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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Benders Decomposition Using Graph Modeling and Multi-Parametric Programming

Benders decomposition is a widely used method for solving large and structured optimization problems, but its performance is affected by the repeated solution of subproblems. We propose a flexible and modular algorithmic framework for accelerating Benders decomposition. Specifically, we express the problem structure by using a graph-theoretic modeling abstraction in which nodes represent optimization subproblems and edges represent connectivity between subproblems. A key innovation of our approach is that we embed multiparametric programming (mp) surrogates for node subproblems, which maps the exact analytical map of the subproblem solution space. The use of mp surrogates allows us to replace subproblem solves with fast look-ups and function evaluations for primal and dual variables during the iterative Benders process. We formally show the equivalence between classical Benders cuts and those derived from the mp solution. We implement our framework in the open-source PlasmoBenders.jl software package. To demonstrate the capabilities of the proposed framework, we apply it to a two-stage stochastic programming problem, which aims to make optimal capacity expansion decisions under market uncertainty. We evaluate both single-cut and multicut variants of Benders decomposition and show that the use of mp surrogates achieves substantial speedups in subproblem solve time, while preserving the convergence guarantees of Benders decomposition. We highlight advantages in solution analysis and interpretability that is enabled by mp critical region tracking; specifically, we show that these reveal how decisions evolve geometrically across the Benders search. Our results aim to demonstrate that combining surrogate modeling with graph modeling offers a promising and extensible foundation for structure-exploiting decomposition. In addition, by decomposing the problem into more tractable subproblems, the proposed approach also aims to overcome scalability issues of mp. Finally, the use of mp surrogates provides a unifying and modular optimization framework that enables the representation of heterogeneous node subproblems as modeling objects with a homogeneous structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Experimental system for studying temperature gradient-driven fracture of oxide nuclear fuel out of reactor

Temperature gradients in ceramic light water reactor (LWR) uranium dioxide (UO 2 ) nuclear fuel pellets generate thermal stresses that cause fractures in the fuel beginning early in the life of fresh fuel. The combination of heating due to fission and forced convective cooling on the exterior of LWR fuel rods generates a temperature profile that is difficult to replicate outside the reactor environment. In the present study, a state-of-the-art experimental set-up using electrical heating to replicate fission heating was built and surrogate fuel materials such as ceria (CeO 2 ) were used to validate the system. Cracking experiments were conducted on these surrogates by inducing reactivity-initiated-accident (RIA) like temperature gradients in the pellets via induction and direct resistance heating. Induction heating was done using copper coils and molybdenum susceptors which heated the surrogates to a threshold temperature that is sufficiently high for the fuel material to conduct current. Thereafter, direct resistance heating was used by a D.C. power supply to introduce volumetric heating to replicate LWR operating conditions analogous to fission heating. The pellets were held against nickel electrodes and mounted on a boron nitride test-stand. All the tests were carried out in a stainless-steel vacuum chamber. Simultaneous real-time dual imaging of the surrogate pellet surface has been implemented using an optical and infrared camera system which will be mounted along axial and perpendicular directions to the pellet surface respectively. A beam-splitter was used to split the incoming radiation from the sample into two halves. While one of the beams is transmitted from the splitter through a bandpass filter to obtain optical images, the other beam is reflected from the splitter to the thermal camera to capture full field temperature gradients of the as fabricated pellet surface during crack initiation and propagation. In the current series of tests, a 2-color pyrometer was used for recording and comparing the surface and centerline temperatures of the surrogate pellets in lieu of the thermal camera. A LabVIEW data acquisition system has been set up for collecting useful data during experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. More accurate simulations with fast electron diffusion are even slower, requiring multiple hours of run time with parallel processing. The machine learning models use a database of 16,000+ GEN-RAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods implemented in πScope ensure that the database covers the range of 9 input parameters (n e0 , T e0 , I p , B t , R 0 , n ∥︀ , Z e f f , V loop , P LHCD ) with sufficient density in all regions of parameter space. The surrogate models reduce the computation time from minutes-hours to ms with high accuracy across the input parameter space. Data-driven surrogate models also allow for solving inverse and “lateral” problems. A surrogate model for the inverse problem maps from a desired current drive or power deposition profile to a set of input parameters that would result in such a profile, while a surrogate model for the lateral problem maps from a measured experimental quantity such as hard x-ray emission to a current drive or power deposition profile. In conclusion, the πScope database creation workflow is flexible and applicable to other RF simulation codes such as TORIC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

Here this paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

4D tumor centroid tracking using orthogonal 2D dynamic MRI: Implications for radiotherapy planning

Purpose: Current pretreatment, 4D imaging techniques are suboptimal in that they sample breathing motion over a very limited “snapshot” in time. Heretofore, long-duration, 4D motion characterization for radiotherapy planning, margin optimization, and validation have been impractical for safety reasons, requiring invasive markers imaged under x-ray fluoroscopy. To characterize 3D tumor motion and associated variability over durations more consistent with treatments, the authors have developed a practical dynamic MRI (dMRI) technique employing two orthogonal planes acquired in a continuous, interleaved fashion.Methods: 2D balanced steady-state free precession MRI was acquired continuously over 9–14 min at approximately 4 Hz in three healthy volunteers using a commercial 1.5 T system; alternating orthogonal imaging planes (sagittal, coronal, sagittal, etc.) were employed. The 2D in-plane pixel resolution was 2 × 2 mm{sup 2} with a 5 mm slice profile. Simultaneous with image acquisition, the authors monitored a 1D surrogate respiratory signal using a device available with the MRI system. 2D template matching-based anatomic feature registration, or tracking, was performed independently in each orientation. 4D feature tracking at the raw frame rate was derived using spline interpolation.Results: Tracking vascular features in the lung for two volunteers and pancreatic features in one volunteer, the authors have successfully demonstrated this method. Registration error, defined here as the difference between the sagittal and coronal tracking result in the SI direction, ranged from 0.7 to 1.6 mm (1σ) which was less than the acquired image resolution. Although the healthy volunteers were instructed to relax and breathe normally, significantly variable respiration was observed. To demonstrate potential applications of this technique, the authors subsequently explored the intrafraction stability of hypothetical tumoral internal target volumes and 3D spatial probability distribution functions. The surrogate respiratory information allowed the authors to show how this technique can be used to study correlations between internal and external (surrogate) information over these prolonged durations. However, compared against the gold standard of the time stamps in the dMRI frames, the temporal synchronization of the surrogate 1D respiratory information was shown to be likely unreliable.Conclusions: The authors have established viability of a novel and practical pretreatment, 4D tumor centroid tracking method employing a commercially available dynamic MRI sequence. Further developments from the vendor are likely needed to provide a reliably synchronized surrogate 1D respiratory signal, which will likely broaden the utility of this method in the pretreatment radiotherapy planning context.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Adaptation of the modified Bouc–Wen model to compensate for hysteresis in respiratory motion for the list-mode binning of cardiac SPECT and PET acquisitions: Testing using MRI

Purpose: Binning list-mode acquisitions as a function of a surrogate signal related to respiration has been employed to reduce the impact of respiratory motion on image quality in cardiac emission tomography (SPECT and PET). Inherent in amplitude binning is the assumption that there is a monotonic relationship between the amplitude of the surrogate signal and respiratory motion of the heart. This assumption is not valid in the presence of hysteresis when heart motion exhibits a different relationship with the surrogate during inspiration and expiration. The purpose of this study was to investigate the novel approach of using the Bouc–Wen (BW) model to provide a signal accounting for hysteresis when binning list-mode data with the goal of thereby improving motion correction. The study is based on the authors’ previous observations that hysteresis between chest and abdomen markers was indicative of hysteresis between abdomen markers and the internal motion of the heart. Methods: In 19 healthy volunteers, they determined the internal motion of the heart and diaphragm in the superior–inferior direction during free breathing using MRI navigators. A visual tracking system (VTS) synchronized with MRI acquisition tracked the anterior–posterior motions of external markers placed on the chest and abdomen. These data were employed to develop and test the Bouc–Wen model by inputting the VTS derived chest and abdomen motions into it and using the resulting output signals as surrogates for cardiac motion. The data of the volunteers were divided into training and testing sets. The training set was used to obtain initial values for the model parameters for all of the volunteers in the set, and for set members based on whether they were or were not classified as exhibiting hysteresis using a metric derived from the markers. These initial parameters were then employed with the testing set to estimate output signals. Pearson’s linear correlation coefficient between the abdomen, chest, average of chest and abdomen markers, and Bouc–Wen derived signals versus the true internal motion of the heart from MRI was used to judge the signals match to the heart motion. Results: The results show that the Bouc–Wen model generated signals demonstrated strong correlation with the heart motion. This correlation was slightly larger on average than that of the external surrogate signals derived from the abdomen marker, and average of the abdomen and chest markers, but was not statistically significantly different from them. Conclusions: The results suggest that the proposed model has the potential to be a unified framework for modeling hysteresis in respiratory motion in cardiac perfusion studies and beyond.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

An Adaptive Sparse-Grid High-Order Stochastic Collocation Method for Bayesian Inference in Groundwater Reactive Transport Modeling

Although Bayesian analysis has become vital to the quantification of prediction uncertainty in groundwater modeling, its application has been hindered due to the computational cost associated with numerous model executions needed for exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, we utilize a compactly supported higher-order hierar- chical basis to construct the surrogate system, resulting in a significant reduction in the number of computational simulations required. In addition, we use hierarchical surplus as an error indi- cator to determine adaptive sparse grids. This allows local refinement in the uncertain domain and/or anisotropic detection with respect to the random model parameters, which further improves computational efficiency. Finally, we incorporate a global optimization technique and propose an iterative algorithm for building the surrogate system for the PPDF with multiple significant modes. Once the surrogate system is determined, the PPDF can be evaluated by sampling the surrogate system directly with very little computational cost. The developed method is evaluated first using a simple analytical density function with multiple modes and then using two synthetic groundwater reactive transport models. The groundwater models represent different levels of complexity; the first example involves coupled linear reactions and the second example simulates nonlinear ura- nium surface complexation. The results show that the aSG-hSC is an effective and efficient tool for Bayesian inference in groundwater modeling in comparison with conventional MCMC sim- ulations. The computational efficiency is expected to be more beneficial to more computational expensive groundwater problems.

Zhang, Guannan↗
DOE OSTI2014

Used fuel rail shock and vibration testing options analysis

The objective of the rail shock and vibration tests is to complete the framework needed to quantify loads of fuel assembly components that are necessary to guide materials research and establish a technical basis for review organizations such as the U.S. Nuclear Regulatory Commission (NRC). A significant body of experimental and numerical modeling data exists to quantify loads and failure limits applicable to normal conditions of transport (NCT) rail transport, but the data are based on assumptions that can only be verified through experimental testing. The test options presented in this report represent possible paths for acquiring the data that are needed to confirm the assumptions of previous work, validate modeling methods that will be needed for evaluating transported fuel on a case-by-case basis, and inform material test campaigns on the anticipated range of fuel loading. The ultimate goal of this testing is to close all of the existing knowledge gaps related to the loading of used fuel under NCT conditions and inform the experiments and analysis program on specific endpoints for their research. The options include tests that would use an actual railcar, surrogate assemblies, and real or simulated rail transportation casks. The railcar carrying the cradle, cask, and surrogate fuel assembly payload would be moved in a train operating over rail track modified or selected to impart shock and vibration forces that occur during normal rail transportation. Computer modeling would be used to help design surrogates that may be needed for a rail cask, a cask’s internal basket, and a transport cradle. The objective of the design of surrogate components would be to provide a test platform that effectively simulates responses to rail shock and vibration loads that would be exhibited by state-of-the-art rail cask, basket, and/or cradle structures. The computer models would also be used to help determine the placement of instrumentation (accelerometers and strain gauges) on the surrogate fuel assemblies, cask and cradle structures, and the railcar so that forces and deflections that would result in the greatest potential for damage to high burnup and long-cooled UNF can be determined. For purposes of this report we consider testing on controlled track when we have control of the track and speed to facilitate modeling.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

30 cm Drop Tests

The data from the multi-modal transportation test conducted in 2017 demonstrated that the inputs from the shock events during all transport modes (truck, rail, and ship) were amplified from the cask to the spent commercial nuclear fuel surrogate assemblies. These data do not support common assumption that the cask content experiences the same accelerations as the cask itself. This was one of the motivations for conducting 30 cm drop tests. The goal of the 30 cm drop test is to measure accelerations and strains on the surrogate spent nuclear fuel assembly and to determine whether the fuel rods can maintain their integrity inside a transportation cask when dropped from a height of 30 cm. The 30 cm drop is the remaining NRC normal conditions of transportation regulatory requirement (10 CFR 71.71) for which there are no data on the actual surrogate fuel. Because the full-scale cask and impact limiters were not available (and their cost was prohibitive), it was proposed to achieve this goal by conducting three separate tests. This report describes the first two tests — the 30 cm drop test of the 1/3 scale cask (conducted in December 2018) and the 30 cm drop of the full-scale dummy assembly (conducted in June 2019). The dummy assembly represents the mass of a real spent nuclear fuel assembly. The third test (to be conducted in the spring of 2020) will be the 30 cm drop of the full-scale surrogate assembly. The surrogate assembly represents a real full-scale assembly in physical, material, and mechanical characteristics, as well as in mass.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Toxicology and Biodegradability of Blendstocks for Mixing Controlled Compression Ignition Combustion: Literature Review of Available Data

The goal of the U.S. Department of Energy’s Co-Optima project is to develop an understanding of potential biomass-derived blendstocks for use in the transportation sector. This report documents the estimated environmental and toxicological impact of potential bioblendstocks for mixing controlled compression ignition (MCCI) engines. The candidate molecules were: 2-nonanol; n-undecane; 2,6,10-trimethyldodecane; 5-ethyl-4-propylnonane; hexylhexanoate; methyldecanoate; dibutoxymethane; 4-butoxyheptane; dipentylether; renewable diesel; and soy biodiesel. To provide a robust comparison, diesel surrogate molecules were also considered: α-methylnaphthalene; decahydronaphthalene; 2,2,4,4,6,8,8-heptamethylnonane; n-butylcyclohexane; n-hexadecane; tetralin; and n-dodecylbenzene. The intent of this work was not to provide absolute answers and remove any molecules from consideration, but to provide more robust data for each molecule that can be used in subsequent evaluations. Little literature data was available for many of the molecules, both potential blendstocks and diesel surrogates, so a quantitative structural activity relationship (QSAR) approach was used to provide input into the relevant models. This assessment included an evaluation of compartment partitioning in the event of an accidental chemical spill, fate and transport indicators, estimated biodegradability, and human and environmental toxicology. The higher molecular weight and longer hydrocarbon chain lengths of these MCCI molecules reduce water solubility and mobility. Generally, the oxygenated molecules improved biodegradability metrics. The hydrocarbons farnesane and 5-Et-4-PrNonane are not predicted to be biodegradable. Human acute toxicity was found to be slightly to non-toxic for the MCCI blendstocks and the diesel surrogates. 2-Nonanol, undecane, MeDecanoate, dibutoxymethane, and dipentylether were likely developmental toxicants. Although poorly water soluble, all the compounds, whether Co-Optima blendstocks or diesel surrogates, showed significant toxicity to D. magna. The oxygenated blendstocks were less likely to bioaccumulate than the non-oxygenated blendstocks, which behaved similar to the diesel surrogates. Overall, this predicted analysis showed that the MCCI blendstocks are very similar to their diesel counterparts. No significant showstoppers were found that would indicate concern moving forward with additional research into these potential fuels. The results presented were based solely on predictive models and serve to provide information going forward for continued evaluation of these molecules.

09 BIOMASS FUELS↗
DOE OSTI2021

Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility

Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Towards Z-Next: The Integration of Theory, Experiments, and Computational Simulation in a Bayesian Data Assimilation Framework

Making reliable predictions in the presence of uncertainty is critical to high-consequence modeling and simulation activities, such as those encountered at Sandia National Laboratories. Surrogate or reduced-order models are often used to mitigate the expense of performing quality uncertainty analyses with high-fidelity, physics-based codes. However, phenomenological surrogate models do not always adhere to important physics and system properties. This project develops surrogate models that integrate physical theory with experimental data through a maximally-informative framework that accounts for the many uncertainties present in computational modeling problems. Correlations between relevant outputs are preserved through the use of multi-output or co-predictive surrogate models; known physical properties (specifically monotoncity) are also preserved; and unknown physics and phenomena are detected using a causal analysis. By endowing surrogate models with key properties of the physical system being studied, their predictive power is arguably enhanced, allowing for reliable simulations and analyses at a reduced computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Electrorefining Bismuth Using AC Superimposed DC Waveforms

Bismuth has been successfully electrorefined using both direct current (DC) and alternating current (AC) superimposed DC waveforms on a kilogram scale at Brigham Young University (BYU) in Provo, Utah. Finding a suitable surrogate for Pu Electrorefining has always been a technical challenge. This work allows the possibility to improve Pu electrorefining with nonradioactive material at universities, which greatly reduces the cost at Lawrence Livermore National Laboratory (LLNL) for improving Pu Electrorefining. Several possible surrogates (Ce, In, Sn, Zn, and Bi) were selected based on their ability to mimic the Pu electrorefining process. Ce and In electrorefining experiments were conducted at the same temperature as Pu electrorefining in CaCl 2 while Sn, Zn, and Bi, electrorefining experiments were conducted at a lower temperature in a eutectic molten salt composed of LiCl, KCl, and CaCl 2 . The electrorefining experiments using Ce with a Ga impurity did not produce a cathode ring. While the remaining Ce anode after electrorefining was significantly less than what was initially added, Scanning Electron Microscopy with Energy Dispersive X-Ray (SEM-EDX) analysis showed that the Ce had been oxidized and formed a colloid with the surrounding molten salt. This is supported in the literature by analysis of Ce-rich CaCl 2 mixtures. All the electrorefining experiments except for the Ce electrorefining experiments did not introduce an impurity to the system. Electrorefining experiments with In showed that the InCl 3 volatilized out of the molten salt to an extent that only a low current (<<1 A) could be supported without decomposing the molten salt. Zn electrorefining experiments had marginal success, however an easily separable product was not formed. The Zn had to be rinsed from the salt after the experiment. Of the identified potential surrogates. Investigated, only Bi and Sn yielded an easily separable product ring, which enables quantitative analysis of yields and coulombic efficiencies. Bismuth was first identified as a surrogate at BYU, while Sn was identified as a surrogate at LLNL later. Thus, the electrorefining experiments conducted at BYU using AC superimposed DC waveforms used Bi as the metal to be electrorefined.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Integrated Process Testing of MSR Salt Spill Accidents

Part of the licensing process for new nuclear reactors requires vendors to assess the potential consequences of identified accident scenarios using accident progression modeling. The accident scenario that will likely be evaluated by all molten salt reactor (MSR) developers is a spill of radionuclide-bearing fuel salt onto the reactor containment floor (i.e., a salt spill accident). The development of accident progression models requires experimental data to inform which processes to incorporate, to enable the calculation of parameters to model these processes, and to validate the model predictions. The data should quantify the sensitivities of key processes (e.g., molten salt spreading, heat transfer, containment structure corrosion, radionuclide vaporization, and aerosol generation) towards the initial conditions of the spill, the ambient environment, and the features of the containment. In addition, results from integrated process tests that quantify coupled processes are required to validate systems-level models. This report documents results from integrated process tests conducted on simulated molten salt spill accidents. The generated experimental data simultaneously quantify the heat transfer behavior of the spilled salt, compositional changes to the bulk salt, and the release of surrogate fission products from the spilled salt as aerosol particles. All tests that were conducted used FLiNaK doped with surrogate fission products, and the variables that were evaluated included the initial salt temperature and the concentration of surrogate fission products present in the salt. The major accomplishments of this work include identifying surrogate fuel salt compositions that provide insight into the dispersal behavior of radionuclides of potential significance to the source term, employing previously developed methods and measurement techniques to simultaneously measure key processes, generating data on the coupled processes of molten salt heat transfer and surrogate fission product release as aerosol particles, demonstrating new test methods for real-time monitoring of the flow rate of the spill and aerosol size quantification in an argon atmosphere, and developing a mass transfer model for cesium and iodine release from molten FLiNaK to provide insight into aerosol formation by vapor condensation. The same methodology applied herein can be employed to study different salt compositions of interest to MSR developers, different environmental conditions, and other variables that are relevant to postulated accident scenarios. The insights gained from these integrated process tests conducted at a laboratory scale will be incorporated into future integral effects tests conducted at an engineering scale.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2016

A traits-based approach for prioritizing species for monitoring and surrogacy selection

The bar for justifying the use of vertebrate animals for study is being increasingly raised, thus requiring increased rigor for species selection and study design. Although we have power analyses to provide quantitative backing for the numbers of organisms used, quantitative backing for selection of study species is not frequently employed. This can be especially important when measuring the impacts of ecosystem alteration, when study species must be chosen that are both sensitive to the alteration and of sufficient abundance for study. Just as important is providing justification for designation of surrogate species for study, especially when the species of interest is rare or of conservation concern and selection of an appropriate surrogate can have legal implications. In this study, we use a combination of GIS, a fish traits database and multivariate statistical analyses to quantitatively prioritize species for study and to determine potential study surrogate species. We provide two case studies to illustrate our quantitative, traits-based approach for designating study species and surrogate species. In the first case study, we select broadly representative fish species to understand the effects of turbine passage on adult fishes based on traits that suggest sensitivity to turbine passage. In our second case study, we present a framework for selecting a surrogate species for an endangered species. Lastly, we suggest that our traits-based framework can provide quantitative backing and added justification to selection of study species while expanding the inference space of study results.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Creation of a Rapid High-Fidelity Aerodynamics Module for a Multidisciplinary Design Environment

In the traditional aerospace vehicle design process, each successive design phase is accompanied by an increment in the modeling fidelity of the disciplinary analyses being performed. This trend follows a corresponding shrinking of the design space as more and more design decisions are locked in. The correlated increase in knowledge about the design and decrease in design freedom occurs partly because increases in modeling fidelity are usually accompanied by significant increases in the computational expense of performing the analyses. When running high fidelity analyses, it is not usually feasible to explore a large number of variations, and so design space exploration is reserved for conceptual design, and higher fidelity analyses are run only once a specific point design has been selected to carry forward. The designs produced by this traditional process have been recognized as being limited by the uncertainty that is present early on due to the use of lower fidelity analyses. For example, uncertainty in aerodynamics predictions produces uncertainty in trajectory optimization, which can impact overall vehicle sizing. This effect can become more significant when trajectories are being shaped by active constraints. For example, if an optimal trajectory is running up against a normal load factor constraint, inaccuracies in the aerodynamic coefficient predictions can cause a feasible trajectory to be considered infeasible, or vice versa. For this reason, a trade must always be performed between the desired fidelity and the resources available. Apart from this trade between fidelity and computational expense, it is very desirable to use higher fidelity analyses earlier in the design process. A large body of work has been performed to this end, led by efforts in the area of surrogate modeling. In surrogate modeling, an up-front investment is made by running a high fidelity code over a Design of Experiments (DOE); once completed, the DOE data is used to create a surrogate model, which captures the relationships between input variables and responses into regression equations. Depending on the dimensionality of the problem and the fidelity of the code for which a surrogate model is being created, the initial DOE can itself be computationally prohibitive to run. Cokriging, a modeling approach from the field of geostatistics, provides a desirable compromise between computational expense and fidelity. To do this, cokriging leverages a large body of data generated by a low fidelity analysis, combines it with a smaller set of data from a higher fidelity analysis, and creates a kriging surrogate model with prediction fidelity approaching that of the higher fidelity analysis. When integrated into a multidisciplinary environment, a disciplinary analysis module employing cokriging can raise the analysis fidelity without drastically impacting the expense of design iterations. This is demonstrated through the creation of an aerodynamics analysis module in NASA’s OpenMDAO framework. Aerodynamic analyses including Missile DATCOM, APAS, and USM3D are leveraged to create high fidelity aerodynamics decks for parametric vehicle geometries, which are created in NASA’s Vehicle Sketch Pad (VSP). Several trade studies are performed to examine the achieved level of model fidelity, and the overall impact to vehicle design is quantified.

Aerodynamic Properties↗
NASA NTRS2016

The First Government Sanctioned Delivery of Medical Supplies by Remotely Controlled Unmanned Aerial System (UAS)

The first government sanctioned delivery of medical supplies by UAS occurred at Wise, Virginia, on July 17, 2015. The "Let's Fly Wisely" event was a demonstration of the humanitarian use of UAS to facilitate delivery of medical supplies to remote or otherwise difficult-to-reach areas. The event was the result of coordinated efforts by a partnership which included the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), Virginia Polytechnic Institute, the Mid-Atlantic Aviation Partnership (MAAP), Flirtey Corporation, Lonesome Pine Airport, Remote Area Medical (RAM), Health Wagon, SEESPAN Aerial Interactive, Rx Partnership, and Wise County, Virginia. The historic event occurred during the annual Remote Area Medical clinic at the Wise County Fairgrounds. The medical supplies in small packages were delivered to the Wise County Fairgrounds from the Lonesome Pine Airport by UAS operated by Firtey. A larger supply of medical supplies were delivered to the Lonesome Pine Airport from the Tazewell County Airport by NASA Langley's SR22 UAS Surrogate Research aircraft. The UAS Surrogate aircraft was remotely controlled for most of the flight by a UAS Ground Control Station located at the Lonesome Pine Airport. The medical supplies were delivered from the UAS Surrogate to Flirtey for final delivery by Hex Multi-Rotor UAS in smaller packages and multiple trips to the fairgrounds. A Certificate of Authorization (COA) issued by the Federal Aviation Administration (FAA) designated the site as an authorized UAS test site. The paper will present additional details of the historic delivery of pharmaceuticals by UAS during the "Let's Fly Wisely" event. The paper will also provide details of NASA's SR22 UAS Surrogate Research aircraft. The UAS Surrogate was designed to investigate the procedures, aircraft sensors and other systems that may be required to allow Unmanned Aerial Systems (UAS) to safely operate with manned aircraft in the National Airspace System (NAS).

Howell, Charles T., III↗