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DOE OSTI2024

How to Safely Build 100-plus Kilograms of Weapons-Grade Plutonium

The goal of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project was to reduce compensating errors by utilizing machine learning to both help determine which reactions contain compensating errors as well as optimizing an experiment which can be used to maximally reduce these errors. Compensating errors can adversely impact the predictive power of application simulations, and therefore it’s useful to further constrain nuclear data and reduce these errors. The EUCLID project included building two configurations at the National Criticality Experiments Research Center (NCERC). These two configurations had very different geometries (one was cube-like and one was slab-like). Previous works focus on selection of the target experiment(s), radiation transport capabilities developed in the project, the experiment optimization, and the performance of the experiments. This work will focus only on the safety aspects of performing this experiment, which utilized over 100 kg of weapons-grade plutonium.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

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↗

NASA NTRS1959

Review of Aircraft Altitude Errors Due to Static-Pressure Source and Description of Nose-Boom Installations for Aerodynamic Compensation of Error

A brief review of airplane altitude errors due to typical pressure installations at the fuselage nose, the wing tip, and the vertical fins is presented. A static-pressure tube designed to compensate for the position errors of fuselage-nose installations in the subsonic speed range is described. This type of tube has an ogival nose shape with the static-pressure orifices located in the low-pressure region near the tip. The results of wind-tunnel tests of these compensated tubes at two distances ahead of a model of an aircraft showed the position errors to be compensated to within 1/2 percent of the static pressure through a Mach number range up to about 1.0. This accuracy of sensing free-stream static pressure was extended up to a Mach number of about 1.15 by use of an orifice arrangement for producing approximate free-stream pressures at supersonic speeds and induced pressures for compensation of error at subsonic speeds.

Gracey, William↗

NASA NTRS1991

Compensating sampling errors in stabilizing helmet-mounted displays using auxiliary acceleration measurements

A method based on complementary filtering is shown to be effective in compensating for the image stabilization error due to sampling delays of HMD position and orientation measurements. These delays would otherwise have prevented the stabilization of the image in HMDs. The method is also shown to improve the resolution of the head orientation measurement, particularly at low frequencies, thus providing smoother head control commands, which are essential for precise head pointing and teleoperation.

Merhav, S.↗

NASA NTRS1992

Antenna analysis using neural networks

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern shaping. The interesting thing about D-C synthesis is that the side lobes have the same amplitude. Five-element arrays were used. Again, 41 pattern samples were used for the input. Nine actual D-C patterns ranging from -10 dB to -30 dB side lobe levels were used to train the network. A comparison between simulated and actual D-C techniques for a pattern with -22 dB side lobe level is shown. The goal for this research was to evaluate the performance of neural network computing with antennas. Future applications will employ the backpropagation training algorithm to drastically reduce the computational complexity involved in performing EM compensation for surface errors in large space reflector antennas.

Smith, William T.↗

NASA NTRS2010

System and method for correcting attitude estimation

A system includes an angular rate sensor disposed in a vehicle for providing angular rates of the vehicle, and an instrument disposed in the vehicle for providing line-of-sight control with respect to a line-of-sight reference. The instrument includes an integrator which is configured to integrate the angular rates of the vehicle to form non-compensated attitudes. Also included is a compensator coupled across the integrator, in a feed-forward loop, for receiving the angular rates of the vehicle and outputting compensated angular rates of the vehicle. A summer combines the non-compensated attitudes and the compensated angular rates of the to vehicle to form estimated vehicle attitudes for controlling the instrument with respect to the line-of-sight reference. The compensator is configured to provide error compensation to the instrument free-of any feedback loop that uses an error signal. The compensator may include a transfer function providing a fixed gain to the received angular rates of the vehicle. The compensator may, alternatively, include a is transfer function providing a variable gain as a function of frequency to operate on the received angular rates of the vehicle.

Josselson, Robert H.↗

DOE OSTI2023

The EUCLID Experiment and Nuclear Data Library Comparisons

The EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project at Los Alamos National Laboratory (LANL) was a Laboratory Directed Research and Development project which aimed to reduce compensating errors in nuclear data. One major component of the project was a series of critical experiments, both measuring k eff and various other observables with the goal of using these experiments to constrain 239 Pu nuclear data uncertainties and identify compensating errors. This paper details some of the experiment design and how various nuclear data libraries simulate the EUCLID system, including sensitivities compared to Jezebel.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Fast neutron leakage spectra of the EUCLID experiment

Special nuclear material in sub-critical and critical configurations measured in integral experiments are important for validation and adjustment of nuclear data. Many different evaluations of nuclear data exist, and these different evaluations can provide different values for individual cross sections that vary due to the uncertainties in differential experiments or lack of such data. For integral experiments, differences in these individual cross sections can have compensating errors, which lead to the same answer. One example of this is the Jezebel critical assembly, where k eff of the system is correctly computed by both ENDF/B-VIII.0 and JEFF-3.3, despite having substantially different underlying evaluated values for specific reactions (such as elastic and inelastic cross sections). To reduce compensating errors in nuclear data, the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project has utilized machine learning to design a set of sub-critical and critical experiments. These experiments include slab- and cube-like configurations of 239 Pu in the form of the ZPPR plates. Six different responses were measured on a total of thirteen different configurations. One of these responses, the neutron leakage spectrum, was measured using an EJ301D detector. Finally, the results of the neutron leakage spectra show good agreement (within 1–2 σ ) with the expected spectrum from simulations and will be used in the subsequent nuclear data adjustment done by the EUCLID team.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Investigating Fission Reaction Rate Ratio Sensitivities [Slides]

One EUCLID project goal is to create a NEW capability in the MCNP6 code. This is being tested on reaction rate sensitivities and compared with SENSMG results. Reaction rate ratio sensitivities were added to EUCLID sensitivity library and investigated to determine value toward designing optimized experiments aimed at reducing compensating errors in nuclear data. The use of adjustment using multiple responses (and underlying/required capabilities) may benefit many applications. These include a tool for adjustment using multiple integral responses (ND adjustment and validation community); sensitivities of additional responses (ND adjustment and validation community); sensitivity capabilities (users in many application areas including safeguards/nonproliferation, criticality safety, etc.); and experiment optimization capability (applications which can benefit from integral experiments). Team is working to build and test future tools and to understand compensating errors

235U↗

DOE OSTI2023

Neutron Leakage Spectra of the EUCLID Experiment [Abstract]

Integral experiments with sub-critical and critical configurations of special nuclear material are performed in support of nuclear data validation and adjustment. Different nuclear data evaluations may have different values for individual cross sections due to uncertainties in (or lack of) differential experiments, but compensating errors in these data sets can lead to the same k eff results for one application while vastly different for another application. One example of this is 239 Pu, where both ENDF/B-VIII.0 and JEFF-3.3 correctly compute k eff of the Jezebel critical assembly, but the individual contributions from each reaction are vastly different. To help resolve this specific case, the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project used machine learning to design a set of experiments to help resolve the compensating errors in 239 Pu. A total of six responses were measured during the experimental campaign, which constrain the data in ways that k eff alone cannot and will be used for adjustment of the nuclear data. One of these responses is the neutron leakage spectrum, which recent work has shown to be useful for constraining the prompt fission neutron spectrum and inelastic scattering. The neutron leakage spectra were measured utilizing a 3 in. right cylinder EJ-301D detector. The measured signal in the detector was deconvoluted using spectrum unfolding techniques, which are presented and compared to simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Analysis of Integrated Vapor Transport Biases

Abstract High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of Earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line analysis correction-based additive inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7- and 14-day IVT bias by 16%–17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the South Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveals areas with compensating errors.

Reynolds, Carolyn A.↗

NASA NTRS1978

Precision attitude determination for multimission spacecraft

Attitude determination algorithms for a multimission spacecraft are derived and their performance analyzed. The attitude determination system is composed of a strapdown Inertial Reference Unit (IRU), two fixed head star trackers, and an onboard computer. IRU data is processed to maintain real-time knowledge of spacecraft attitude relative to an inertial reference frame. Star tracker data is processed using Kalman filtering techniques to estimate and correct the attitude determination errors and the gyro drift compensation errors. The results of a star availability analysis for stellar, solar and earth pointing missions are presented. Linear covariance analysis techniques are used to evaluate nominal attitude determination performance, the effects of sensor measurement accuracy variations, the effects of errors in knowledge of sensor measurement accuracy, and the effects of star tracker misalignment errors. Results of a nonlinear simulation analysis of attitude determination performance are also presented. These analyses show that precision attitude determination for stellar, solar and earth pointing missions is achieved.

Murrell, J. W.↗

NASA NTRS1971

Dynamic errors in strapdown inertial navigation systems

Motion-induced errors in strapdown inertial navigation systems are discussed. The errors generated in single-degree-of-freedom gyros and accelerometers are treated in great detail. These sensor errors are related to errors at the system level and common pulse rebalance techniques are compared. Since instrument single transmission characteristics are found to be important, describing function analysis is applied to nonlinear pulse torqued inertial sensor loops, and the results are compared with simulations. Two means for reducing motion-induced errors are explored: (1) selection of inertial sensor parameters, and (2) active error compensation by measuring the environment, computing the errors generated, and correcting for them.

Source record↗

DOE OSTI2022

Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1

Abstract. Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2007

The EPEC Algorithm for Vision Guided Manipulation: Analysis and Validation

This paper describes the simulated performance and experimental validation of a computationally efficient algorithm for improving positioning accuracy of robot arms using low speed feedback from fixed stereo cameras. The algorithm, called End-Effector Position Error Compensation (EPEC) is robust to visual occlusion of the end-effector and does not require high fidelity calibration of either the arm or stereo camera. The algorithm works by calculating an error vector between the locations of a fiducial on the arm's end-effector as predicted by arm kinematics and detected by a stereo camera triangulation. With this knowledge, the commanded target pose is adjusted to compensate for positioning errors. A simulation environment where arbitrary error can be introduced into arm-camera systems is introduced and used to provide an assessment of the performance of the algorithm under both ideal and degraded conditions.

manipulations↗

NASA NTRS1994

Operational support for Upper Atmosphere Research Satellite (UARS) attitude sensors

The Upper Atmosphere Research Satellite (UARS) has several sensors that can provide observations for attitude determination: star trackers, Sun sensors (gimbaled as well as fixed), magnetometers, Earth sensors, and gyroscopes. The accuracy of these observations is important for mission success. Analysts on the Flight Dynamics Facility (FDF) UARS Attitude task monitor these data to evaluate the performance of the sensors taking corrective action when appropriate. Monitoring activities range from examining the data during real-time passes to constructing long-term trend plots. Increasing residuals (differences) between the observed and expected quantities is a prime indicator of sensor problems. Residual increases may be due to alignment shifts and/or degradation in sensor output. Residuals from star tracker data revealed and anomalous behavior that contributes to attitude errors. Compensating for this behavior has significantly reduced the attitude errors. This paper discusses the methods used by the FDF UARS attitude task for maintenance of the attitude sensors, including short- and long-term monitoring, trend analysis, and calibration methods, and presents the results obtained through corrective action.

Lee, M.↗

DOE OSTI2025

Global models predict clouds at the wrong time of day: Does it matter for radiation and climate?

Accurate prediction of future climate change hinges upon the ability of Earth system models (ESMs) to simulate clouds and their radiative effects. Even if an ESM can simulate the correct clouds, a systematic error in the amount of sunlight reflected by clouds (and, thus, cloud radiative effect) can exist if the clouds are simulated at the incorrect time of day. In this work, we develop an analytical model connecting diurnal cloud biases to emergent mean state radiative biases. With the use of satellite observations, we demonstrate that there are errors in the time of day that clouds are occurring in ESMs that would cause bias in shortwave cloud radiative effect (SWCRE) that is greater than 45% of the total SWCRE bias, but such errors in the cloud diurnal cycle are masked by other compensating errors, indicating that these ESMs are getting the right answer for the wrong reasons.

Geosciences↗

NASA NTRS2012

Stitching of near-nulled subaperture measurements

A metrology system for measuring aspheric test objects by subaperture stitching. A wavefront-measuring gauge having a limited capture range of wavefront shapes collects partially overlapping subaperture measurements over the test object. A variable optical aberrator reshapes the measurement wavefront with between a limited number of the measurements to maintain the measurement wavefront within the capture range of the wavefront-measuring gauge. Various error compensators are incorporated into a stitching operation to manage residual errors associated with the use of the variable optical aberrator.

Murphy, Paul↗