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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Embedded-cluster calculations in a numeric atomic orbital density-functional theory framework

We integrate the all-electron electronic structure code FHI-aims into the general ChemShell package for solid-state embedding quantum and molecular mechanical (QM/MM) calculations. A major undertaking in this integration is the implementation of pseudopotential functionality into FHI-aims to describe cations at the QM/MM boundary through effective core potentials and therewith prevent spurious overpolarization of the electronic density. Based on numeric atomic orbital basis sets, FHI-aims offers particularly efficient access to exact exchange and second order perturbation theory, rendering the established QM/MM setup an ideal tool for hybrid and double-hybrid level density functional theory calculations of solid systems. We illustrate this capability by calculating the reduction potential of Fe in the Fe-substituted ZSM-5 zeolitic framework and the reaction energy profile for (photo-)catalytic water oxidation at TiO{sub 2}(110)

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

Full Modeling and parameter compression methods in configuration space for DESI 2024 and beyond

In the contemporary era of high-precision spectroscopic surveys, led by projects like DESI, there is an increasing demand for optimizing the extraction of cosmological information from clustering data. This work conducts a thorough comparison of various methodologies for modeling the full shape of the two-point statistics in configuration space. We investigate the performance of both direct fits (Full Modeling) and the parameter compression approaches (ShapeFit and Standard). We utilize the ABACUS-SUMMIT simulations, tailored to exceed DESI's precision requirements. Particularly, we fit the two-point statistics of three distinct tracers (LRG, ELG, and QSO), by employing a Gaussian Streaming Model in tandem with Convolution Lagrangian Perturbation Theory and Effective Field Theory. We explore methodological setup variations, including the range of scales, the set of galaxy bias parameters, the inclusion of the hexadecapole, as well as model extensions encompassing varying ns and allowing for w 0 w a CDM dark energy model. Throughout these varied explorations, while precision levels fluctuate and certain configurations exhibit tighter parameter constraints, our pipeline consistently recovers the parameter values of the mocks within 1σ in all cases for a 1-year DESI volume. Additionally, we compare the performance of configuration space analysis with its Fourier space counterpart using three models: PyBird, FOLPS and velocileptors, presented in companion papers. We find good agreement with the results from all these models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Simulation of gas mixture dynamics in a pipeline network using explicit staggered-grid discretization

Here we develop an explicit staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge boundary values that are more complex than for a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method

Abstract We report an evaluation of a semi-empirical quantum chemical method PM7 from the perspective of uncertainty quantification. Specifically, we apply Bound-to-Bound Data Collaboration, an uncertainty quantification framework, to characterize (a) variability of PM7 model parameter values consistent with the uncertainty in the training data and (b) uncertainty propagation from the training data to the model predictions. Experimental heats of formation of a homologous series of linear alkanes are used as the property of interest. The training data are chemically accurate , i.e., they have very low uncertainty by the standards of computational chemistry. The analysis does not find evidence of PM7 consistency with the entire data set considered as no single set of parameter values is found that captures the experimental uncertainties of all training data. A set of parameter values for PM7 was able to capture the training data within ±1 kcal/mol, but not to the smaller level of uncertainty in the reported data. Nevertheless, PM7 was found to be consistent for subsets of the training data. In such cases, uncertainty propagation from the chemically accurate training data to the predicted values preserves error within bounds of chemical accuracy if predictions are made for the molecules of comparable size. Otherwise, the error grows linearly with the relative size of the molecules.

42 ENGINEERING↗
DOE OSTI2013

Topology for Statistical Modeling of Petascale Data

Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, the approach of the entire team involving all three institutions is based on the complementary techniques of combinatorial topology and statistical modelling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modelling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. The overall technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modelling, and (3) new integrated topological and statistical methods. Roughly speaking, the division of labor between our 3 groups (Sandia Labs in Livermore, Texas A&M in College Station, and U Utah in Salt Lake City) is as follows: the Sandia group focuses on statistical methods and their formulation in algebraic terms, and finds the application problems (and data sets) most relevant to this project, the Texas A&M Group develops new algebraic geometry algorithms, in particular with fewnomial theory, and the Utah group develops new algorithms in computational topology via Discrete Morse Theory. However, we hasten to point out that our three groups stay in tight contact via videconference every 2 weeks, so there is much synergy of ideas between the groups. The following of this document is focused on the contributions that had grater direct involvement from the team at the University of Utah in Salt Lake City.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Initial CEED API (CEED-MS10)

Key components of CEED software development involve fast finite element operator storage and evaluation, architecture optimizations, performant algorithms for all orders, global kernels for finite element operators, efficient use of the memory sub-system, optimal data locality and motion, enhanced scalability and parallelism, and fast tensor contractions. As ultimate goals of the project, CEED is exploring and identifying the best algorithms for the full range of discretizations and applying algorithmic and software development to support ECP applications’ needs. In this milestone, we initiated the development and implementation of a common CEED API consisting of two main components: low-level and high-level APIs. We accomplished the goal of having the low-level component ready for the release of CEED v1.0 in the next quarter, see CEED-MS13. In this report we also provide a highlight from CEED’s integration with the MARBL app and the corresponding Laghos miniapp. A main theme in this work has been the systematic transition of advanced algorithms, developed and tested in Laghos, to the large-scale multi-physics settings of the MARBL code. The low-level CEED API provides a set of FE kernels and components for writing new low-level kernels. Examples include: vector and sparse linear algebra, element matrix assembly over a batch of elements, partial assembly and action for efficient high-order operators like mass, diffusion, advection, etc. The main goal of the low-level API is to establish the basis for the high-level API. Also, identifying such low-level kernels and providing a reference implementation for them serves as the basis for collaboration with vendors and STs. Another goal for the low-level API is to make it easy for applications with their own discretization infrastructure, who do not want to switch directly to using the high-level CEED API, to still evaluate and use the core operations provided by the low-level API. One of the challenges with high-order methods is that a global sparse matrix is no longer a good representation of a high-order linear operator, both with respect to the FLOPs needed for its evaluation, as well as the memory transfer needed for a matvec. Thus, another important question we tried to address while developing the low-level API is: is it possible to abstract out in a natural and efficient way the different implementations and data structures that arise when dealing with various computational device types: CPUs, GPUs, etc. Fully answering this question is beyond the scope of this particular milestone, since that requires longer time as we develop and expand the devices we support. The future high-level API will be the frontend provided by CEED consisting of high-level abstractions such as meshes, finite element spaces and solutions, bilinear forms, solvers, etc. At this high-level, an important aspect of the API will be to provide a uniform abstraction layer for writing code in a portable manner. To achieve that, we plan to utilize the MPI programming model combined with a device abstraction layer (DAL) for finite element computations that we will develop. The DAL will serve as the bridge between the low- and the high-level APIs, allowing a variety of programming models to be used for the low-level kernel implementations. The software artifacts delivered as part of this milestone include an initial CEED API software implementation (libCEED) provided through the CEED website, http://ceed.exascaleproject.org and the CEED GitHub organization, http://github.com/ceed. In addition to details and results from the above R&D efforts, in this document we are also reporting on other project-wide activities performed in Q1 of FY18, including: the Nek5000 v17.0 MFEM v3.3.2 and PETSc v3.8 releases, collaborations with ECP/ST and SciDAC projects, the inaugural Nek5000 hackathon and various outreach activities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Galaxy Cluster Mass Reconstruction Project – III. The impact of dynamical substructure on cluster mass estimates

With the advent of wide-field cosmological surveys, we are approaching samples of hundreds of thousands of galaxy clusters. While such large numbers will help reduce statistical uncertainties, the control of systematics in cluster masses is crucial. Here we examine the effects of an important source of systematic uncertainty in galaxy-based cluster mass estimation techniques: the presence of significant dynamical substructure. Dynamical substructure manifests as dynamically distinct subgroups in phase-space, indicating an ‘unrelaxed’ state. This issue affects around a quarter of clusters in a generally selected sample. We employ a set of mock clusters whose masses have been measured homogeneously with commonly used galaxy-based mass estimation techniques (kinematic, richness, caustic, radial methods). We use these to study how the relation between observationally estimated and true cluster mass depends on the presence of substructure, as identified by various popular diagnostics. We find that the scatter for an ensemble of clusters does not increase dramatically for clusters with dynamical substructure. However, we find a systematic bias for all methods, such that clusters with significant substructure have higher measured masses than their relaxed counterparts. This bias depends on cluster mass: the most massive clusters are largely unaffected by the presence of significant substructure, but masses are significantly overestimated for lower mass clusters, by ~ 10 percent at 10 14 and ≳ 20 percent for ≲ 10 13.5 . Finally, the use of cluster samples with different levels of substructure can therefore bias certain cosmological parameters up to a level comparable to the typical uncertainties in current cosmological studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

The D and D of Nuclear Facilities: a Systemic Approach for Project Organization and Monitoring - 18099

The Decontamination and Decommissioning (D and D) of Licensed Nuclear facilities takes place under circumstances which make it a very complex field. On the one hand, more and more facilities of widely varying types are concerned, and many D and D projects are currently running or planned. A nuclear facility's life cycle is often very long and the building may be modified or impacted by incidents during its operation. These changes need to be known and taken into account for the D and D plans, in order to ensure safety at all times. In addition, the numerous stakeholders involved in a project express needs which impose many requirements. These encompass various points of view, and data from different detail levels must be managed to satisfactorily meet them. On the other hand, legacy data (including records, plans etc.) is a recurring issue for D and D projects. The quality and availability of relevant data regarding the different phases of the nuclear facility lifetime may be insufficient, for instance due to the lack of traceability practices in the past. The uncertainty level is not always specified and explained, old documents can be damaged and are not necessarily digitized, etc. It is therefore particularly important to define the relevant type of raw data to be collected and traced for D and D projects as early as possible. In this context, CEA (Commissariat a l'energie Atomique et aux energies Alternatives) has been analyzing past D and D projects for many years. The goal is to facilitate reproducibility by generalizing best practices and promoting efficient organizational rules and technical solutions. A method is currently being studied to help project managers to prepare specifications for future D and D operations and improve both the efficiency and the relevance of the decommissioning strategy throughout each project, depending on the expected quality of its end-state. This method first consists in formalizing and specifying the entire set of requirements to be taken into consideration, e.g. technical, operational, logistical, regulatory, financial, organizational, safety and security, human, ethical, or, naturally, environmental aspects. Second, based on these requirements, it will enable the project team to structure, check, and then to demonstrate the coherence and feasibility of the project from both the technological and organizational points of view. Lastly, the method will enable the evaluation of the various possible solutions for the management of dismantling products, for instance, regarding the waste outlets: direct disposal or interim storage. To conclude, the method should permit a constant re-evaluation of the D and D strategy depending on the possible evolution of the stakeholders' requirements, and thus it should optimize the monitoring of D and D projects. (authors)

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

Asymmetry in Time Evolution of Magnetization in Magnetic Nanostructures

Strong interest in nanomagnetism stems from the promise of high storage densities of information through control of ever smaller and smaller ensembles of spins. There is a broad consensus that the Landau-Lifshitz-Gilbert equation reliably describes the magnetization dynamics on classical phenomenological level. On the other hand, it is not so evident that the magnetization dynamics governed by this equation contains built-in asymmetry in the case of broad topology sets of symmetric total energy functional surfaces. The magnetization dynamics in such cases shows preference for one particular state from many energetically equivalent available minima. Here, we demonstrate this behavior on a simple one-spin model which can be treated analytically. Depending on the ferromagnet geometry and material parameters, this asymmetric behavior can be robust enough to survive even at high temperatures opening simplified venues for controlling magnetic states of nanodevices in practical applications. Using micromagnetic simulations we demonstrate the asymmetry in magnetization dynamics in a real system with reduced symmetry such as Pacman-like nanodot. Finally, exploiting the built-in asymmetry in the dynamics could lead to practical methods of preparing desired spin configurations on nanoscale. Introduction

36 MATERIALS SCIENCE↗
DOE OSTI2018

Marine Mammal Behavioral Response to Tidal Turbine Sound

The sound emitted by tidal turbines overlaps with the generalized hearing ranges for marine mammals and may, consequently, affect marine mammal behavior. Because of the strong environmental protections for marine mammals, these effects are of interest to resource agencies and project developers. The primary goal of this project is to improve the understanding of how sound emitted by tidal turbines may affect marine mammal behavior, with the intent that such understanding may allow this environmental risk to be “retired” for initial demonstrations and arrays. A secondary project goal is to evaluate the efficacy of marine mammal observations from a shore-based vantage point as a method to detect change as a consequence of marine energy stressors. Three, two-week trials were conducted across spring, summer and autumn of 2017 during which vantage-point observations of marine mammals were made from land. We refer to these two-week periods as ‘trials’ as they represent different periods of field data collection. These observations consisted of scan samples every 10 minutes with a set of binoculars and a DSLR camera. When pinnipeds were sighted, still images were taken. When harbor porpoise were sighted, focal follows using video were initiated. These data were later analyzed to estimate the location of animals sighted using photogrammetric techniques. For approximately half of each trial period, a boat was moored in the study area from which an underwater projector was used to play back tidal turbine sounds at a broadband source level of 158 dB re µPa @ 1 m. The other half of each trial period had no playbacks and was used as a control period. During the control and playback periods, ancillary data were collected in the study area near the sea floor (ambient noise, tidal velocity, and porpoise clicks) while weather and vessel tracks were recorded near the vantage-point station. Harbor seals did not show a significant response to the simulated turbine noise, nor did harbor porpoise change their closest point of approach or change the direction/deviation of the paths during periods when the playback was occurring. While porpoise sightings and porpoise acoustic detections were significantly reduced during playback periods in the first two trials, the number of detections during the playback periods increased during the third trial. Similarly, the calculated distance from porpoises to the playback location suggests that harbor porpoises avoided an area around the playback location of approximately 300 m during Trial 1, which decreased to approximately 100 m in Trial 2, and disappeared in Trial 3. This could be an indication of habituation or increased tolerance, either to the playback sound or the vessel presence. Our finding of no significant effect of turbine noise on harbor seals contrast with the findings in a previous study by Hastie et al. (2017) conducted in United Kingdom waters. However, due to substantive differences in source levels and propagation loss, seals in our study would need to have been within 10 m of the playback location to experience similar received levels to those in the Hastie et al. study. Consequently, the two studies may actually be in agreement. Our findings of significant effect of turbine noise on harbor porpoise are in line with previous studies suggesting the harbor porpoise are sensitive to noise. However, we note that, due to cost and logistical constraints, the playback vessel was only mooring during the playback periods. Consequently, we cannot definitively ascribe the changes in porpoise behavior to the simulated turbine noise, playback vessel presence, or a combination of both factors. This methodological limitation and other lessons learned are discussed to benefit future studies that explore the effects of acoustic stressors on marine mammals.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.

McDonough, Benjamin↗
DOE OSTI2017

Construction of a cyber attack model for nuclear power plants - 115

The consideration of how a compromised digital component can impact neighboring components is critical to understanding the progression of cyber attacks. The degree of influence that one component may have on another depends on a variety of factors, including the sharing of resources such as network bandwidth or processing power, the level of trust between components, and the inclusion of segmentation devices such as firewalls. The interactions among components via mechanisms that are unique to the digital world are not usually considered in traditional probabilistic risk assessment (PRA). This means potential sequences of events that may occur during an attack may be missed if one were to only look at conventional accident sequences. This paper presents a method where, starting from the initial attack vector, the progression of a cyber attack can be modeled. The propagation of the attack is modeled by considering certain attributes of the digital components in the system. These attributes determine the potential vulnerability of a component to different classes of attack and the capability gained by the attackers once they are in control of the equipment. The use of attributes allows similar components (components with the same set of attributes) to be modeled in the same way, thereby reducing the computing resources required for analyzing large systems. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Low Level Calibration of Liquid Scintillation Counting

Liquid Scintillation Counting (LSC) is an analytical laboratory method that quantifies the concentrations of alpha and beta emitting isotopes. The LSC method can be utilized for the detection of tritium, both environmentally and at nuclear facilities. Most LSC samples are aqueous-based and prepared as a radioactive sample dissolved or suspended in a high efficiency scintillation cocktail that converts radioactive energy into light pulses. When placed in counting vials, the liquid scintillation cocktail allows for optical coupling in which energy is released from the samples radioactive decay, and transferred to the scintillator. That energy is then absorbed to produce excited electrons that fall back to their ground-state and emit a pulse of light that is detected by the liquid scintillation analyzers photomultiplier tubes. Quench is a signal attenuation due to physical, chemical, or color interferences. To compensate for quench, quench standards of a known concentration are implemented at increasing increments of quench per vial to create a quench curve. Quench standards can be purchased pre-made or customized from the manufacturer, however, there are several issues that arise from purchasing the pre-made quench standards. The main issues with ordering pre-made quench standards is that they use higher count values, between 30,000 and 250,000 disintegrations per minute (dpm) of tritium per vial and only come in glass vials. Using higher counting standards in glass vials makes it hard to see lower concentrations in plastic vials. Additionally, when using higher energy standards the accuracy of the calibration readings tend to be lower. On the other hand, making a set of quench standards in-house allows for the customization of the dpm concentration to roughly 1,500-3,000 and preparation in plastic vials. This allows for lower activity readings that results in closer precision of the samples' true concentration. Technical Objectives: Create a quench curve and calibrate the LSA for tritium. Verify the calibration of the quench curve prior to cross sample runs. Check calibration against Quality Control (QC) reference value. Conclusion: a tritium quench curve calibration was performed using a scintillation cocktail with ultima gold (AB and AB/F), acetone (only with AB), and water (only with AB/F). By performing the tritium quench curve calibration, the counting efficiency was correlated to various levels of quench. Calibration checks with quality control standards were also performed to verify that the calibration was correct. The observed results provided acceptable values that matched the quality control standard reference values with an acceptable uncertainty. The next step will be fine tuning and cross comparison checks with legacy instrumentation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Toward a lean analysis for waste to support the decommissioning operation - 15321

All over the world, several nuclear facilities have been shut down few years ago and are now under dismantling or will be dismantled in the future. - Dismantling project takes place according to three major phases: - Study and definition of the dismantling scenario - Dismantling site operations - Decommissioning and final state characterization. Each step is lead in agreement with safety, security and waste management. This requires analytical tools appropriate to the radiological environment and aims. In the field of security, fissile products, explosives... will be researched and quantified while occupational safety requires quantification of radiological, carcinogenic, mutagenic and reprotoxic elements such as As, Se, Cr(IV)... or even asbestos in order to follow more and more strengthened rules. For waste management, radiological and chemical quantification are necessary but must be completed by determining physical and chemical properties such as reactivity, stability or dissolution of the waste... Required level of knowledge, analytical deadline, uncertainties, limits of detection and matrix varieties depend on the decommissioning phase. Setting up a dismantling scenario requires the most possible accurate knowledge of the initial source term present in the facility, in order to establish waste management flow-sheets, define dismantling intervention modes, and prepare the associated safety demonstrations. As history is often not sufficient, in-situ investigations and off-site laboratories are necessary to assign an initial waste and radiological inventory and propose some strategy operations. When a first scenario is chosen, more detailed samplings and analyses are required to fully prepare estimates of waste volumes and categories, and define and validate the waste recovery system that would be used during the dismantling operation. As the definition of the dismantling plan takes shape, data must be more and more accurate and reliable in order to define the waste outlet. The scaling factors for the waste must be defined in order to assure the refunding of the waste. This includes complete characterizations of various matrixes, with high precision, low detection limit in a limited time of few months, as sketchy data could lead to fix inappropriate plan Once site operations begin, it is necessary to control the safety operational conditions and waste production. Measurements requirement is under the safety threshold. Matrixes are often limited to filter. The most important factor is deadline, because operations delay is costly. Finally at the end of dismantling operations, analyses must be performed to demonstrate the successful clean-up of the work site. Analytical methods with low detection limit are required. Thus, characterization appears as a graded and iterative process and is a key issue for decommissioning and dismantling operations in term of efficiency and cost. In order to face this huge analytical demand, a strategy for sampling and characterization must be developed regarding the real needs. On site measurements are usually performed with existing hand-held instruments. A basic first level of knowledge is obtained without delay, feeding the dismantling scenario. On these bases, a sampling strategy permits to select some representative specimens to be more precise, and accurate analysis is performed on off-site laboratories introducing an additional difficulty: the sampling on the site and sub-sampling in the laboratory itself. Whatever the level of sampling, aliquots are small considering the dimension of the dismantling area. This step is crucial. Sampling strategy must be optimized according to objectives (scaling factors measurement, waste level). But in the particular case of high radioactive level waste, due to security manipulation constraints, the number and mass of samples collected are limited. At the opposite for low level waste, large number of samples is collected meaning that large analytical method capacity is necessary. Optimization of the analytical plan is critical. The analytical strategy (including control of sub-sampling, analysis sequence according to analytical capability and performances, especially for pure beta emitters) developed for real case will be detailed. (authors)

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

Network Algorithms for Detection of Radiation Sources

In support of national defense, Domestic Nuclear Detection Office’s (DNDO) Intelligent Radiation Sensor Systems (IRSS) program supported the development of networks of radiation counters for detecting, localizing and identifying low-level, hazardous radiation sources. Industry teams developed the first generation of such networks with tens of counters, and demonstrated several of their capabilities in indoor and outdoor characterization tests. Subsequently, these test measurements have been used in algorithm replays using various sub-networks of counters. Test measurements combined with algorithm outputs are used to extract Key Measurements and Benchmark (KMB) datasets. We present two selective analyses of these datasets: (a) a notional border monitoring scenario that highlights the benefits of a network of counters compared to individual detectors, and (b) new insights into the Sequential Probability Ratio Test (SPRT) detection method, which lead to its adaptations for improved detection. Using KMB datasets from an outdoor test, we construct a notional border monitoring scenario, wherein twelve 2”*2” NaI detectors are deployed on the periphery of 21*21meter square region. A Cs-137 (175 uCi) source is moved across this region, starting several meters from outside and finally moving away. The measurements from individual counters and the network were processed using replays of a particle filter algorithm developed under IRSS program. The algorithm outputs from KMB datasets clearly illustrate the benefits of combining measurements from all networked counters: the source was detected before it entered the region, during its trajectory inside, and until it moved several meters away. When individual counters are used for detection, the source was detected for much shorter durations, and sometimes was missed in the interior region. The application of SPRT for detecting radiation sources requires choosing the detection threshold, which in turn requires a source strength estimate, typically specified as a multiplier of the background radiation level. A judicious selection of this source multiplier is essential to achieve optimal detection probability at a specified false alarm rate. Typically, this threshold is chosen from the Receiver Operating Characteristic (ROC) by varying the source multiplier estimate. ROC is expected to have a monotonically increasing profile between the detection probability and false alarm rate. We derived ROCs for multiple indoor tests using KMB datasets, which revealed an unexpected loop shape: as the multiplier increases, detection probability and false alarm rate both increase until a limit, and then both contract. Consequently, two detection probabilities correspond to the same false alarm rate, and the higher is achieved at a lower multiplier, which is the desired operating point. Using the Chebyshev’s inequality we analytically confirm this shape. Then, we present two improved network-SPRT methods by (a) using the threshold off-set as a weighting factor for the binary decisions from individual detectors in a weighted majority voting fusion rule, and (b) applying a composite SPRT derived using measurements from all counters.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Rapidly convergent quantum Monte Carlo using a Chebyshev projector

The multireference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although application to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multireference problem. These include a logarithmically scaling metric-tree-based excitation acceptance algorithm to search for determinants connected to the reference space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized reference spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active reference space determinants with essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev projector can be used to accelerate any projector-based QMC algorithm. We show that it requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We benchmark these acceleration methods on the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis sets up to cc-pVQZ quality.

Zhao, Zijun↗