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Active Interrogation using Photofission Technique for Nuclear Materials Control and Accountability

Innovative systems with increased sensitivity and resolution are in great demand to detect diversion and to prevent misuse in support of nuclear materials management for the U.S. fuel cycle. Nuclear fission is the most important multiplicative process involved in non-destructive active interrogation. This process produces the most easily recognizable signature for nuclear materials. In addition to thermal or high-energy neutrons, high-energy gamma rays can also excite a nucleus and cause fission through a process known as photofission. Electron linear accelerators (linacs) are widely used as the interrogating photon sources for inspection methods involving photofission technique. After photofission reactions, prompt signals are much stronger than the delayed signals, but it is difficult to quantify them in practical measurements. Delayed signals are easily distinguishable from the interrogating radiation. Linac-based, advanced inspection techniques utilizing the delayed signals after photofission have been extensively studied for homeland security applications. Previous research also showed that a unique delayed gamma ray energy spectrum exists for each fissionable isotope. In this work, high-energy delayed γ-rays were demonstrated to be signatures for detection, identification, and quantification of special nuclear materials. Such γ-rays were measured in between linac pulses using independent data acquisition systems. A list-mode system was developed to measure low-energy delayed γ-rays after irradiation. Photofission product yields of 238 U and 239 Pu were determined based on the measured delayed γ-ray spectra. The differential yields of delayed γ-rays were also proven to be able to discriminate nuclear from non-nuclear materials. The measurement outcomes were compared with Monte Carlo simulation results. It was demonstrated that the current available codes have capabilities and limitations in the simulation of photofission process. A two-fold approach was used to address the high-rate challenge in used nuclear fuel assay based on photofission technique. First, a standard HPGe preamplifier was modified to improve its capabilities in high-rate pulsed photofission environment. Second, advanced pulse processing algorithms were shown to greatly improve throughput rate without large sacrifice in energy resolution at ultra-high input count rate. Two customized gamma spectroscopy systems were also developed in real-time on FPGAs. They were shown to have promising performance matching available commercial units.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Modular machine learning-based elastoplasticity: Generalization in the context of limited data

The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.

42 ENGINEERING↗

Optimization of a high-yield, low-areal-density fusion product source at the National Ignition Facility with applications in nucleosynthesis experiments

Polar-direct-drive exploding pushers are used as a high-yield, low-areal-density fusion product source at the National Ignition Facility with applications including diagnostic calibration, nuclear security, backlighting, electron-ion equilibration, and nucleosynthesis-relevant experiments. In this paper, two different paths to improving the performance of this platform are explored: (i) optimizing the laser drive, and (ii) optimizing the target. While the present study is specifically geared towards nucleosynthesis experiments, the results are generally applicable. Example data from T 2 / 3 He-gas-filled implosions with trace deuterium are used to show that yield and ion temperature (Tion) from 1.6 mm-outer-diameter thin-glass-shell capsule implosions are improved at a set laser energy by switching from a ramped to a square laser pulse shape, and that increased laser energy further improves yield and Tion, although by factors lower than predicted by 1 D simulations. Using data from D2-3He-gas-filled implosions, yield at a set Tion is experimentally verified to increase with capsule size. Uniform D-3He-proton spectra from 3 mm-outer-diameter CH shell implosions demonstrate the utility of this platform for studying charged-particle-producing reactions relevant to stellar nucleosynthesis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Ocean Energy Sandia TEAMER WEC Simulation Results

Computational fluid simulations for wave energy converters and supporting materials from Ocean Energy's WEC Buoy TEAMER project in collaboration with Sandia National Laboratories. Each file includes images and video of simulation along with the simulation files for the case. The videos and images include air and water simulations of the buoy in 2D and 3D views. Data spreadsheets of the simulation outputs are also included in the files. The data are represented as different cases in which the wave height(Hs) and peak period(Tp) were different.

16 TIDAL AND WAVE POWER↗

Additive Manufacturing with Cellulose-Based Composites: Materials, Modeling, and Applications

Recent advances in large-scale additive manufacturing (AM) with polymer-based composites have enabled efficient production of high-performance materials. Cellulose nanomaterials (CNMs) have emerged as bio-based feedstocks due to their exceptional strength and sustainability. However, challenges such as hornification and poor dispersion in polymer matrices still limit large-scale CNM–polymer composite manufacturing, requiring novel strategies. Here, this review outlines an approach starting with atomic-level simulations to link molecular composition to key parameters like bulk density, viscosity, and modulus. These simulations provide data for finite element analysis (FEA), which informs large-scale experiments and reduces the need for extensive trials. The strategy explores how atomic interactions impact the morphology, adhesion, and mechanical properties of CNM-based composites in AM processes. The review also discusses current developments in AM, along with predictions of mechanical and thermal properties for structural applications, packaging, flexible electronics, and hydrogel scaffolds. By integrating experimental findings with molecular dynamics (MD) simulations and finite element modeling (FEM), valuable insights for material design, process optimization, and performance enhancement in CNM-based AM are provided to address ongoing challenges.

36 MATERIALS SCIENCE↗

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Automatic Analyte-Ion Recognition and Background Removal for Ambient Mass-Spectrometric Data Based on Cross-Correlation

Ambient mass spectrometry is a powerful approach for rapid, high-throughput, and direct sample analysis. Due to the open-air desorption and ionization processes, random fluctuations of ambient conditions can lead to large variances in mass-spectral signals over time. The mass-spectral data also can be further complicated due to multiple analytes present in the sample, background-ion signals stemming from the desorption/ionization source itself, and other laboratory-specific conditions (e.g., ambient laboratory air, nearby hardware). Thus, background removal and analyte-ion recognition can be quite difficult, particularly in non-targeted analyses. Here, we demonstrate the use of a cross-correlation-based approach to exploit chemical information encoded in the time domain to group/categorize mass-spectral peaks from a single analysis dataset. Ions that originate from ambient (or other) background species were readily flagged and removed from spectra; the result was a decrease in mass-spectral complexity by over 70% due to the removal of these background ions. Meanwhile, analyte ions were differentiated and categorized based on their time-domain profiles. With sufficient mass resolving-power and mass-spectral acquisition rate, isolated mass spectra containing ions from the same species in a sample could be extracted, leading to a reduction in mass-spectral complexity by more than 98% in some cases. The cross-correlation approach was tested with different ionization sources as well as reproducible and irreproducible sample introduction. Software built in-house enabled fully automated data processing, which can be performed within a few seconds. Ultimately, this approach provides an additional dimension of analyte separation in ambient mass-spectrometric analyses with information that is already recorded throughout the analysis. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

FUSRAP Case Study - Benefits and Lessons Learned from Implementation of a Modern Data Solution at Maywood - 18399

This paper focuses on data production and management practices dating back to 1999 that continue to evolve to support the project today. In 2000, the FUSRAP Maywood Project began using a software environmental data management system and laboratory information management system, developed by a single database administrator (DA) and authored in Microsoft Access{sup TM} (MS Access). The original databases included basic functionality including sample number assignment and label generation, tracking of LIMS gamma spec QC and assignment of basic qualifiers and basic database queries. The DA continued over the next fifteen years to refine the EDMS/LIMS by integrating project-specific data evaluation guidance document processes and Quality Assurance/Quality Control (QA/QC) charting and excursion warning tools. With the retirement of the original DA, the U.S. Army Corps of Engineers (USACE)-Cabrera Team procured and implemented a commercial-leaning solution using modern database tools and an established EDMS vendor. For the database transition process, the project team established and configured the new EDMS/LIMS with basic architecture and accessibility; evaluated existing workflows; established/revised LIMS and EDMS-related documentation (manuals and procedures); and conducted dry runs and validation testing. The overall team coordinated closely to facilitate a seamless transition from initial assessment and authoring, to data migration, and finally to resumption of normal ongoing operations. Key features of the new EDMS Subcontractor's software included lookup tables, an import wizard, data checking options and unique indexes. Since the database systems became operational, the technical staff gathered useful feedback from end users (e.g., lab staff, field engineers, task managers, and the third-party data validator) to refine the EDMS/LIMS operation. This feedback triggered improvements in the performance of onsite laboratory qualification forms, promoted consistency in sample ID numbers between the database sample planning screens and field chain-of-custodies, corrected bugs in the LIMS Duplicate Evaluation Report, eliminated data import issues such as blank, inconsistent or missing data from key fields. One of the advantages of the commercial database solution was that it allowed the project to cut cost over time due to the lower cost of database maintenance provided by the commercial database vendor versus the cost of an on-staff DA. Cost savings are estimated at $40,000 per year. There were also indirect cost savings resulting from implementation of the new database since it allowed better integration of the LIMS and EDMS. While there will always be challenges associated with long term records/data management for any old remediation site, the Maywood Team's approach to an upgraded EDMS/LIMS implementation serves as one element of the overall effort to enhance the integrity and reproducibility of critical data that supports the remediation program while at the same time, more effectively using available resources. These lessons-learned will enhance implementation of cost-effective solutions for similar environmental remediation sites. (authors)

97 MATHEMATICS AND COMPUTING↗

Neutron-neutron angular correlations in spontaneous fission of Cf 252 and Pu 240

Background: Angular anisotropy has been observed between prompt neutrons emitted during the fission process. Such an anisotropy arises because the emitted neutrons are boosted along the direction of the parent fragment. Purpose: To measure the neutron-neutron angular correlations from the spontaneous fission of 252 Cf and 240 Pu oxide samples using a liquid scintillator array capable of pulse-shape discrimination. To compare these correlations to simulations combining the Monte Carlo radiation transport code MCNPX with the fission event generator FREYA. Method: Two different analysis methods were used to study the neutron-neutron correlations with varying energy thresholds. The first is based on setting a light output threshold while the second imposes a time-of-flight cutoff. The second method has the advantage of being truly detector independent. Results: The neutron-neutron correlation modeled by FREYA depends strongly on the sharing of the excitation energy between the two fragments. The measured asymmetry enabled us to adjust the FREYA parameter x in 240 Pu , which controls the energy partition between the fragments and is so far inaccessible in other measurements. The 240 Pu data in this analysis was the first available to quantify the energy partition for this isotope. The agreement between data and simulation is overall very good for 252 Cf ( sf ) and 240 Pu ( sf ). Conclusions: The asymmetry in the measured neutron-neutron angular distributions can be predicted by FREYA. The shape of the correlation function depends on how the excitation energy is partitioned between the two fission fragments. Lastly, experimental data suggest that the lighter fragment is disproportionately excited.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Optimization of a high-yield, low-areal-density fusion product source at the National Ignition Facility with applications in nucleosynthesis experiments

Polar-direct-drive exploding pushers are used as a high-yield, low-areal-density fusion product source at the National Ignition Facility with applications including diagnostic calibration, nuclear security, backlighting, electron-ion equilibration and nucleosynthesis-relevant experiments. In this paper, two different paths to improving the performance of this platform are explored: (i) optimizing the laser drive, and (ii) optimizing the target. While the present study is specifically geared towards nucleosynthesis experiments, the results are generally applicable. Example data from T[sub 2]/[sup 3]He-gas-filled implosions with trace deuterium are used to show that yield and ion temperature (T[sub ion]) from 1.6mm-outer-diameter thin-glass-shell capsule implosions are improved at a set laser energy by switching from a ramped to a square laser pulse shape, and that increased laser energy further improves yield and Tion, although by factors lower than predicted by 1D simulations. Using data from D[sub 2]/[sup 3]He-gas-filled implosions, yield at a set T[sub ion] is experimentally verified to increase with capsule size. Uniform D[sup 3]He-proton spectra from 3mm-outer-diameter CH shell implosions demonstrate the utility of this platform for studying charged-particle-producing reactions relevant to stellar nucleosynthesis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗

Variable length adjacent partitioning for PTS based PAPR reduction of OFDM signal

Peak-to-Average power ratio (PAPR) is a major drawback in OFDM communication. It leads the power amplifier into nonlinear region operation resulting into loss of data integrity. As such, there is a strong motivation to find techniques to reduce PAPR. Partial Transmit Sequence (PTS) is an attractive scheme for this purpose. Judicious partitioning the OFDM data frame into disjoint subsets is a pivotal component of any PTS scheme. Out of the existing partitioning techniques, adjacent partitioning is characterized by an attractive trade-off between cost and performance. With an aim of determining effects of length variability of adjacent partitions, we performed an investigation into the performances of a variable length adjacent partitioning (VL-AP) and fixed length adjacent partitioning in comparison with other partitioning schemes such as pseudorandom partitioning. Simulation results with different modulation and partitioning scenarios showed that fixed length adjacent partition had better performance compared to variable length adjacent partitioning. As expected, simulation results showed a slightly better performance of pseudorandom partitioning technique compared to fixed and variable adjacent partitioning schemes. However, as the pseudorandom technique incurs high computational complexities, adjacent partitioning schemes were still seen as favorable candidates for PAPR reduction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Streaming Compression of Scientific Data via Weak-SINDy

Here, in this paper, a streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. The production of scientific data, either via simulation or experiments, is undergoing a stage of exponential growth, which makes data compression important and often necessary for storing and utilizing large scientific data sets. As opposed to classical “offline” compression algorithms that perform compression on a readily available data set, streaming compression algorithms compress data “online” while the data generated from simulation or experiments is still flowing through the system. This feature makes streaming compression algorithms well suited for scientific data compression, where storing the full data set offline is often infeasible. This work proposes a new streaming compression algorithm, streaming weak-SINDy, which takes advantage of the underlying data characteristics during compression. The streaming weak-SINDy algorithm constructs feature matrices and target vectors in the online stage via a streaming integration method in a memory efficient manner. The feature matrices and target vectors are then used in the offline stage to build a model through a regression process that aims to recover equations that govern the evolution of the data. For compressing high-dimensional streaming data, we adopt a streaming proper orthogonal decomposition (POD) process to reduce the data dimension and then use the streaming weak-SINDy algorithm to compress the temporal data of the POD expansion. We propose modifications to the streaming weak-SINDy algorithm to accommodate the dynamically updated POD basis. By combining the built model from the streaming weak-SINDy algorithm and a small amount of data samples, the full data flow could be reconstructed accurately at a low memory cost, as shown in the numerical tests.

97 MATHEMATICS AND COMPUTING↗

Particle-based fast jet simulation at the LHC with variational autoencoders

Abstract We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a deep VAE to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Global Analysis of Perovskite Photophysics Reveals Importance of Geminate Pathways

Hybrid organic-inorganic perovskites demonstrate desirable photophysical behaviors and promising applications from efficient photovoltaics to lasing, but the fundamental nature of excited state species is still under debate. We also collected time-resolved photoluminescence of single-crystal nanoplates of methylammonium lead iodide perovskite (MAPbI3), with excitation over a range of fluences and repetition rates, to provide a more complete photophysical picture. A fundamentally different way of simulating the photophysics is developed that relies on unnormalized decays, global analysis over a large array of conditions, and inclusion of steady-state behavior; these details are critical to capturing observed behaviors. These additional constraints require inclusion of spatially-correlated pairs, along with free carriers and traps, demonstrating the importance of our comprehensive analysis. Modeling geminate and non-geminate pathways shows geminate processes are dominant at high carrier densities and early times. This combination of data and simulation provides a detailed picture of perovskite photophysics across multiple excitation regimes that was not previously available.

14 SOLAR ENERGY↗

Kinetic physics in ICF: present understanding and future directions

Kinetic physics has the potential to impact the performance of indirect-drive inertial confinement fusion (ICF) experiments. Systematic anomalies in the National Ignition Facility implosion dataset have been identified in which kinetic physics may play a role, including inferred missing energy in the hohlraum, drive asymmetry in near-vacuum hohlraums, low areal density and high burn-averaged ion temperatures (T i ) compared with mainline simulations, and low ratios of the DD-neutron and DT-neutron yields and inferred T i . Several components of ICF implosions are likely to be influenced or dominated by kinetic physics: laser-plasma interactions in the LEH and hohlraum interior; the hohlraum wall blowoff, blowoff/gas and blowoff/ablator interfaces; the ablator and ablator/ice interface; and the DT fuel all present conditions in which kinetic physics can significantly affect the dynamics. This review presents the assembled experimental data and simulation results to date, which indicate that the effects of long mean-free-path plasma phenomena and self-generated electromagnetic fields may have a significant impact in ICF targets. Finally, simulation and experimental efforts are proposed to definitively quantify the importance of these effects at ignition-relevant conditions, including priorities for ongoing study.

42 ENGINEERING↗

Evaluation of linear interpolation method for missing value on solar radiation dataset in Perlis

This paper intends to reveal the ability of the linear interpolation method to predict missing values in solar radiation time series. Reliable dataset is equally tends to complete time series observed dataset. The absence or presence of radiation data alters long-term variation of solar radiation measurement values. Based on that change, the opportunities to provide bias output result for modelling and the validation process is higher. The completeness of the observed variable dataset has significantly important for data analysis. Occurrence the lack of continual and unreliable time series solar radiation data widely spread and become the main problematic issue. However, the limited number of research quantity that has carried out to emphasize and gives full attention to estimate missing values in the solar radiation dataset.

42 ENGINEERING↗

Utilization of the Differential Die-Away Self-Interrogation Technique for Characterization and Verification of Spent Nuclear Fuel

New nondestructive assay techniques are sought to better characterize spent nuclear fuel. One of the NDA instruments selected for possible deployment is differential die-away self-interrogation (DDSI). The proposed DDSI approach for spent fuel assembly assay utilizes primarily the spontaneous fission and (α, n) neutrons in the assemblies as an internal interrogating radiation source. The neutrons released in spontaneous fission or (α,n) reactions are thermalized in the surrounding water and induce fission in fissile isotopes, thereby creating a measurable signal from isotopes of interest that would be otherwise difficult to measure. The DDSI instrument employs neutron coincidence counting with 3He tubes and list-mode-based data acquisition to allow for production of Rossi-alpha distributions (RADs) in post-processing. The list-mode approach to data collection and subsequent construction of RADs has expanded the analytical possibilities, as will be demonstrated throughout this thesis. One of the primary advantages is that the measured signal in the form of a RAD can be analyzed in its entirety including determination of die-away times in different time domains. This capability led to the development of the early die-away method, a novel leakage multiplication determination method which is tested throughout the thesis on different sources in simulation space and fresh fuel experiments. The early die-away method is a robust, accurate, improved method of determining multiplication without the need for knowledge of the (α,n) source term. The DDSI technique and instrument are presented along with the many novel capabilities enabled by and discovered through RAD analysis. Among the new capabilities presented are the early die-away method, total plutonium content determination, and highly sensitive missing pin detection. Simulation of hundreds of different spent and fresh fuel assemblies were used to develop the analysis algorithms and the techniques were tested on a variety of spontaneous fission-driven fresh fuel assemblies at Los Alamos National Laboratory and the BeRP ball at the Nevada National Security Site. The development of the new, improved analysis and characterization methods with the DDSI instrument makes it a viable technique for implementation in a facility to meet material control and safeguards needs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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