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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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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Opportunities and limits of Decay storage based on Dismantled Materials in Nuclear Power Plants - 17650

We investigate the potential of decay storage of low and intermediate radioactive waste for release procedures. We present the Safetec software tool HL3 and its application to data from the German nuclear power plant at the site in Stade. In general, during the dismantling process of nuclear power plants, waste of slightly contaminated and/or activated systems is produced in large amounts. These are in particular demolition rubble from concrete structures and ingots from the melting process of metals. If, at the time of disposal, the nuclear inventory is near or slightly above federal clearance limits, the material cannot be released as conventional waste. However, due to the decline of the nuclear inventory over time, a significant amount of that waste could qualify for the release procedure in the future. Although this concept of decay storage is well known, specific predictions of the mass flow to be released is non-trivial. The mass flow over time depends on the composition of known and assumed nuclides (nuclide vector), as well as on the underlying threshold values for release. To handle the waste stream as a function of time, the nuclide composition and specific threshold values, Safetec developed a bespoke software tool named HL3. Its primary function is to visualize and to test/analyze various nuclide vectors and its decompositions over time. For the application within German law, threshold values according to the different disposal routes can be considered. Using the batch interface in the software tool HL3, large amounts of data can be automatically analyzed in a traceable and quick way. To test both, the tool and the benefit for the concept of decay storage, we have applied our code HL3 to material from the dismantling of the pressurized-water reactor at the site in Stade, Germany. We set the focus on concrete debris and metal ingots, which in principle are best suited for decay storage over years. The assignment and the packaging of that waste from Stade were based on application documents for decommissioning of the Year 2000 and according to the state of art conditioning technique at that time. Based on the data of that waste and the appropriate disposal routes, we calculate and discuss the amount of waste releasable after decay storage. As for the situation in Germany, we find that only a negligible amount would be releasable, mainly due to other reasons. These are the deterioration of detectability of radionuclides with time, and other regulatory conditions. However, on an international scale, a different assessment can be drawn. Our results thus motivate to reconsider the option of interim decay storage to reduce radioactive waste in large amounts. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm

Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of myxobacteria that has the smallest genome among all sequenced aerobic myxobacteria, was easily achieved using the binning software. This work demonstrates that the processes required for recovering genomes from assembled metagenomic datasets can be readily automated, an important advance in understanding the metabolic potential of microbes in natural environments. MaxBin is available at https://sourceforge.net/projects/maxbin/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

An automated system to define the optimal operating settings of cryogenic calorimeters

Cryogenic macro-calorimeters instrumented with NTD thermistors have been developed for several decades. The choice of the optimal bias current is crucial for a proper operation of these detectors, both in terms of energy resolution and stability. In this paper we present a set of automatic measurements and analysis procedures for the characterization and optimization of the working configuration of the NTD thermistors. The presented procedures were developed for CUORE, an array of 988 cryogenic macro-calorimeters instrumented with NTD thermistors that has been taking data since 2017. These procedures made it possible to characterize a large number of detectors in a reliable way. They are suitable enough to be used also in other large arrays of cryogenic detectors, such as CUPID.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

TU-F-CAMPUS-I-03: Preliminary Study of Size-Specific Dose Estimates in Adult Abdomenal CT Examinations in Taiwan

Purpose: This study was to investigate size-specific dose estimates (SSDE) for routine adult abdominal CT examinations in Taiwan. Methods: A national survey was conducted in Taiwan in 2014 to investigate SSDEs for routine adult abdominal CT examinations. The hospitals involved in this study provided CT images of their typical patients. The CT image in the level of the middle liver was selected to record the corresponding tube current, slice mAs or effective mAs. The image was also used to estimate the dimensions of patient as measuring the lengths in the anterior to posterior (AP) and lateral (LAT) directions. The effective diameter was then calculated from AP and LAT, and used to look up conversion factors in the AAPM 204 report. The volume CTDI (CTDIvol) for each CT unit was measured on sites using a 32-cm cylindrical standard dose phantom and a calibrated pencil-type ionization chamber. Individual patient’s SSDEs were then calculated from the corresponding SSDE conversion factor and the CTDIvol. Results: The study cohort included 111 CT units. The ratio of turning on automatic tube current modulation (ATCM) or not is 88:23. Effective diameters are 258.7±25.1 mm (167–366 mm). 99.3% of typical patients selected by each hospital have smaller effective diameter than the 32-cm dosimetry phantom. Adult abdomenal SSDE is 17.5 ± 8.8 mGy (1.9-58 mGy). The SSDE seems to decrease as the effective diameter increases as the ATCM turns off, and independent with the effective diameter as the ATCM turns on. Conclusion: The SSDE for typical patients in Taiwan was investigated. We continue to complete this investigation in 2015 to include more valid data to establish SSDE reference level in Taiwan. This study was financially supported by the Atomic Energy Council in Taiwan.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Automatic and Transparent Resource Contention Mitigation for Improving Large-Scale Parallel File System Performance

Proportional to the scale increases in HPC systems, many scientific applications are becoming increasingly data intensive, and parallel I/O has become one of the dominant factors impacting the large-scale HPC application performance. On a typical large-scale HPC system, we have observed that the lack of a global workload coordination coupled with the shared nature of storage systems cause load imbalance and resource contention over the end-to-end I/O paths resulting in severe performance degradation. I/O load imbalance on HPC systems is generally a self-inflicted wound and mostly occurs between the I/O paths and resources consumed by each individual job. In this paper, we introduce TAPP-IO, a dynamic, shared load balancing framework for mitigating resource contention. TAPP-IO extends our previous work and solves two major limitations: First, it transparently intercepts file creation calls during runtime to balance the workload over all available storage targets. The usage of TAPP-IO requires no application source code modifications and is independent from any I/O middleware. The framework can be applied to almost any HPC platform and is suitable for systems that lack a centralized file system resource manager. Second, the framework proposes a new placement strategy to support not only file-per-process I/O, but also single shared file I/O. This opens the door to a new class of scientific applications that can leverage the placement library for improved performance. We demonstrate the effectiveness of our integration on the Titan system at the Oak Ridge National Laboratory. Our experiments with a synthetic benchmark and real-world HPC workload show that, even in a noisy production environment, TAPP-IO can improve large-scale application performance significantly.

Neuwirth, Sarah↗
DOE OSTI2014

Predictive RANS simulations via Bayesian Model-Scenario Averaging

The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Microkinetic modeling of aqueous phase biomass conversion: Application to ethylene glycol reforming

In this communication, we develop automatically a reaction network for ethylene glycol reforming on Pt. We employ a recently introduced group additivity scheme for estimation of thermochemistry over metal catalysts in vapor phase and in solution along with Brønsted-Evans-Polanyi (BEP) relations for vapor phase kinetics to parameterize microkinetic models for vapor and aqueous phases. Unlike vapor-phase reforming, we show that solvent occupies a significant fraction of surface sites and by doing this, it shifts the water-gas shift reaction to CO2 and H2 and avoids poisoning of the catalyst by CO. The model is found to be in reasonable agreement with the experimental data.

bio-inspired↗
DOE OSTI2014

Adventures on the C 3 H 5 O potential energy surface: OH+propyne, OH+allene and related reactions

We mapped out the stationary points and the corresponding conformational space on the C 3 H 5 O potential energy surface relevant for the OH + allene and OH + propyne reactions systematically and automatically using the KinBot software at the UCCSD(T)-F12b/cc-pVQZ-F12//M06-2X/6-311++G(d,p) level of theory. We used RRKM-based 1-D master equations to calculate pressure- and temperature-dependent, channel-specific phenomenological rate coefficients for the bimolecular reactions propyne + OH and allene + OH, and for the unimolecular decomposition of the CH 3 CCHOH, CH 3 C(OH)CH, CH 2 CCH 2 OH, CH 2 C(OH)CH 2 primary adducts, and also for the related acetonyl, propionyl, 2-methylvinoxy, and 3-oxo-1-propyl radicals. The major channel of the bimolecular reactions at high temperatures is the formation propargyl + H 2 O, which makes the title reactions important players in soot formation at high temperatures. However, below ~1000 K the chemistry is more complex, involving the competition of stabilization, isomerization and dissociation processes. We found that the OH addition to the central carbon of allene has a particularly interesting and complex pressure dependence, caused by the low-lying exit channel to form ketene + CH 3 bimolecular products. In this study, we compared our results to a wide range of experimental data and assessed possible uncertainties arising from certain aspects of the theoretical framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI data obtained from both scanners, the classification accuracies with the SVM and Bayesian classifiers were 92% and 77%, respectively. The selected features resulting from the classification process differed by scanner, with more features included for the classification of the integrated HRCT data than for the classification of the HRCT data from each scanner. For the integrated data, consisting of HRCT images of both scanners, the classification accuracy based on the SVM was statistically similar to the accuracy of the data obtained from each scanner. However, the classification accuracy of the integrated data using the Bayesian classifier was significantly lower than the classification accuracy of the ROI data of each scanner. Conclusions: The use of an integrated dataset along with a SVM classifier rather than a Bayesian classifier has benefits in terms of the classification accuracy of HRCT images acquired with more than one scanner. This finding is of relevance in studies involving large number of images, as is the case in a multicenter trial with different scanners.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Neutral gas analysis for JET DT operation

Neutral gas analysis, the measurement and evaluation of total and partial pressures, is a key technique to study the impact of neutral gas dynamics on retention, recycling and release processes of fuel or impurity species in fusion devices. At JET, the experiment closest to ITER in terms of operating parameters and size, various detectors and techniques for partial pressure and total pressure measurements are deployed together to characterise neutral gas dynamics during and after plasma operation on various toroidal and poloidal locations. An extensive modification of JET's sub-divertor neutral gas diagnostic system aims at retaining and extending established measurement capabilities in the forthcoming Deuterium-Tritium (DT) experiments (DTE2). To achieve DT compatibility, a separation of radiation-sensitive electronics from the sensor and adequate radiation shielding is required, as well as utilisation of a DT compatible differential pumping system with adjustable throughput to account for the strong pressure variation in the sub-divertor region. Finally, the sub-divertor neutral gas diagnostic will be equipped with multiple Residual Gas Analysers (RGAs), utilising quadrupole mass spectrometry and electrostatic ion-trap-principles, all operating with remote electronics located behind the biological radiation shield. These RGAs will record data in a fast selected discrete mass mode during plasma pulses (cycle ~2 s) and will automatically switch back to continuous data recording (cycle ~100 s) afterwards. They will be complemented by a newly improved Penning gauge spectroscopy configuration in particular supporting the He and D 2 separation relevant for DT operation. The distance between these devices and their associated control unit is typically 15m . A newly developed RGA with a cable length of 80 m, compatible with the ITER environment, will also be employed for the first time. Finally, this set-up and its operation in DTE2 will provide vital input to the development of the ITER divertor RGA in the most relevant environment currently available.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

R and DE Robotic Sensor Intern Team: Advanced Sensor Suite Prototype

The Department of Energy (DOE) nuclear complex includes many hazardous and challenging environments that must be inspected remotely and routinely for structural integrity evaluation and long-term planning purposes. The H-Canyon Air Exhaust (HCAEX) Tunnel is an example of such an environment where a biannual inspection is currently performed. Presently inspections are performed by teleoperated robotic crawlers which provide low-res video feedback to the structural engineers. Advanced sensors: including Lidar and panospheric hi-res cameras have the potential to provide additional valuable data in future inspections. The primary goal of the sensor suite is to provide an internal 3D map of the HCAEX tunnel. Possible sensors were a stereo camera, a 3D Lidar, or a 2D Lidar that would need to be spun around. The issues with the stereo camera were that it does not have a large field-of-view and that its depth information was not nearly as accurate as even the baseline Lidars. The Hokuyo UTM-30LX-EW shown below provides a 270 deg. FOV with high accuracy and resolution while the largest FOV on a 3D Lidar is 40 deg. This was the reason a 2D Lidar was chosen over the 3D Lidar. Even though the 2D Lidar requires additional components and software to generate a 3D map, its FOV advantage is too great given that the HCAEX tunnel is a relatively confined space. Along with the Lidar, a camera sensor, IMU sensor, pan-tit unit, and on board computer needed to be identified. The two panospheric cameras allow for full 360 deg. FOV. The IMU will give data on the state of the robot inside the tunnel. The computer is used to gather and process data on board and to control the pan-tilt system. Mounts were designed and 3D printed to build the prototype of the sensor suite. It was designed to have the Lidar on top With its full FOV free while having the camera below and as close to together as possible while still leaving much of their FOV visible. The design also managed the cables such that they were unlikely to be damaged if in tension and so that they did not intrude much on the cameras vision. The sensor tree developed for the H-Canyon tunnel inspection project produces a large quantity of camera and Lidar data. a postprocessing pipeline was created to facilitate the analysis of these data. The pipeline follows the following routine: 1) Registration - Salient local features in subsequent scans are identified and used to align the scans With one another. This allows production of a single cohesive map of the tunnel. 2) Sensor Fusion - RGB camera data is 'painted' onto the depth data created by the Lidar. 3) Segmentation - Individual surfaces from within the scene are segmented out separately for individual analysis. 4) Degradation Analysis - Automatic mapping of local deviation from the plane in position or surface normal is performed, as is detection of exposed rebar using Lidar return intensity. 2) Obstacle Detection - Lidar data is also used in real time to detect obstacles in the environment. This could give feedback to the operator to successfully drive in the tunnel. 6) Graphic User Interface - a custom GUI was created to facilitate customer use of the system. The system provides real-time feedback from both the cameras and Lidar. The Lidar point-cloud data is also used for obstacle detection. As the Lidar spins, various algorithms are run to analyze the current 'slice' and obstacles in the environment are determined and registered on a cost-map. A Graphical User Interface (GUI) was developed to allow the user to easily command the crawler to set the rotational speed of the Lidar, perform a scan, and register and save the 3D generated cloud after a scan is done. For the good of the crawler, a critical aspect of the GUI design is to allow the user to cancel a scan any time after it is commanded and before it is saved. The sensor trees depth accuracy was evaluated in comparison to a high end commercial FARO Terrestrial Laser Scanner (TLS) with specifications for millimeter accuracy at distances above 10 m. For the purposes of these tests, the depth measurements produced by the TLS were taken as ground truth. Example test wall panels were scanned with the TLS and postprocessed with its proprietary software, and subsequently scanned with the sensor tree system. The two scans were manually aligned and for each point in the output cloud from the sensor tree, the distance to the nearest ground truth point in the FARO cloud was determined. 98.7% of all produced points were within the specified target range of 6 mm, with an average deviation of 1.75 mm. The test walls used have an overall in-wall depth variation of around 40 mm.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Statistical optimisation techniques in fatigue signal editing problem

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

TRIPOLI-4{sup R} version 9 Shielding for the Shielding Community: Overview, Relevant Benchmarking for Shielding and Licensing Policy - Paper 49

This is a review paper of the Shielding only version of TRIPOLI-4. TRIPOLI-4 and TRIPOLI-4 S: TRIPOLI{sup R} represents a family of radiation transport codes using the Monte Carlo method (1,2). It has been continuously developed at CEA since the mid 60's. TRIPOLI-4{sup R}, being the fourth generation, has been developed starting from the mid 90's in C++. It tracks neutrons from 20 MeV down to 10{sup -5} eV, photons, electrons and positrons from 100 MeV to 1 keV, in 3D arbitrary geometries either in TRIPOLI{sup R} proprietary format or ROOT(4) format. It reads its continuous energy nuclear data from any evaluation in ENDF format, including but not limited to JEFF-3.1.1 and ENDF/BVII.0. It has various simulation modes, fixed-source (with or without fission), criticality, or both of them chained. A variety of tallies are available: averaged flux in a volume (track length and collision estimators), surface flux and current, point flux, reaction rates, deposited energy, dpa, pka, gamma spectroscopy, mesh tallies, tallies for criticality (k{sub eff}, beta eff, kinetic parameters). It provides the user with embedded response functions from IRDF and EAF as well. Several variance reduction techniques (3), including automated ones, make it particularly suitable to deep penetration problems (implicit capture, Russian roulette, exponential biasing with automatic estimation of the importance map). It may run in parallel, on single multi-core machines as well as heterogeneous networks of stations or massively parallel machines. It comes with productivity tools designed to speed-up the elaboration and verification of the input deck as well as the output analysis: 2D interactive display of geometry; 2D display of mesh tallies and collision sites; 3D input deck modeler; compatibility with ROOT tools to post-process TRIPOLI{sup R} outputs. Its V and V test base is made of more than 1000 internationally accepted benchmarks from ICSBEP and SINBAD (REPLICA, ASPIS, NESDIP) and covers all its application domains, including several shielding cases which will be detailed in the talk. TRIPOLI-4{sup R} is the reference Monte Carlo code for CEA, EDF, and also for AREVA for a number of applications. A 'Shielding-only' version has been designed to specifically answer the needs of the shielding community. It is distributed through the NEA Databank with a license for R and D, covering the fusion applications, whose terms are detailed in the paper.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

SCoPE: an efficient method of Cosmological Parameter Estimation

Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological parameter estimation from CMB and other data. However, due to the intrinsic serial nature of the MCMC sampler, convergence is often very slow. Here we present a fast and independently written Monte Carlo method for cosmological parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE), that employs delayed rejection to increase the acceptance rate of a chain, and pre-fetching that helps an individual chain to run on parallel CPUs. An inter-chain covariance update is also incorporated to prevent clustering of the chains allowing faster and better mixing of the chains. We use an adaptive method for covariance calculation to calculate and update the covariance automatically as the chains progress. Our analysis shows that the acceptance probability of each step in SCoPE is more than 95% and the convergence of the chains are faster. Using SCoPE, we carry out some cosmological parameter estimations with different cosmological models using WMAP-9 and Planck results. One of the current research interests in cosmology is quantifying the nature of dark energy. We analyze the cosmological parameters from two illustrative commonly used parameterisations of dark energy models. We also asses primordial helium fraction in the universe can be constrained by the present CMB data from WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us to understand the workability of the SCoPE better, on the other hand it provides a completely independent estimation of cosmological parameters from WMAP-9 and Planck data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.

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

Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow. Methods: Considering the radiation therapy structures’ geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter- and intrastructural GAD models. These training data and the remaining fifteen testing data sets were separately employed to test the effectiveness of the proposed contouring error detection strategy. Results: An evaluation tool was implemented to illustrate how the proposed strategy automatically detects the radiation therapy contouring errors for a given patient and provides 3D graphical visualization of error detection results as well. The contouring error detection results were achieved with an average sensitivity of 0.954/0.906 and an average specificity of 0.901/0.909 on the centroid/volume related contouring errors of all the tested samples. As for the detection results on structural shape related contouring errors, an average sensitivity of 0.816 and an average specificity of 0.94 on all the tested samples were obtained. The promising results indicated the feasibility of the proposed strategy for the detection of contouring errors with low false detection rate. Conclusions: The proposed strategy can reliably identify contouring errors based upon inter- and intrastructural constraints derived from clinically approved contours. It holds great potential for improving the radiation therapy workflow. ROC and box plot analyses allow for analytically tuning of the system parameters to satisfy clinical requirements. Future work will focus on the improvement of strategy reliability by utilizing more training sets and additional geometric attribute constraints.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Advances in Westinghouse fuel rod design technology (PAD5)

The Westinghouse Performance Analysis and Design (PAD) code for light-water reactors has been recently updated to version PAD5 and submitted to NRC for review. PAD5 incorporates extensive updates in fuel performance and material property models, including fuel thermal conductivity, pellet fragment relocation, fission gas release, fission gas swelling, and cladding creep and growth. Advances in thermal models are mainly in pellet fragment relocation, radial power distribution within the pellet, and the changes in fuel thermal conductivity due to changes in temperature, burnup and fuel porosity. The PAD5 thermal fission gas release model is a phenomenological model based on the irradiation dependence of the gas release from mechanistic processes for the diffusion of the fission gas generated in the fuel grains to the grain boundaries, formation of fission gas bubbles on the grain boundaries and release of the fission gas when these grain boundary bubbles inter-link. The PAD5 cladding creep model is predominantly a combination of the following mechanisms: thermal creep, irradiation induced creep, and irradiation enhanced creep. The fuel performance database used for the development, calibration and testing of the PAD5 code covers a broad range of both commercial irradiation data and test rod data and requires continuously additional data focusing on the latest development of the PAD5 code. Test programs are also included in the database. These data are checked and stored in a hierarchical database structure that supports the automation of model development and calibration. New data, once incorporated into the database, can be quickly evaluated with respect to key fuel and cladding performance models to confirm expected trends or to evaluate potential model improvements. This automated calibration system has been integrated with state-of-the-art optimization packages in DAKOTA Analysis Kit to provide for advanced statistical assessments and model coefficient optimization. Calibration results are automatically tabulated and plotted to provide easy assessment of trends or new behaviors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗