Unlocking Hydrogen Carrier Potential: Integrated Mechanistic Studies and Surrogate Modeling
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Chemistry↗
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Results for “Surrogates”
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.
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Surrogate modeling for Z-Machine Shielding Dose Response
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Arguello, Christian Alexander [Sandia National Lab↗
Fiducial-free rho-of-phi measurements by surrogate registration and repeatable mounting scheme
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Materials science↗
Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
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Nguyen, Kyle Cuongthe [Sandia National Laboratorie↗
Multifidelity importance sampling
Estimating statistics of model outputs with the Monte Carlo method usually requires a large number of model evaluations. This leads to long runtimes if the model is expensive to evaluate. Importance sampling is one method that can lead to a reduction in the number of model evaluations. Importance sampling uses a biasing distribution to sample the model more efficiently, but generating such a biasing distribution can be difficult and usually also requires model evaluations. A different strategy to speed up Monte Carlo sampling is to replace the computationally expensive high-fidelity model with a computationally cheap surrogate model; however, because the surrogate model outputs are only approximations of the high-fidelity model outputs, the estimate obtained using a surrogate model is in general biased with respect to the estimate obtained using the high-fidelity model. We introduce a multifidelity importance sampling (MFIS) method, which combines evaluations of both the high-fidelity and a surrogate model. It uses a surrogate model to facilitate the construction of the biasing distribution, but relies on a small number of evaluations of the high-fidelity model to derive an unbiased estimate of the statistics of interest. We prove that the MFIS estimate is unbiased even in the absence of accuracy guarantees on the surrogate model itself. The MFIS method can be used with any type of surrogate model, such as projection-based reduced-order models and data-fit models. Moreover, the MFIS method is applicable to black-box models, i.e., where only inputs and the corresponding outputs of the high-fidelity and the surrogate model are available but not the details of the models themselves. We demonstrate on nonlinear and time-dependent problems that our MFIS method achieves speedups of up to several orders of magnitude compared to Monte Carlo with importance sampling that uses the high-fidelity model only.
42 ENGINEERING↗
Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling
Here, we propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems’ spatially heterogeneous parameter fields, with applications to uncertainty quantification and parameter estimation in high-dimensional problems. Practitioners often formulate finite-dimensional representations of spatially heterogeneous parameter fields using truncated unconditional Karhunen–Loève expansions (KLEs) for a certain choice of unconditional covariance kernel and construct surrogate models of the observable response with respect to the KLE coefficients. When direct measurements of the parameter fields are available, we propose improving the accuracy of these surrogate models by representing the parameter fields via conditional Karhunen-Loève expansions (CKLEs). CKLEs are constructed by conditioning the covariance kernel of the unconditional expansion on the direct measurements of the parameter field via Gaussian process regression, and then truncating the corresponding KLE. We apply the proposed methodology to constructing surrogate models via the Basis Adaptation (BA) method of the stationary hydraulic head response, measured at spatially discrete observation locations, of a groundwater flow model of the Hanford Site, as a function of the 1000-dimensional representation of the model’s log-transmissivity field. We find that BA surrogate models of the hydraulic head based on CKLEs are more accurate than BA surrogate models based on unconditional expansions for forward uncertainty quantification tasks. Furthermore, we find that inverse estimates of the hydraulic transmissivity field computed using CKLE-based BA surrogate models are more accurate than those computed using unconditional BA surrogate models.
97 MATHEMATICS AND COMPUTING↗
Exploring gasoline oxidation chemistry in jet stirred reactors
Recent decades have seen increasingly restrictive regulations applied to gasoline engines. Gasoline combustion chemistry must be investigated to achieve a better understanding and control of internal combustion engine efficiency and emissions. In this work, several gasoline fuels, namely the FACE (Fuel for Advanced Combustion Engines) gasolines, were selected as targets for oxidation study in jet-stirred reactors (JSR). The study is facilitated by formulating various gasoline surrogate mixtures with known hydrocarbon compositions to represent the real gasolines. Surrogates included binary mixtures of n-heptane and iso-octane, as well as more complex multi-component mixtures. Here, the oxidation characteristics of FACE gasolines and their surrogates were experimentally examined in JSR-1 and numerically simulated under the following conditions: pressure 1 bar, temperature 500–1050 K, residence time 1.0 and 2.0 s, and two equivalence ratios (Φ= 0.5 and 1.0). In the high temperature region, all real fuels and surrogates showed similar oxidation behavior, but in the low temperature region, a fuel’s octane number and composition had a significant effect on its JSR oxidation characteristics. Low octane number fuels displayed more low temperature reactivity, while fuels with similar octane number but a larger number of n-alkane components were more reactive. A gasoline surrogate kinetic model was examined with FACE gasoline experiments either measured in JSR-2, or taken from previous work under the following conditions: pressure 10 bar, temperature 530–1200 K, residence time 0.7 s, and three equivalence ratios (Φ = 0.5, 1.0 and 2.0). Comparison between FACE gasoline experimental results with surrogate model predictions showed good agreement, demonstrating considerable potential for surrogate fuel kinetic modeling in engine simulations.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions
An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.
Shenoy, Rajiv R.↗
Modeling of the radiation belt megnetosphere in decisional timeframes
Systems and methods for calculating L* in the magnetosphere with essentially the same accuracy as with a physics based model at many times the speed by developing a surrogate trained to be a surrogate for the physics-based model. The trained model can then beneficially process input data falling within the training range of the surrogate model. The surrogate model can be a feedforward neural network and the physics-based model can be the TSK03 model. Operatively, the surrogate model can use parameters on which the physics-based model was based, and/or spatial data for the location where L* is to be calculated. Surrogate models should be provided for each of a plurality of pitch angles. Accordingly, a surrogate model having a closed drift shell can be used from the plurality of models. The feedforward neural network can have a plurality of input-layer units, there being at least one input-layer unit for each physics-based model parameter, a plurality of hidden layer units and at least one output unit for the value of L*.
79 ASTRONOMY AND ASTROPHYSICS↗
A chemical kinetic analysis of knock propensity of methanol-to-gasoline fuel
Production of low carbon gasoline-like fuels such as methanol-to-gasoline (MTG) is a promising approach to achieve rapid greenhouse gas emission reduction of the transportation sector. Despite the fact that gasoline that meets the ASTM D4814 standard for automotive spark-ignition engine fuel can be readily produced from these processes, it is unclear how the composition of MTG may affect engine performance and emissions. Here, in this paper, a surrogate for an MTG is used to numerically study the effects of gasoline composition on knock propensity and on the sensitivity of knock to thermal and fuel stratification, to oxygen dilution and to nitric oxide from exhaust gas recirculation of residual gases. Simulations were performed in ANSYS CHEMKIN-PRO using a comprehensive chemical kinetic mechanism for gasoline surrogates, and results of the MTG surrogate were compared against those of a petroleum-based regular E10 gasoline, termed PACE-20. A premium-grade MTG fuel was also formulated by adding ethanol to the MTG surrogate, and results were compared against those of four premium-grade, gasoline-like fuels representative of future alternative gasoline formulations. Surrogates and mechanism were evaluated by comparison against experimental engine data, and the model showed high accuracy at stoichiometric conditions (mean absolute error of ignition timing equal to 1.46 crank angle degrees) but larger deviations at lean conditions (mean absolute error of ignition timing equal to 5.52 crank angle degrees). Despite the fact that the MTG surrogate has a RON 1.1 units higher than that of PACE-20, it may show higher knock propensity at medium temperature conditions due to a less intense NTC behavior. MTG autoignition was more temperature- and equivalence ratio-sensitive than that of PACE20, suggesting that MTG can benefit more from naturally-occurring thermal stratification or from induced fuel stratification of the end gas to mitigate knock intensity. The sensitivity of autoignition reactivity to oxygen dilution and to NO concentration was higher for MTG than for regular gasoline at medium loads, but the opposite trend was observed at high loads due to the effect of pressure on the low-temperature chemistry of regular gasoline. Approximately 14 % vol ethanol content was required to upgrade the octane rating of MTG from regular grade to premium grade. Adding 13.6 % vol ethanol made the fuel autoignition less sensitive to both oxygen dilution and NO content (ignition time varies approx. 17 % and 50 % less with oxygen dilution and NO addition, respectively, when adding ethanol at high engine loads).
02 PETROLEUM↗
Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method
In decision-making for groundwater management and contamination remediation, it is important to accurately evaluate the probability of the occurrence of a failure event. For small failure probability analysis, a large number of model evaluations are needed in the Monte Carlo (MC) simulation, which is impractical for CPU-demanding models. One approach to alleviate the computational cost caused by the model evaluations is to construct a computationally inexpensive surrogate model instead. However, using a surrogate approximation can cause an extra error in the failure probability analysis. Furthermore, constructing accurate surrogates is challenging for high-dimensional models, i.e., models containing many uncertain input parameters. To address these issues, we propose an efficient two-stage MC approach for small failure probability analysis in high-dimensional groundwater contaminant transport modeling. In the first stage, a low-dimensional representation of the original high-dimensional model is sought with Karhunen–Loève expansion and sliced inverse regression jointly, which allows for the easy construction of a surrogate with polynomial chaos expansion. Then a surrogate-based MC simulation is implemented. In the second stage, the small number of samples that are close to the failure boundary are re-evaluated with the original model, which corrects the bias introduced by the surrogate approximation. The proposed approach is tested with a numerical case study and is shown to be 100 times faster than the traditional MC approach in achieving the same level of estimation accuracy.
54 ENVIRONMENTAL SCIENCES↗
Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems
Polynomial chaos expansions are used to reduce the computational cost in the Bayesian solutions of inverse problems by creating a surrogate posterior that can be evaluated inexpensively. We show, by analysis and example, that when the data contain significant information beyond what is assumed in the prior, the surrogate posterior can be very different from the posterior, and the resulting estimates become inaccurate. One can improve the accuracy by adaptively increasing the order of the polynomial chaos, but the cost may increase too fast for this to be cost effective compared to Monte Carlo sampling without a surrogate posterior.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Toward a machine-guided approach to energetic material discovery
In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources.
42 ENGINEERING↗
Physiologically guided approach to characterizing respiratory motion
Purpose: To characterize radiation therapy patient breathing patterns based on measured external surrogate information. Methods: Breathing surrogate data were collected during 4DCT from a cohort of 50 patients including 28 patients with lung cancer and 22 patients without lung cancer. A spirometer and an abdominal pneumatic bellows were used as the surrogates. The relationship between these measurements was assumed to be linear within a small phase difference. The signals were correlated and drift corrected using a previously published method to convert the signal into tidal volume. The airflow was calculated with a first order time derivative of the tidal volume using a window centered on the point of interest and with a window length equal to the CT gantry rotation period. The airflow was compared against the tidal volume to create ellipsoidal patterns that were binned into 25 ml × 25 ml/s bins to determine the relative amount of time spent in each bin. To calculate the variability of the maximum inhalation tidal volume within a free-breathing scan timeframe, a metric based on percentile volume ratios was defined. The free breathing variability metric (κ) was defined as the ratio between extreme inhalation tidal volumes (defined as >93 tidal volume percentile of the measured tidal volume) and normal inhalation tidal volume (defined as >80 tidal volume percentile of the measured tidal volume). Results: There were three observed types of volume-flow curves, labeled Types 1, 2, and 3. Type 1 patients spent a greater duration of time during exhalation withκ = 1.37 ± 0.11. Type 2 patients had equal time duration spent during inhalation and exhalation with κ = 1.28 ± 0.09. The differences between the mean peak exhalation to peak inhalation tidal volume, breathing period, and the 85th tidal volume percentile for Type 1 and Type 2 patients were statistically significant at the 2% significance level. The difference between κ and the 98th tidal volume percentile for Type 1 and Type 2 patients was found to be statistically significant at the 1% significance level. Three patients did not display a breathing stability curve that could be classified as Type 1 or Type 2 due to chaotic breathing patterns. These patients were classified as Type 3 patients. Conclusions: Based on an observed volume-flow curve pattern, the cohort of 50 patients was divided into three categories called Type 1, Type 2, and Type 3. There were statistically significant differences in breathing characteristics between Type 1 and Type 2 patients. The use of volume-flow curves to classify patients has been demonstrated as a physiological characterization metric that has the potential to optimize gating windows in radiation therapy.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
A method of surface marker location optimization for tumor motion estimation in lung stereotactic body radiation therapy
Purpose: Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient’s body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparse optimization approach (SOA) model. Methods: The principle-component-analysis (PCA) method was employed as the framework to build the authors’ statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the “best represented” columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution. Results: The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms. Conclusions: The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.
60 APPLIED LIFE SCIENCES↗
SU-G-JeP4-08: External and Internal Motion Assessments with Amplitude and Time Based Reconstruction and AveCT
Purpose: To investigate the effects of external surrogate and tumour motion by observing the reconstructed phases and AveCT in an Amplitude and Time based 4DCT. Methods: Based on patient motion studies, Cos6 and sinusoidal motions were simulated as external surrogate and tumour motions in a motion phantom. The diaphragm and tumour motions may or may not display the same waveform therefore the same and different waveforms were programmed into the phantom, scanned and reconstructed based on Amplitude and Time. The AveCT and phases were investigated with these different scenarios. The AveCT phantom images were also compared with CBCT phantom images programmed with the same motions. Results: For the same surrogate and tumour sin motions, the phases (Amplitude and Time) and AveCT indicated similar motions based on the position of the BB at the slice and displayed contrast values respectively. For cos6 motions, due to the varied time the tumour spends at each position, the Amplitude and Time based phases differed. The AveCT images represented the actual tumour motions and the Time and Amplitude based phases were represented by the surrogate with varied times. Conclusion: Different external surrogate and tumour motions may result in different displayed image motions when observing the AveCT and reconstructed phases. During the 4DCT, the surrogate motion is readily available for observation of the amplitude and time of the diaphragm position. Following image reconstruction, the user may need to observe the AveCT in addition to the reconstructed phases to comprehend the time weightings of the tumour motion during the scan. This may also apply to 3D CBCT images where the displayed tumour position in the images is influenced by the long duration of the CBCT. Knowledge of the tumour motion represented by the greyscale of the AveCT may also assist in CBCT treatment beam verification matching.
60 APPLIED LIFE SCIENCES↗
TH-B-204-00: Implanted Markers for Radiation Therapy and TG 199 Update
Implanted markers as target surrogates have been widely used for treatment verification, as they provide safe and reliable monitoring of the inter- and intra-fractional target motion. The rapid advancement of technology requires a critical review and recommendation for the usage of implanted surrogates in current field. The symposium, also reporting an update of AAPM TG 199 - Implanted Target Surrogates for Radiation Treatment Verification, will be focusing on all clinical aspects of using the implanted target surrogates for treatment verification and related issues. A wide variety of markers available in the market will be first reviewed, including radiopaque markers, MRI compatible makers, non-migrating coils, surgical clips and electromagnetic transponders etc. The pros and cons of each kind will be discussed. The clinical applications of implanted surrogates will be presented based on different anatomical sites. For the lung, we will discuss gated treatments and 2D or 3D real-time fiducial tracking techniques. For the prostate, we will be focusing on 2D-3D, 3D-3D matching and electromagnetic transponder based localization techniques. For the liver, we will review techniques when patients are under gating, shallow or free breathing condition. We will review techniques when treating challenging breast cancer as deformation may occur. Finally, we will summarize potential issues related to the usage of implanted target surrogates with TG 199 recommendations. A review of fiducial migration and fiducial derived target rotation in different disease sites will be provided. The issue of target deformation, especially near the diaphragm, and related suggestions will be also presented and discussed. Learning Objectives: Knowledge of a wide variety of markers Knowledge of their application for different disease sites Understand of issues related to these applications Z. Wang: Research funding support from Brainlab AG Q. Xu: Consultant for Accuray; Q. Xu, I am a consultant for Accuray planning service.
60 APPLIED LIFE SCIENCES↗
TH-B-204-01: Real-Time Tracking with Implanted Markers
Implanted markers as target surrogates have been widely used for treatment verification, as they provide safe and reliable monitoring of the inter- and intra-fractional target motion. The rapid advancement of technology requires a critical review and recommendation for the usage of implanted surrogates in current field. The symposium, also reporting an update of AAPM TG 199 - Implanted Target Surrogates for Radiation Treatment Verification, will be focusing on all clinical aspects of using the implanted target surrogates for treatment verification and related issues. A wide variety of markers available in the market will be first reviewed, including radiopaque markers, MRI compatible makers, non-migrating coils, surgical clips and electromagnetic transponders etc. The pros and cons of each kind will be discussed. The clinical applications of implanted surrogates will be presented based on different anatomical sites. For the lung, we will discuss gated treatments and 2D or 3D real-time fiducial tracking techniques. For the prostate, we will be focusing on 2D-3D, 3D-3D matching and electromagnetic transponder based localization techniques. For the liver, we will review techniques when patients are under gating, shallow or free breathing condition. We will review techniques when treating challenging breast cancer as deformation may occur. Finally, we will summarize potential issues related to the usage of implanted target surrogates with TG 199 recommendations. A review of fiducial migration and fiducial derived target rotation in different disease sites will be provided. The issue of target deformation, especially near the diaphragm, and related suggestions will be also presented and discussed. Learning Objectives: Knowledge of a wide variety of markers Knowledge of their application for different disease sites Understand of issues related to these applications Z. Wang: Research funding support from Brainlab AG Q. Xu: Consultant for Accuray; Q. Xu, I am a consultant for Accuray planning service.
60 APPLIED LIFE SCIENCES↗