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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Determination of the Distribution and Inventory of Radionuclides within a Savannah River Site Waterway - 13202

An investigation was conducted to evaluate the radionuclide inventory within the Lower Three Runs (LTR) Integrator Operable Unit (IOU) at the U.S. Department of Energy's (DOE's) Savannah River Site (SRS). The scope of this effort included the analysis of previously existing sampling and analysis data as well as additional stream bed and flood plain sampling and analysis data acquired to delineate horizontal and vertical distributions of the radionuclide as part of the ongoing SRS environmental restoration program, and specifically for the LTR IOU program. While cesium-137 (Cs-137) is the most significant and abundant radionuclide associated with the LTR IOU it is not the only radionuclide, hence the scope included evaluating all radionuclides present and includes an evaluation of inventory uncertainty for use in sensitivity and uncertainty analyses. The scope involved evaluation of the radionuclide inventory in the P-Reactor and R-Reactor cooling water effluent canal systems, PAR Pond (including Pond C) and the flood plain and stream sediment sections of LTR between the PAR Pond Dam and the Savannah River. The approach taken was to examine all of the available Sediment and Sediment/Soil analysis data available along the P- and R-Reactor cooling water re-circulation canal system, the ponds situated along those canal reaches and along the length of LTR below Par Pond dam. By breaking the IOU into a series of sub-components and sub-sections, the mass of contaminated material was estimated and a representative central concentration of each radionuclide was computed for each compartment. The radionuclide inventory associated with each sub-compartment was then aggregated to determine the total radionuclide inventory that represented the full LTR IOU. Of special interest was the inventory of Cs-137 due to its role in contributing to the potential dose to an offsite member of the public. The overall LTR IOU inventory of Cs-137 was determined to be 2.87 E+02 GBq, which is similar to two earlier estimates. This investigation provides an independent, ground-up estimate of Cs-137 inventory in LTR IOU utilizing the most recent field data. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Constraining Carbon and Nutrient Flows in Soil With Ecological Stoichiometry

Soil organic matter (SOM) is central to soil carbon (C) storage and terrestrial nutrient cycling. New data have upended the traditional model of stabilization, which held that stable SOM was mostly made of undecomposed plant molecules. We now know that microbial by-products and dead cells comprise unexpectedly large amounts of stable SOM because they can become attached to mineral surfaces or physically protected within soil aggregates. SOM models have been built to incorporate the microbial to mineral stabilization of organic matter, but now face a new challenge of accurately capturing microbial productivity and metabolism. Explicitly representing stoichiometry, the relative nutrient requirements for growth and maintenance of organisms, could provide a way forward. Stoichiometry limits SOM formation and turnover in nature, but important nutrients like nitrogen (N), phosphorus (P), and sulfur (S) are often missing from the new generation of SOM models. In this synthesis, we seek to facilitate the addition of these nutrients to SOM models by (1) reviewing the stoichiometric bias—the tendency to favor one element over another—of four key processes in the new framework of SOM cycling and (2) applying this knowledge to build a stoichiometrically explicit budget of C, N, P, and S flow through the major SOM pools. By quantifying the role of stoichiometry in SOM cycling, we discover that constraining the C:N:P:S ratio of microorganisms and SOM to specific values reduces uncertainty in C and nutrient flow as effectively as using microbial C use efficiency (CUE) parameters. We find that the value of additional constraints on stoichiometry vs. CUE varies across ecosystems, depending on how precise the available data are for that ecosystem and which biogeochemical pathways are present. Moreover, because CUE summarizes many different processes, stoichiometric measurements of key soil pools are likely to be more robust when extrapolated from soil incubations to plot or biome scale estimates. Our results suggest that measuring SOM stoichiometry should be a priority for future empirical work and that the inclusion of new nutrients in SOM models may be an effective way to improve precision.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK —a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Criteria Air Pollutants and Greenhouse Gas Emissions from Hydrogen Production in U.S. Steam Methane Reforming Facilities

The global and U.S. domestic effort to develop a clean energy economy and curb environmental pollution incentivizes the use of hydrogen as a transportation fuel, owing to its zero tailpipe pollutant emissions and high fuel efficiency in fuel cell electric vehicles (FCEVs). However, the hydrogen production process is not emissions free. Conventional hydrogen production via steam methane reforming (SMR) is energy intensive, coproduces carbon dioxide, and emits air pollutants. Therefore, it is necessary to quantify the environmental impacts of SMR hydrogen production alongside the use-phase of FCEVs. This study fills the information gap, analyzing the greenhouse gas (GHG) and criteria air pollutant (CAP) emissions associated with hydrogen production in U.S. SMR facilities by compiling and matching the facility-reported GHG and CAP emissions data with facilities’ hydrogen production data. The actual amounts of hydrogen produced at U.S. SMR facilities are often confidential. Thus, we have developed four approaches to estimate the hydrogen production amounts. The resultant GHG and CAP emissions per MJ of hydrogen produced in individual facilities were aggregated to develop emission values for both a national median and a California state median. Lastly, this study also investigates the breakdown of facility emissions into combustion emissions and noncombustion emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants

Recent events in Odessa [1], [2] have shed light on the complexities of integrating large Inverter-Based Resource (IBR) plants with the transmission system, prompting NERC to stress continuous performance monitoring by transmission operators. Challenges such as plant control updates, IBR model revisions, Phase-locked loop loss of synchronism, and protection events have been identified, underscoring the need for enhanced monitoring protocols by regulatory bodies. The recent FERC 901 order underscores the importance of accurate data exchange regarding IBRs for reliability studies. However, limited access to IBR plant-related data hampers effective decision-making for transmission operators (TOP). This paper proposes a method for constructing data-driven User-Defined dynamic Models (UDM) for power plants for validating multiple-event data using field measurements from interconnection bus locations. The problem is formulated as a power plant model identification problem and a multi-task learning approach under partial input observability assumptions is proposed in this work. This approach aims to predict aggregated responses of conventional and IBR power plants during various dynamic physical events which is useful for planning studies under diverse disturbance conditions. Ultimately, this methodology emphasizes the importance of plant visibility to operators in addressing power system challenges, facilitating improved planning and operational studies.

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2020

Comparing traditional and Bayesian approaches to ecological meta‐analysis

Abstract Despite the wide application of meta‐analysis in ecology, some of the traditional methods used for meta‐analysis may not perform well given the type of data characteristic of ecological meta‐analyses. We reviewed published meta‐analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies ( n i ) and the number of studies or records ( k ) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta‐analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that n i and k were highly variable, distributions were right‐skewed and were generally small (median n i = 5, median k = 44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval (CI) based on the t distribution that uses an adjusted standard error (the Hartung–Knapp–Sidik–Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z distribution CIs had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta‐analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random‐effects meta‐analysis, the performance of the best frequentist and Bayesian methods was similar for the same combinations of factors ( k and mean replication), though the Bayesian approach had higher than nominal (>95%) coverage for the mean effect when k was very low ( k < 15). Our literature review suggests that many meta‐analyses that used z distribution or bootstrapping CIs may have overestimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta‐analyses.

Pappalardo, Paula↗
DOE OSTI2018

COMMON – Communications System Monitor v. 1.5

COMMON is a tool for collecting, storing, and displaying critical information to facilitate analysis on Microsoft Windows based IT systems. COMMON was developed to provide insight into the IT infrastructure for operational radiation detection systems, but is applicable to most Windows-based operating systems and TCP/IP network components. The COMMON tool aggregates a host of important Windows Management Instrumentation (WMI) information and as well as network ping responses to provide valuable insight into the performance of the IT system. A unique feature is the creation of daily output files (JSON) from the data collected for off site evaluation by subject matter experts.

Aragon, MonaL↗
DOE OSTI2022

ASP (Adaptive Splines for Prediction) [SWR-22-66]

This software package provides functionality to fit piecewise cubic splines to model the relationship between a univariate X (independent variable) and a univariate Y (dependent variable). The output is akin to linear regression but has higher "capacity" in that it can learn more complicated relationships despite leveraging only a single predictor. It thus can learn very reasonable relationships between X and Y with minimal effort. The canonical intended use case is for predicting electric demand (Y) from temperature (X). There can be complicated, non-linear relationships between these two variables, but these can be reasonably approximated by the sum of: 1) an "aggregate" relationship between daily average temperature and daily average demand, and 2) intra-day patterns represented as hourly offsets from the daily average load. The spline functionality is used for learning a robust "aggregate" relationship without the inconvenience of manual feature engineering while still ensuring robust results. The model fitting workhorse is R's built-in smooth.spline. This package provides a suite of options for controlling how smooth.spline fits a spline to the data. In particular, it is designed to return a model with a single critical point (i.e., a single location along the domain/support where the first derivative is zero). This is to ensure that predicted changes in electric demand are always positive as temperatures become more extreme. This helps to prevent overfitting, particularly when the input data has few observations or has significant uncertainty from other sources.

Murphy, Sinnott↗
DOE OSTI2026

National Summary Statistics for Depot-Based Medium- and Heavy-Duty Vehicle Operations

This file provides nationally aggregated summary statistics to characterize the daily operations of medium- and heavy-duty vehicles. It provides typical daily driving distances, domicile dwell durations, and one version of potential normalized electric vehicle depot charging load curves. The methodology to create these data was designed with national representativeness in mind, and the data are suitable for national analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Final Scientific/Technical Report: Real Time Particle Size and Settling Velocity for In-Situ Monitoring of Deep-Sea Polymetallic Nodule Mining

In the coming decade, it is anticipated that deep-sea mining activities will commence throughout the deep ocean, at depths around 4000-6000m where vast deposits of baseball-sized rocks called polymetallic nodules lay on the seabed. These nodules contain resources such as cobalt, copper and nickel which are needed to produce batteries for electric vehicles. Collector machines driving on the seabed and gathering these nodules will stir up clouds of sediment, and there is concern about how the resulting concentration of sediment in the near-bottom ocean water will affect deep and mid-ocean biology. There is currently no technology capable of comprehensively monitoring the concentrations and properties of stirred up sediment in the deep. This project addressed this critical gap by developing a deep-sea particle measurement system (Real-Time Size and Settling Velocity, RTSSV) that uses a multi-camera high-resolution video imaging system, with accompanying image processing, to measure the concentration, size distribution and settling speed of sediment. Importantly, these measurements are made in-situ, so that delicate sediment aggregates will not be disturbed, as is the case when collecting samples at depth to be measured in the lab. The instrument can be mounted on a variety of established deep-sea platforms such as moorings, AUVs and ROVs, providing data in near real time, where the key measurements need to be made—from the source of the sediment disturbance and throughout the resulting plume—vital for determining if deep-sea mining can be done in an environmentally responsible manner. The developed system (RTSSV) will possess the only such capability worldwide, giving a US-based group a substantial competitive advantage as this newly evolving global industry develops. As added value, the technology will also have widespread application to the offshore oil and gas industry, dredging industry, and to sediment science research.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

National Community Solar Partnership (NCSP+) Resource List

NLR compiles and makes quarterly updates to this dataset of publications related to expanding access to solar through its support for the National Community Solar Partnership (NCSP+). The purpose of the list is to help NCSP+ partners and others to find relevant resources on community solar, low- and moderate-income residential rooftop solar + storage, community-serving commercial solar projects, microgrids, and distributed solar + storage aggregations such as virtual power plants serving low-income communities. Resource types include journal articles, reports, fact sheets, presentations, videos, datasets, modeling tools, webinars, and other formats. A web-based version of the database is available on the NCSP+ Data Hub at: https://openei.org/wiki/NCSP_Hub/Resources .

14 SOLAR ENERGY↗
DOE OSTI2016

Comfort in High-Performance Homes in a Hot-Humid Climate

TO5 2.3.2: Large-Scale Temperature Study Although space-conditioning systems are required to meet standards such as those in the Air Conditioning Contractors of America (ACCA) Manual RS (Rutkowski 1997) and the ASHRAE Standard 55 (ASHRAE 2013) comfort criteria, little is known about how space-conditioning systems are actually operating in the field and if these systems are meeting those standards. Unconventional space-conditioning systems may be necessary for low-load homes in the future. It is important to know how current systems are functioning in existing homes and to what temperatures occupants typically set the thermostats in their homes. The U.S. Department of Energy's Building America research team IBACOS set out to find some answers. By using low-cost data loggers and occupant surveys to monitor 37 single-family homes that have standard heating, ventilating, and air-conditioning (HVAC) equipment, the team gained insight into how sensitive occupants are to comfort in their homes relative to the ACCA and ASHRAE standards. This study focused on one climate zone (hot-humid); however, future studies may find differences among occupants- ideas of thermal comfort in other climate zones. Data were collected from installed sensors during a 2-month period from late August 2014 through late October 2014. The final aspect of this project was to review the business metrics associated with builders involved in a comfort and performance guarantee program. The results of this study-taken as an aggregate-show that the homes provided room-to-room temperature differences less than 6 degrees F 95% of the time. Temperature differences were less than 4 degrees F 80% of the time. Some homes showed better or worse performance. On average, two-story homes had a 3.3 degrees F temperature difference between rooms; single-story homes had an average of a 2.2 degrees F temperature difference between rooms. Occupants who did not use programmable thermostats had an average set point of 75 degrees F. Homes in which the thermostats were programmed with a setback schedule showed a median baseline temperature value of 74 degrees F and had a median afternoon setback of 75 degrees F. System runtime was analyzed and showed that the room-to-room temperature uniformity in some homes worsened during an on cycle, whereas the uniformity improved in other homes. That is, when the system was running, some rooms were receiving too much or too little air relative to the rest of the house, and the temperatures in those rooms were moving away from the temperatures of the other rooms in the house. Finally, analysis of the thermostat data shows a wide degree of variability among homes in the study. Interpretation of the measured data suggests that 21 occupants made no regular adjustments to the thermostats in their homes, seven had regular setbacks, seven showed varying setbacks, and 10 appeared to follow no pattern (with random adjustments). Ultimately, the data show that current systems are maintaining expected levels of comfort. As expected, it is more challenging to maintain a uniform temperature in all rooms of two-story homes, and future systems should address this issue. Furthermore, the data show that the majority of homes maintained relative humidity levels below 60% without any supplemental dehumidification

ACCA Manual RS↗
DOE OSTI2020

Effects of Deep Reductions in Energy Storage Costs on Highly Reliable Wind and Solar Electricity Systems.

We use 36 years (1980-2015) of hourly weather data over the contiguous United States (CONUS) to assess the impact of low-cost energy storage on highly reliable electricity systems that use only variable renewable energy (VRE; wind and solar photovoltaics). Even assuming perfect transmission of wind and solar generation aggregated over CONUS, energy storage costs would need to decrease several hundred-fold from current costs (to ~$1/kWh) in fully VRE electricity systems to yield highly reliable electricity without extensive curtailment of VRE generation. The role of energy storage changes from high-cost storage competing with curtailment to fill short-term gaps between VRE generation and hourly demand to near-free storage serving as seasonal storage for VRE resources. Energy storage faces "double penalties" in VRE/storage systems: with increasing capacity, (1) the additional storage is used less frequently and (2) hourly electricity costs would become less volatile, thus reducing price arbitrage opportunities for the additional storage.

14 SOLAR ENERGY↗
DOE OSTI2018

ARIES Blend Lot Book Data Analysis

The Advanced Recovery Integrated Extraction System (ARIES) Program being executed at Los Alamos TA-55 produces unpolished plutonium oxide powder from pit disassembly in batches of material referred to as a blend lot. Each blend lot is sampled and analyzed by Los Alamos group C-AAC (Chemistry Division, Actinide Analytical Chemistry Group) for radioactive and nonradioactive constituents contained in the material. A blend lot, which nominally consists of 12 kg(Pu), is then packaged into three DOE-STD-3013 containers that are analyzed by nondestructive analysis (NDA) to determine the fraction and total amount of plutonium mass relative to impure plutonium oxide. The analytical and NDA data are presented in a Blend Lot Book (BLB). The data are used to confirm that a blend lot grouping of three cans meets the requirements for off-site shipment specified in the formal interface control document (ICD). The analytical and NDA data from 49 blend lots were reviewed to provide summary-level information on the constituents present in the blend lots. The results provide an overall perspective in historical amounts for constituent averages over all blend lots and similar information in the variation between constituent levels. Note that to properly compare the can contents, it would be most appropriate to decay all of cans’ constituents to a particular date. Previously, this analysis only examined statistical data as determined at the time of blend lot chemical analysis. The chemical analysis dates span the period from June 2011 to May 2015 (4 years), which is 28% of one Pu-241 half-life. Therefore, the approximate nature of the aggregate information must be properly understood in this light. However, to address this issue, a new table is included in this revision that decays the 48 blend lots to a single date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Data from: Comparing traditional and Bayesian approaches to ecological meta-analysis

Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Chapter 6 Assessing Transformation Pathways. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

Stabilizing greenhouse gas (GHG) concentrations will require large-scale transformations in human societies, from the way that we produce and consume energy to how we use the land surface. A natural question in this context is what will be the ‘transformation pathway’ towards stabilization; that is, how do we get from here to there? The topic of this chapter is transformation pathways. The chapter is primarily motivated by three questions. First, what are the near-term and future choices that define transformation pathways, including the goal itself, the emissions pathway to the goal, technologies used for and sectors contributing to mitigation, the nature of international coordination, and mitigation policies? Second, what are the key characteristics of different transformation pathways, including the rates of emissions reductions and deployment of low-carbon energy, the magnitude and timing of aggregate economic costs, and the implications for other policy objectives such as those generally associated with sustainable development? Third, how will actions taken today influence the options that might be available in the future? As part of the assessment in this chapter, data from over 1000 new scenarios published since the IPCC Fourth Assessment Report (AR4) were collected from integrated modelling research groups, many from large-scale model intercomparison studies. In comparison to AR4, new scenarios, both in this AR5 dataset and more broadly in the literature assessed in this chapter, consider more ambitious concentration goals, a wider range of assumptions about technology, and more possibilities for delays in global mitigation and fragmented international action.

Clarke, Leon E.↗
DOE OSTI2024

Application of Banking Scoring and Rating for Coherent Risk Measures in Electricity Systems ABSCORES

This project developed a framework for asset and system risk management that can be incorporated into current electricity system operations to improve economic efficiency and establish an Electric Assets Risk Bureau. We leveraged scoring and ratings from banking and financial institutions alongside current optimization methods in dispatching power systems to help system operators and electricity markets schedule resources. This approach is based on the observation that there are major discrepancies between the power scheduled by a system operator and the actual power generated/consumed. These discrepancies—exacerbated by unplanned contingencies (e.g., natural disasters)—are caused by multiple factors, including the different financial, environmental and risk preferences of power producers, consumers, and aggregators. We developed a framework that counteracts two failures in electricity system operations: imperfect information and missing markets for products. The technical approach included five tasks. Tasks 1 and 2 supported the development of risk scores at the asset level with historical data collected for this project. Tasks 3, 4, and 5 incorporated scoring into decision-making at the system level. The proposed effort achieved PERFORM's Program Objectives because the proposed outputs and algorithms do not exist in the electricity industry and are an innovative approach to managing risk. Since the acknowledged need to better assess and act upon risk profiles for grid assets has not been met by the industry, this project will also impact ARPA-E's Mission Areas, including improving energy efficiency and giving the U.S. a technological lead in advanced energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

End-to-End Simulations of a 3.4-Meter Detector Wall for Neutron-Diagnosed Subcritical Experiments

The Nevada National Security Site, together with Los Alamos National Laboratory and Lawrence Livermore National Laboratory, is developing a novel diagnostic to measure the reactivity of subcritical experiments. This capability is known as neutron-diagnosed subcritical experiments. The decay of the fission gamma rays from the neutron-interrogated subcritical experiment is measured as a function of time with a large (~3-meter diameter) detector wall consisting of 151 individual detector pixels. The data from this current mode measurement inform the neutron multiplication factor, keff, and thus the relative reactivity of the subcritical experiment. The Nevada National Security Site developed the Gamma Array Simulation Toolkit initially to help inform the design of the individual detector pixels and the 3.4-meter diameter detector wall to be fielded as part of neutron-diagnosed subcritical experiments. This toolkit is now being used to simulate and predict the performance of the final design of the individual detector pixels and the aggregate detector wall. Key detector characteristics evaluated from these simulations include impulse response, pulse height spectrum, number of photoelectrons per MeV, detector efficiency, and cross talk between detector pixels. Collectively, the results of these simulations inform how well the fission gamma ray die-off distribution from a neutron-diagnosed subcritical experiment measurement can be resolved. This is critical to determining the relative reactivity of the experiment. Furthermore, the Gamma Array Simulation Toolkit can be used to aid in the analysis of the experimental data from a neutron-diagnosed subcritical experiment measurement once the simulation has been benchmarked.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗