysis Tool (ASAT) is developed to address the complicity in design and analysis of a launch vehicle. The design objective for the flight control system of a launch vehicle is to best follow guidance commands while robustly maintaining system stability. A constrained optimization approach takes the advantage of modern computational control techniques to simultaneously design multiple control systems in compliance with required design specs. "Tower Clearance" and "Load Relief" designs have been achieved for liftoff and max dynamic pressure flight regions, respectively, in the presence of large wind disturbances. The robustness of the flight control system designs has been verified in the frequency domain Monte Carlo analysis using ASAT.

Jang, Jiann-Woei↗
DOE OSTI2016

A Higher Fidelity Cost Analysis of Wind and Uranium from Seawater Acquisition symbiotic Infrastructure

The vastness of the worlds oceans makes the low concentration of naturally existing uranium, 3.3 ppb, appealing as a recoverable natural resource. Although ample uranium is available currently through conventional means, uranium from seawater is a widely studied topic due to the long term security it can assure the nuclear fuel cycle. It is important to mention that seawater uranium is not meant to act as a direct competitor to terrestrial uranium, but instead create a cost ceiling that provides economic stability and potential savings. Additionally, seawater uranium could circumvent some of the environmental impacts associated with recovery of any land based resource. The system currently studied by a nation-wide consortium of national lab and university partners involves the passive recovery of uranium using polymer based adsorbents. High Density Polyethylene fibers undergo a radiation induced graft co-polymerization process involving amidoxime, to attract uranium, and a polar co-monomer to increase hydrophilicity. After further chemical conditioning adsorbent fibers are braided into 60 meter strands for marine deployment. Braids are moored to the ocean floor for the duration of their soaking campaign. After sufficient seawater exposure they are winched up so the adsorbed uranium may be eluted off the braids. This deployment and elution process is repeated multiple times before the adsorbent's ultimate disposal, where its lifetime is dictated by the degradation it suffers with each re-cycle. Previous economic analyses have identified the adsorbent production and mooring as the most expensive components of the recovery process. Therefore, a later publication by Picard et al. proposed an alternative deployment method in an effort to reduce seawater uranium production cost. The Wind and Uranium from Seawater Acquisition symbiotic Infrastructure (WUSABI) couples uranium recovery with off shore wind to reduce mooring capital cost. The uranium harvesting structure attached to the underwater base of the wind turbine supports elution tanks and an adsorbent pulley system. This allows for a nearly autonomous mooring and elution procedure as adsorbent is in constant motion, reaching the elution tanks at the end of its soaking campaign. Although the original publication did include a short economic section, further analysis was carried out to provide a higher fidelity production cost estimate for uranium recovered by this system. Sufficient detail was provided regarding the capital and operating costs of the novel uranium harvesting units. The adsorbent production and uranium elution and purification steps however were estimated by simpler methods, for example applying a previously reported cost for producing a unit mass of adsorbent. This methodology, although satisfactory for the zeroth-order approximation desired in the original publication, overlooks many of the complexities and feedbacks embedded in the full cost model, which are necessary to provide a more accurate estimate. Perhaps more importantly, given the constant updates regarding adsorbent synthesis and performance this value quickly become outdated. Syncing an improved estimate for the WUSABI harvesting units with the existing economic analysis for adsorbent production and elution, which evolves in tandem with the technology, provides a more robust production cost estimate that will remain current over time. In addition to the updates and higher level of detail provided in this independent analysis, consistency in methodology allows for accurate comparison of uranium production costs across recovery methods. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS2012

Developing the Next Generation Shell Buckling Design Factors and Technologies

NASA s Shell Buckling Knockdown Factor (SBKF) Project was established in the spring of 2007 by the NASA Engineering and Safety Center (NESC) in collaboration with the Constellation Program and Exploration Systems Mission Directorate. The SBKF project has the current goal of developing less-conservative, robust shell buckling design factors (a.k.a. knockdown factors) and design and analysis technologies for light-weight stiffened metallic launch vehicle (LV) structures. Preliminary design studies indicate that implementation of these new knockdown factors can enable significant reductions in mass and mass-growth in these vehicles and can help mitigate some of NASA s LV development and performance risks. In particular, it is expected that the results from this project will help reduce the reliance on testing, provide high-fidelity estimates of structural performance, reliability, robustness, and enable increased payload capability. The SBKF project objectives and approach used to develop and validate new design technologies are presented, and provide a glimpse into the future of design of the next generation of buckling-critical launch vehicle structures.

Hilburger, Mark W.↗
DOE OSTI2016

NUHOMS{sup R} EOS (Extended Optimized Storage): Robust, Advanced, and Dual Purpose Dry Storage System - 16472

AREVA TN has completed the design, analysis, and US NRC license submittal for the NUHOMS{sup R} Extended Optimized Storage (EOS) system. NUHOMS{sup R} EOS offers a large capacity and high heat load dry storage and transportation solution for UNF. EOS is not a new system, but an evolution of the already-proven safe and low-risk horizontal NUHOMS{sup R} storage system, which is one of the most widely used UNF dry storage systems in the United States. The EOS system features a 37PTH dry shielded canister (DSC) for 37 PWR UNF assemblies and an 89BTH DSC for the 89 BWR UNF assemblies. The EOS DSCs will be stored inside a horizontal storage module (HSM) that is a robust, fortress-like concrete storage overpack. Designed with the lessons learned from the Fukushima event in Japan in mind, EOS has robust margins for beyond design basis (BDB) events and offers the lowest risk and highest safety margin of any dry storage system design for recovery from BDB events. EOS also offers innovative aging management features with an aging management program (AMP) to assure safe and low dose long-term storage at the utility sites. EOS incorporates ports for AMP inspections and offers the option of using duplex stainless steel (DSS) for the DSC shell in order to negate corrosion issues. EOS is stored horizontally and above ground, making it easy and quick to access for visual inspection, monitoring, and mitigating actions during AMP. NUHOMS{sup R} EOS is optimized for high capacity, high burnup, high enrichments, and very low cooling times for UNF assemblies. It enables the highest heat load per canister (50 kW) and low dose rates. Even with high heat loads, EOS has significant margins to design basis limits. As the future of used fuel disposition will require the need to move fuel off-site to interim or consolidated storage, EOS is the ideal solution as it enables on-site loading and unloading operations that mitigate risk when transferring fuel on-site or transporting fuel off-site. The EOS design can be safely located in a traditional outdoor setting with fortress-like concrete reinforced modules that offer superior resistance to attack or can be safely operated inside of a building enclosure for those utilities desiring a more discreet siting for its used fuel storage systems. EOS has an innovative basket design that is made of high strength low alloy (HSLA) material and is easy to fabricate due to its non-welded design. Its innovative inlet and outlet air vent design, which incorporates dose reduction hardware, allows for optimal ambient airflow for highly efficient heat transfer with minimal radiation dose. The NUHOMS{sup R} EOS storage modules are interlocked in a side-by-side configuration offering superior and robust seismic performance. The license application for NUHOMS{sup R} EOS is currently under US NRC review and this system will be available for deployment in 2017. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Deep Direct-Use Feasibility Study Numerical Modeling and Uncertainty Analysis using iTOUGH2 for West Virginia University

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. As a result, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided to use single permeability model for further analysis; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the perdition, therefore, is considered in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis are used for economic analysis. The dataset submitted here support the described study. Manuscript is submitted to Geothermics, will be linked once paper is accepted.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

EVOLUTION OF NEAR-SURFACE FLOWS INFERRED FROM HIGH-RESOLUTION RING-DIAGRAM ANALYSIS

Ring-diagram analysis of acoustic waves observed at the photosphere can provide a relatively robust determination of the sub-surface flows at a particular time under a particular region. The depth of penetration of the waves is related to the size of the region, hence the depth extent of the measured flows is inversely proportional to the spatial resolution. Most ring-diagram analysis has focused on regions of extent ∼15° (180 Mm) or more in order to provide reasonable mode sets for inversions. Helioseismic and Magnetic Imager (HMI) data analysis also provides a set of ring fit parameters on a scale three times smaller. These provide flow estimates for the outer 1% (7 Mm) of the Sun only, with very limited depth resolution, but with spatial resolution adequate to map structures potentially associated with the belts and regions of magnetic activity. There are a number of systematic effects affecting the determination of flows from a local helioseismic analysis of regions over different parts of the observable disk, and not all of them are well understood. In this study we characterize those systematic effects with higher spatial resolution so that they may be accounted for more effectively in mapping the temporal and spatial evolution of the flows. Leaving open the question of the mean structure of the global meridional circulation and the differential rotation, we describe the near-surface flow anomalies in time and latitude corresponding to the torsional oscillation pattern in differential rotation and analogous patterns in the meridional cell structure as observed by the Solar Dynamics Observatory/HMI.

79 ASTRONOMY AND ASTROPHYSICS↗