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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling

Microbial decomposition of organic matter (OM) in river corridors is a major driver of nutrient and energy cycles in natural ecosystems. Recent advances in omics technologies enabled high-throughput generation of molecular data that could be used to inform biogeochemical models. With ultrahigh-resolution OM data becoming more readily available, in particular, the substrate-explicit thermodynamic modeling (SXTM) has emerged as a promising approach due to its ability to predict OM degradation and respiration rates from chemical formulae of compounds. This model implicitly assumes that all detected organic compounds are bioavailable, and that aerobic respiration is driven solely by thermodynamics. Despite promising demonstrations in previous studies, these assumptions may not be universally valid because OM degradation is a complex process governed by multiple factors. To identify key drivers of OM respiration, we performed a comprehensive analysis of diverse river systems using Fourier-transform ion cyclotron resonance mass spectrometry OM data and associated respiration measurements collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium. In support of our argument, we found that the incorporation of all compounds detected in the samples into the SXTM resulted in a poor correlation between the predicted and measured respiration rates. The data-model consistency was significantly improved by the selective use of a small subset (i.e., only about 5%) of organic compounds identified using an optimization method. Through a subsequent comparative analysis of the subset of compounds (which we presume as bioavailable) against the full set of compounds, we identified three major traits that potentially determine OM bioavailability, including: (1) thermodynamic favorability of aerobic respiration, (2) the number of C atoms contained in compounds, and (2) carbon/nitrogen (C/N) ratio. We found that all three factors serve as “filters” in that the compounds with undesirable properties in any of these traits are strictly excluded from the bioavailable fraction. This work highlights the importance of accounting for the complex interplay among multiple key traits to increase the predictive power of biogeochemical and ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Best Practices for Smart Grid-Interactive Efficient Building Ready Performance Contracts

Grid-interactive efficient building (GEB) measures reduce costs and optimize energy use for additional grid services by coordinating building energy loads and providing continuous demand management. Incorporating GEB energy conservation measures (ECMs) in performance contracts is reliant upon multiple factors. These factors include site selection with utility tariffs and incentives favorable to GEB, the identification of GEB as a priority in the initial stages of the contracting process, integration of GEB within comprehensive performance contracts with multiple other ECMs, and careful consideration of GEB measurement and verification (M&V) for energy savings performance contracts (ESPCs) and performance assurance for utility energy service contracts (UESCs).

building energy loads↗
DOE OSTI2022

Estimating List-Mode Data Sensitivities to Nuclear Data with MCNP6

Nuclear data are a vital component of predictive simulations used in applications like experiment design, stockpile stewardship, nuclear nonproliferation/safeguards, health physics, and criticality safety. A singular simulation requires the coalescence of different areas of nuclear data such as cross sections, angular distributions, and energy distributions of emitted neutrons for different materials and energy ranges. Improving nuclear data and thus reducing the uncertainty in simulated parameters could enable smaller, better-informed safety factors and ultimately reduce operational and procedural costs. There is a constant effort to garner a better understanding of the physical quantities represented by nuclear data through experiments. Integral experiment benchmarks use simulated and measured results to validate current nuclear data values. In the past, benchmarks primarily focused on the effective multiplication factor (k eff ); however, this limited scope has caused compensating errors and areas of nuclear data that lack validation. Compensating errors are inaccuracies in nuclear data that are obfuscated by cancellation when observing integrated values such as k eff . Diverse integral benchmark experiments that look for quantities of interest other than k eff and include multiple responses minimize the possibility of compensating errors and provides validation to areas of nuclear data previously lacking experimental validation. Benchmark experiments can be optimized during the design process to be highly dependent on specific areas of nuclear data. The dependence of a response in an experiment to a specific area/type of nuclear data is defined as sensitivity. A larger sensitivity means that nuclear data uncertainties will play a larger role in the response(s) resulting in larger bias. Currently, the sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®1 ) transport code are limited to responses of k eff and tallied values (e.g., flux, surface current). As a part of the EUCLID project, this work explores estimating list-mode nuclear data sensitivities that can be used to design experiments aimed to constrain and reduce compensating errors in nuclear data by focusing on responses other than k eff . Tallied values are ideal quantities that are estimated with detectors during experiments. List-mode data (a list of neutron collection times) are the direct output of detector systems in subcritical neutron noise experiments. Expanding MCNP sensitivity capabilities to include the sensitivity of responses estimated from list-mode data, such as the prompt neutron decay constant (α) and multiplicity estimates (S and D), enables more direct comparison of simulated and measured experimental quantities. Additionally, deterministic tools such as SENSMG are capable of obtaining sensitivities to a wide variety of responses; however, these tools cannot handle complex geometries due to the assumptions made in discretizing the phase-space variables of the Boltzman transport equation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Quantum Key Distribution Applicability to Smart Grid Cybersecurity Systems

To meet the increasing demand for electricity and to have a more reliable and resilient electric grid against conventional and extreme events, grid modernization is more crucial now than ever before. This will require the development and deployment of devices that provide advanced communication capabilities. The overall efficiency, reliability, and resilience of the smart grid will be inextricably linked to the exchange of information between these devices. Unfortunately, the increased information flow will increase the potential attack surface and introduce new vulnerabilities. While a smarter grid will depend critically on information flow, these benefits will be accrued only if that information can be protected. Nowadays, information is secured in smart grids primarily through cryptography. However, with the increasing number of sophisticated attacks as well as the increasing computational power, the security of the “classical” cryptographic algorithms is threatened. Quantum information science offers solutions to this problem, specifically quantum key distribution (QKD), which provides a means for the generation and secure distribution of symmetric cryptographic keys. The security of QKD stems ultimately from the very nature of quantum physics. In this paper, we investigate the applicability of QKD to the various smart grid sectors and specific use cases. We have identified 18 smart grid use cases of interest for QKD suitability together with 7 QKD factors used for the assessment of the various use cases. For each use case, the impact to security of the loss of confidentiality, integrity, and/or availability is specified. In addition, the suitability of QKD is assessed for each use case with respect to multiple factors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Including Chi-Nu 235 U PFNS Experimental Data into an ENDF/B-VIII.1 Release Candidate Evaluation

This report documents an evaluation of 235 U prompt fission neutron spectra (PFNS) that is a release candidate for the upcoming U.S. nuclear data library, ENDF/B-VIII.1. This evaluation differs from its predecessor, ENDF/B-VIII.0, mainly by the inclusion of 235 U PFNS measured by the Chi-Nu team of LANL and LLNL. This data set is the first one that covers the 235 U PFNS for continuous incident-neutron energies of 1⁻20 MeV and outgoing-neutron energies from 10 keV⁻10 MeV with high precision. Previous data sets were either measured in a limited energy range or with less precision. Hence, these new Chi-Nu data provide decisive information for the evaluation. The resulting evaluated data correspond well to the new experimental PFNS. The evaluated PFNS also produce average mean energies and 239 Pu/ 235 U PFNS in agreement with associated Chi-Nu data. If one uses the new evaluated data to predict the neutron multiplication factor, k eff , of selected ICSBEP critical assemblies, the differences of simulated values compared to those using ENDF/B-VIII.0 is modest (less than 55 pcm). This difference in k eff can be easily accommodated by changes in the 235 U average prompt fission neutron multiplicity that is currently being re-evaluated. In addition to that, the new PFNS predict on average 235 U LLNL pulsed-sphere neutron-leakage spectra better than ENDF/B-VIIII.0 PFNS.

235U↗
DOE OSTI2023

Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios

Management of the nation’s power grid over the coming decades will need to factor multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behavior caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We the used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.

electic grid, reinforcement learning, climate adap↗
NASA NTRS2011

Increased Intracranial Pressure and Visual Impairment Associated with Long-Duration Spaceflight

Although humans have been flying in space since the 1960s, more recent missions have revealed a new suite of physiological adaptations and consequences of space flight. Notably, 60% of long-duration crewmembers (ISS/MIR) and >25% of short-duration (Shuttle) crewmembers have reported subjective degradation in vision (based on debrief comments) (Gibson 2011). Decreased near-visual acuity was demonstrated in 46% of ISS/Mir and 21% of Shuttle crewmembers, resulting in a shift of up to 1-2 diopters in their refractive correction. It is likely that the recently revealed ophthalmic changes have been present since the first days of human space flight, but have been overlooked or attributed to other causations. The reported changes in vision have occurred at various time points throughout missions, with ranging degrees of visual degradation. Although some cases resolved upon return to Earth, several astronauts have not regained preflight visual acuity, indicating that the damage may be permanent. While observing these changes over the years, without other overt symptomology and with the given age range of the flying population, this has largely been attributed to an expected hyperopic shift due to aging. However, the availability of onboard analysis techniques, including visual acuity assessments, retinal imagery, and ultrasounds of the eye and optic nerve tracts, along with more detailed post-flight techniques, has led to the recent recognition of a wider syndrome. Along with vision changes, findings include flattening of the globe, swelling of the optic disc (papilledema), choroidal folds in the retina, swelling of the optic nerve sheath, and visual field defects. It is widely hypothesized that this constellation of findings may be explained by an elevation of intracranial pressure (ICP). Out of the 60% of long-duration astronauts that have reported a subjective degradation in vision, a subset (currently 10 astronauts) have developed this syndrome. The National Aeronautics and Space Administration (NASA) has made it a high priority to understand this syndrome and provide mitigation techniques to protect crewmembers from visual impairment. While there are many possible factors that could contribute to intracranial hypertension associated with spaceflight, the relative contribution of these, as well as the processes by which eye damage occurs as a result of intracranial hypertension, are not fully understood. The observed pathophysiological phenomena are extremely complex and it is likely that multiple factors contribute to their incidence, rather than one simple mechanism. This paper will define and examine the findings in detail, and expound upon the potential contributing factors and their relative contribution to this syndrome.

Marshall-Bowman, Karina↗
DOE OSTI2023

The role of social support on midwestern farmers’ willingness to grow perennial bioenergy crops

The lack of farmers' willingness to grow perennial bioenergy crops (PBCs) presents a critical barrier to the emergence of cellulosic biofuel production. The willingness relies on a complex network of economic, environmental, and social drivers, among which the influence of social factors (e.g., the influence of neighborhood, community, and communication) is less understood. This study addresses this knowledge gap via a survey analysis of midwestern farmers. The survey data are analyzed through ordinary least square regression and structural equation model, which together investigate the individual and interactive impacts of multiple factors on farmers' decisions to adopt PBCs. Based on a farm-scale analysis, six statistically significant predictors of farmer willingness to grow PBCs are identified: perception of PBCs' environment benefits, education level, willingness to take risks, familiarity with PBCs, portion of peers already growing PBCs, and support of biorefineries locating in the local community. Among these, the latter three predictors are social support variables. It is found that familiarity with the crops is the most significant predictor of willingness; familiarity is also an important intermediate variable that mediates the influence of many other predictors. In addition, peer adoption can both directly and indirectly affect willingness via its influence on familiarity. Furthermore, these findings suggest that it is a pressing need to improve farmers’ knowledge of PBCs to promote the adoption of such crops.

09 BIOMASS FUELS↗
DOE OSTI2023

Data for The Role of Social Support on Midwestern Farmers’ Willingness to Grow Perennial Bioenergy Crops

The lack of farmers’ willingness to grow perennial bioenergy crops (PBCs) presents a critical barrier to the emergence of cellulosic biofuel production. The willingness relies on a complex network of economic, environmental, and social drivers, among which the influence of social factors (e.g., the influence of neighborhood, community, and communication) is less understood. This study addresses this knowledge gap via a survey analysis of midwestern farmers. The survey data are analyzed through ordinary least square regression and structural equation model, which together investigate the individual and interactive impacts of multiple factors on farmers’ decisions to adopt PBCs. Based on a farm-scale analysis, six statistically significant predictors of farmer willingness to grow PBCs are identified: perception of PBCs’ environment benefits, education level, willingness to take risks, familiarity with PBCs, portion of peers already growing PBCs, and support of biorefineries locating in the local community. Among these, the latter three predictors are social support variables. It is found that familiarity with the crops is the most significant predictor of willingness; familiarity is also an important intermediate variable that mediates the influence of many other predictors. In addition, peer adoption can both directly and indirectly affect willingness via its influence on familiarity. These findings suggest that it is a pressing need to improve farmers’ knowledge of PBCs to promote the adoption of such crops.

Economics↗
DOE OSTI2022

Equivalent Fundamental-Mode Source Simulations for Spherical Uranium and Plutonium Systems [Abstract]

The equivalent fundamental-mode source refers to a “source” which is identically distributed in space, energy, and angle as that of a fundamental-mode fission source distribution. It has previously been shown that the system fixed source multiplication ($M_{fs}$) relates to the effective multiplication factor ($\kappa_{eff}$) as $M_{fs} = \frac{g^*}{1-k_{eff}}$ The term $\mathcal{g}$* therefore relates the system source distribution to the system equivalent fundamental-mode source. As previously shown, the $\mathcal{g}$* term is approximately 1 for deeply subcritical systems and then diverges as the system multiplication increases. For a system with a point source located in the center of an assembly (such a sphere), $\mathcal{g}$* will be greater than 1; this happens because there is a higher probability of induced fission when all of the starter neutrons originate from the center of the sphere. For a uniform source, $\mathcal{g}$* is less than 1, but does not diverge from 1 as dramatically as seen by a point source. One reason that $\mathcal{g}$* is an important parameter is that it is used in measurement methods to infer reactor kinetics parameters ($β_{eff}$ being one example). This work will discuss multiple ways to simulate $\mathcal{g}$* using MCNP®6.2, which include use of traditional methods versus a single input file method that was recently published. A discussion related to methods to simulate $M_{fs}$ will be presented. Spherical systems of Highly Enriched Uranium (HEU) will be compared with previous works. In addition, results of plutonium including multiple reflector materials will be investigated. Simulated systems will include both parametric studies as well as configurations used in recent experiments performed at the National Criticality Experiments Research Center (NCERC). These studies will include subtleties associated with system geometry that have not been previously shown. Last, this work will investigate the relationship between $\mathcal{g}$* and reactor kinetics parameters (such as $β_{eff}$).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Neutronic design and fuel cycle analysis of a fluoride salt-cooled High Temperature Reactor (FHR)

The Fluoride salt-cooled High temperature Reactor (FHR) is a new Gen IV reactor concept that can operate under near atmospheric pressure circumstances and further enhance the inherent safety. In this study, an FHR core design with 165 megawatts of thermal output (MWth) is proposed. The reactor core employs tri-structural-isotropic (TRISO) particle fuel within prismatic graphite blocks as the basic fuel form, FLiBe (2 {sup 7}LiF-BeF{sub 2}) as the primary coolant, and a three-batch fuel cycle scheme. Sensitivity analyses on various parameters were performed to optimize the cycle length and neutronic parameters. The fuel cycle of this core design was evaluated in detail from 4 aspects: cycle length, power peaking factor (PPF), discharge burnup, and temperature coefficient. It was found that larger fuel channel pitch (FCP) would have a relatively harder neutron spectrum, and yield relatively longer cycle length, lower, better fuel temperature coefficient (FTC), and moderator temperature coefficient (MTC). In addition, burnable poison (BP), Er{sub 2}O{sub 3}, can effectively reduce PPF, hold down the multiplication factor, and more importantly it can improve the MTC. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A nodal transport method for coupled fast-thermal reactor analysis

We report that for efficient core analyses of coupled fast-thermal reactors, a new deterministic method has been developed based on the variational nodal transport method of VARIANT. A new multigroup cross section generation procedure was devised by combining Monte Carlo lattice calculations for thermal assemblies and the two-step procedure of the MC 2 -3 code for fast assemblies. To reduce assembly homogenization errors in nodal transport calculations, a new nodal equivalence method was developed based on the partial current discontinuity factor (PCDF) and incorporated in VARIANT. A practical procedure to calculate PCDFs with fixed source supercell calculations was also devised. The performance of the proposed method was investigated using a test problem derived from the versatile coupled test reactor (VCTR) design. The new procedure produced multigroup cross sections accurately for all assemblies. VARIANT transport calculations with PCDFs produced accurate multiplication factor and power distribution compared to reference Serpent-2 Monte Carlo solutions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Customer enrollment and participation in building demand management programs: A review of key factors

Increasing the efficiency and flexibility of electricity demand is necessary for ensuring a cost-effective and reliable transition to zero-carbon electricity systems. Such demand-side management (DSM) resources have been procured by utilities for decades via energy efficiency and demand response programs; however, the key drivers of program enrollment and customer participation levels remain poorly understood — even as governments and grid planners seek to scale up the deployment of DSM assets to meet climate targets. Here we systematically review the evidence on multiple factors that may influence customer enrollment and participation in building DSM programs, focusing primarily on residential and commercial buildings. We examine the contexts in which relationships between DSM factors and outcomes are most often explored and with which methods; we also score the strength, direction, and internal consistency of each factor's reported impact on the enrollment and participation outcomes. We find that studies most commonly assess the effects of economic incentives for load flexibility on program participation levels, often using simulation-based methods in lieu of measured data. Few studies focus on program enrollment outcomes or regulatory drivers of either enrollment or participation, and gaps are also evident in the coverage of emerging DSM opportunities like load electrification. Removal of structural barriers (e.g., the lack of controls infrastructure) and the use of third party services (e.g., load aggregators) are the factors with the largest positive impacts on DSM outcomes, but no single factor emerges as clearly most impactful. For a given factor, the range of reported impacts typically varies widely across the relevant studies reviewed. Our findings provide a snapshot of the state of knowledge about building DSM and customer decision-making, and they expose key gaps in understanding that must be filled if building DSM is to expand as a critical resource for operating clean power grids.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2018

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures.

runway reconfiguration↗
NASA NTRS1975

Influence of the atmosphere on remotely sensed data

Factors which influence the effects of the atmosphere on the data of remote sensing are examined. A radiative-transfer model is considered and effects of varied optical thickness of the atmosphere are investigated. Effects of varied surface albedo are discussed along with the effects of the sun angle, the effects of the scan angle, and questions regarding the atmospheric effects on the recognition performance. It is found that a multiplicative factor involving the sun angle alone is not sufficient for the correction of space data.

Turner, R. E.↗
NASA NTRSDate not supplied

Climate change reshapes the drivers of false spring risk across European trees

Temperate and boreal forests are shaped by late spring freezing events after budburst, which are also known as false springs. Research has generated conflicting results on whether or not these events will change with climate change, potentially because---to date---no study has compared the myriad climatic and geographic factors that contribute to a plant's risk of a false spring. We assessed and compared the strength of the effects of mean spring temperature, distance from the coast, elevation and the North Atlantic Oscillation (NAO) using PEP725 leafout data for six temperate, decidious tree species across 11,648 sites in Central Europe and how these predictors shifted with climate change. Across species before recent warming, mean spring temperature and distance from the coast were the strongest predictors, with higher mean spring temperatures associated with decreased risk in false springs (–7.64% per 2°C increase) and sites further from the coast experiencing an increased risk (5.32% per 150km from the coast). Elevation (2.23% per 200m increase in elevation) and NAO index (1.91% per 0.3 increase) also increased false spring risk. With climate change, elevation and distance from coast---i.e., the geographic factors---remain relatively stable, while climatic factors shifted in magnitude for mean spring temperature (down to -2.84% in risk per 2°C), and in direction, with positive NAO phases leading to lower risk (-9.15% per 0.3). The residual effects of climate change---unexplained by the climatic and geographic factors already included in the model---magnified the species-level variation in risk, with risk increasing among early-leafout species (i.e., Aesculus hippocastanum, Alnus glutinosa and Betula pendula) but a decline or no change in risk among late-leafout species (i.e., Fagus sylvatica, Fraxinus excelsior and Quercus robur). Our results show that climate change has reshaped the major drivers of false spring risk and highlight how considering multiple factors can yield a better understanding of the complexities of climate change.

Climate change↗
DOE OSTI2024

At-power subcritical multiplication in the Advanced Test Reactor during nuclear requalification testing

Power division information during nuclear requalification of the Advanced Test Reactor (ATR) is of considerable interest as an importance function for observed changes to core reactivity. The degree to which a given physical subdivision of a critical reactor acts as a neutron source for other lobes is not analytically characterized for general application. When ATR operates at power, individual power-producing lobes rely on each other as neutron sources in order to maintain constant power, which in general requires either exactly critical multiplication within a reactor or an external neutron source. Here, this work shows that fuel element and lobe powers in ATR can be related with subcritical multiplication theory. Subcritical multiplication factors are computed with a physically validated analytical method based on actual at-power operation, quantifying for each lobe its dependence on other lobes as an external neutron source. This explanation is significant for ATR due to the desire to irradiate a large variety of experiments simultaneously, each having its impact on the core neutron population. For any physical subdivision of any other critical reactor, it is likewise true that the subdivision undergoes only subcritical multiplication.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

Criticality Experiments to Reduce Compensating Errors in Plutonium Nuclear Data

Compensating errors between nuclear data observables in a library can adversely impact application simulations. The primary goal of the EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) is to reduce compensating errors in nuclear data. A new criticality experiment, described in this work, was designed with the specific target nuclear data of 239 Pu fission, inelastic scattering, elastic scattering, capture, nu-bar, and prompt fission neutron spectrum (PFNS). This work will focus on the design and execution of the EUCLID experiment, performed on the Planet vertical lift critical assembly machine at the National Criticality Experiments Research Center (NCERC). The criticality experiment includes two different configurations with very different geometries: one is cube-like to minimize neutron leakage while the other is slab-like to maximize leakage. Having these two widely varying configurations allows the scattering sensitivities of 239 Pu to the neutron multiplication factor to be greatly changed while minimally impacting the other cross section sensitivities. Both configurations utilize the Pu ZPPR (Zero Power Physics Reactor) plates as fuel. The experiments were designed using a D-Optimality criteria, which is an optimization method minimizing the log-determinant of the adjusted nuclear data covariance for the target reactions. These experiments include not only inference of k eff , as done in all critical benchmark experiments, but several other responses as well, such as neutron multiplication measurements and reaction rate ratios. After analysis of the measured data is complete, adjustment of nuclear data will be performed to assess whether the new experimental data successfully reduced compensating errors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗