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The NDP measures contamination, plasma characteristics, electrical fields, and magnetic fields. This paper describes the NDP requirements, development process, and flight systems functionality.
NdP is Halite, Rock Salt structured and crystallizes in the cubic Fm-3m space group. The structure is three-dimensional. Nd3+ is bonded to six equivalent P3- atoms to form a mixture of edge and corner-sharing NdP6 octahedra. The corner-sharing octahedral tilt angles are 0°. All Nd–P bond lengths are 2.95 Å. P3- is bonded to six equivalent Nd3+ atoms to form a mixture of edge and corner-sharing PNd6 octahedra. The corner-sharing octahedral tilt angles are 0°.
The increased levels of carbon dioxide (CO 2 ) emissions due to the combustion of fossil fuels and the consequential impact on global climate change have made CO 2 capture, storage, and utilization a significant area of focus for current research. In most electrochemical CO 2 applications, water is used as a proton donor due to its high availability and mobility and use as a polar solvent. Additionally, supercritical CO 2 is a promising avenue for electrochemical applications due to its unique chemical and physical properties. Consequently, understanding the interactions between water and supercritical CO 2 is of great importance for future electrochemical applications. Molecular dynamics (MD) simulation is a powerful tool that enables atomistic-resolution dynamics of molecular systems, which can complement and guide future experimental investigations. This study employed atomistic MD to study the cosolubilities, codiffusivities, and structure of supercritical CO 2 and water systems, with a polarizable water model (SWM4-NDP) and a nonpolarizable CO 2 model (TraPPE). Additionally, ab initio MD simulations were used to better understand how atomistic polarizable/nonpolarizable models compare to explicit modeling of electron densities. The polarizable water model exhibited substantial improvement in water-associated properties. In conclusion, we anticipate the development of a compatible polarizable CO 2 model to yield similar improvement, providing a pathway for realizing novel high-pressure electrochemical systems.
This documents the National Diagnostic Plan as of September 2020. The major changes in this version compared to the NDP document issued in 2019 are the new schedules and the text for the national transformative diagnostics - section III, and a new transformational diagnostic is being added hCMOS. The many local diagnostics for our three Inertial Confinement Fusion (ICF) facilities; NIF, Z and OMEGA are updated and captured in section V.
This report documents the National Diagnostic Plan as of September 2021. The major changes in this version compared to the NDP document issued in 2020 are the new schedules and the text for the national transformative diagnostics–section III. The many local diagnostics for our three Inertial Confinement Fusion (ICF) facilities, NIF, Z, and OMEGA, are updated and captured in section V.
This documents the National Diagnostic Plan as of September 2022. The major changes in this version compared to the NDP document issued in 2021 are the new schedules and the text for the national transformative diagnostics - section III. The many local diagnostics for our three Inertial Confinement Fusion (ICF) facilities; NIF, Z and OMEGA are updated and captured in section V.
This documents the National Diagnostic Plan as of September 2023. The major changes in this version compared to the NDP document issued in 2021 are the new schedules and the text for the national transformative diagnostics - section III. The many local diagnostics for our three Inertial Confinement Fusion (ICF) facilities; NIF, Z and OMEGA are updated and captured in section V.
This documents the National Diagnostic Plan as of September 2025. The major changes in this version compared to the NDP document issued in 2024 are the new schedules and the text for the national transformative diagnostics - section III. The many local diagnostics for our three Inertial Confinement Fusion (ICF) facilities; NIF, Z and OMEGA are also updated and captured in section V.
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Interfaces are the key to next-generation high-energy batteries including solid-state Li metal batteries. In solid-state batteries, the buried nature of solid–solid electrolyte–electrode interfaces makes studying them difficult. Neutrons have significant potential to non-destructively probe these buried solid–solid interfaces. This work presents a comparative study using both neutron depth profiling (NDP) and neutron reflectometry (NR) to study a model lithium metal–lithium phosphorus oxynitride (LiPON) solid electrolyte system. In the NDP data, no distinct interphase is observed at the interface. NR shows a difference between electrodeposited, and vapor deposited LiPON–Li interfaces but finds both are gradient interphases that are less than 30 nm thick. Additional simulations of the LiPON–Li 2 O–Li system demonstrate that NDP has an excellent resolution in the 50 nm–1 μm regime while NR has an ideal resolution from 0.1–200 nm with different sample requirements. Together NDP and NR can provide a complementary understanding of interfaces between Li metal and solid electrolytes across relevant length scales.
Zames (1981) has observed that there is, in general, no 'separation principle' to guarantee optimality of a division between control law design and filtering of plant uncertainty. Peczkowski and Sain (1978) have solved a model matching problem using transfer functions. Taking into consideration this investigation, Peczkowski et al. (1979) proposed the Total Synthesis Problem (TSP), wherein both the command/output-response and command/control-response are to be synthesized, subject to the plant constraint. The TSP concept can be subdivided into a Nominal Design Problem (NDP), which is not dependent upon specific controller structures, and a Feedback Synthesis Problem (FSP), which is. Gejji (1980) found that NDP was characterized in terms of the plant structural matrices and a single, 'good' transfer function matrix. Sain et al. (1981) have extended this NDP work. The present investigation is concerned with a study of FSP for the unity feedback case. NDP, together with feedback synthesis, is understood as a Total Synthesis Problem.
The Nominal Design Problem (NDP) is extended to nonlinear cases, and a new case study of robust feedback synthesis for gas turbine control design is presented. The discussion of NDP extends and builds on earlier Total Synthesis Problem theory and ideas. Some mathematical preliminaries are given in which a bijection from a set S onto a set T is considered, with T admitting the structure of an F-vector space. NDP is then discussed for a nonlinear plant, and nonlinear nominal design is defined and characterized. The design of local controllers for a turbojet and the scheduling of these controls into a global control are addressed.
The field of nuclear data lost a pioneer with the passing of Murray Martin on March 9, 2022. Born in Regina, Canada in 1935, Murray Martin received his B.A. and M.A. in experimental physics from the University of Saskatchewan and his PhD in theoretical nuclear physics in 1962 from McMaster Univ. His thesis was entitled “A Study of the Low-Lying Excited Levels in the Even Lead Isotopes”. Additionally, he joined the NSF Data Project in 1962 in Washington, DC, and then followed that project as it moved to ORNL in 1964 as the Nuclear Data Project (NDP). Murray stayed with the NDP until his retirement in 1997 but resumed his work on a part-time basis in 2004 as a subcontractor to ORNL, which he continued until his last days.
Most of the energy produced globally comes by way of a heat engine. The Carnot principle places a limit as to how thermodynamically efficient a heat engine can be. There is no heat engine that can be 100% thermodynamically efficient and as such a substantial proportion of all heat supplied to a heat engine is lost as waste heat. Waste heat therefore is a large energy source ready to be properly utilized. Herein, a novel approach for converting waste heat to electricity is discussed. It involves the use of the liquid to vapor phase change of a material dielectric (water) or electrolyte (nanofluid) in the embodiment of a capacitor for direct thermal to electrostatic energy conversion. While this method of waste heat recovery could potentially be added to the ever expanding portfolio of energy conversion techniques, a number of aspects must be addressed before it can be brought into practice. Water was seen as an ideal dielectric phase change material given its high relative permittivity ratio when in the liquid form as compared to its vapor form. However, given its short voltage holdoff time the phase change of water would need to occur rapidly. This brings up concerns of explosive boiling. Herein, molecular dynamics analysis into the explosive boiling behavior of thin water films gave more insight into how the interaction between the surface and liquid affected explosive boiling onset time. A Lennard-Jones potential with one interaction site and a Morse potential with three interaction sites between water and solid substrate were used. It was found generally that a stronger interaction between water film and substrate led to faster explosive boiling onset times but an increase in the number of interaction sites delayed explosive boiling, even at the same wettability (contact angle). Understanding changes in the density and enthalpy of vaporization of a liquid dielectric such as water in the presence of an electric field is of importance due to the electrostatic nature of the waste heat conversion method under consideration. Specifically, if both density and enthalpy of vaporization are increased, the thermodynamic efficiency of the waste heat conversion method under consideration is decreased. Electric field effects are explored herein via molecular dynamics using two water models, the TIP4P-Ew and SWM4-NDP. The SWM4-NDP model is polarizable while the TIP4P-Ew model is not, which allows for a determination of the importance of model polarizability (i.e. variation in water model dipole moment) on these two properties of water when subjected to an electric field. Herein it was found that both water models respond similarly in terms of density and vaporization enthalpy variance upon the introduction of an electric field. Comparison was also made to the pressure induced by the electric field (electrostriction pressure) by way of a density comparison and it was found that the predicted electrostriction pressure overestimates the pressure experienced by water. Water by itself has a high enthalpy of vaporization, which limits the efficiency of the newly proposed conversion method. Research both experimental and through simulation has shown that the vaporization enthalpy of nanofluids can be engineered via nanoparticle size and material selection. An avenue less explored is manipulating the enthalpy of vaporization by altering the interaction strength between the nanoparticles and the base fluid. In practice this could be achieved through the addition of coatings to the nanoparticles to alter their wettability to the base fluid. This was explored by using a Lennard-Jones potential and Morse potential to model the interaction between base fluid (water) and the nanoparticle. For nanoparticles 2nm in diameter and at weight percentages up to 6%, the change in vaporization enthalpy due to alterations of the interaction strength between nanoparticle and base fluid was not significant (less than a 1% difference) when compared to the effect of altering the weight percentage of nanoparticles in the nanofluid or introducing an electric field. However, the effect of wettability may still become important at other nanoparticle concentrations and sizes. In all, the studies presented here further the understanding of phase change and thermodynamic properties of water and water based nanofluids under an electrostatic field which will help inform the development of a novel approach to waste heat conversion. The reduction of waste heat will improve energy sustainability outlooks.
In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.
The performance of solid-state electrochemical systems is intimately tied to the potential and lithium distributions across electrolyte–electrode junctions that give rise to interface impedance. Here, we combine two operando methods, Kelvin probe force microscopy (KPFM) and neutron depth profiling (NDP), to identify the rate-limiting interface in operating Si-LiPON-LiCoO 2 solid-state batteries by mapping the contact potential difference (CPD) and the corresponding Li distributions. The contributions from ions, electrons, and interfaces are deconvolved by correlating the CPD profiles with Li-concentration profiles and by comparisons with first-principles-informed modeling. Furthermore, we find that the largest potential drop and variation in the Li concentration occur at the anode–electrolyte interface, with a smaller drop at the cathode–electrolyte interface and a shallow gradient within the bulk electrolyte. Correlating these results with electrochemical impedance spectroscopy following battery cycling at low and high rates confirms a long-standing conjecture linking large potential drops with a rate-limiting interfacial process.
The Neutrino Discovery Platform (NDP) aims to accelerate DUNE-era science by making the neutrino program's existing datasets analyzable through fast, reproducible, and auditable workflows. We report a working version of two of its layers, data curation and agentic orchestration, built and tested end to end on MINERvA open data. The guiding lesson throughout is that a cross section is a measurement, and not just a plotted shape, only if it carries a defensible systematic-uncertainty budget, a trustworthy unfolding, and a reproducible record. Using a single medium-energy playlist pair from the MINERvA open-data release (about $2.05\times10^{17}$ protons on target of data), we first reproduced the shapes of two published charged-current inclusive $\nu_\mu$ measurements through a complete extraction ladder: selection, background subtraction, D'Agostini unfolding, efficiency correction, and flux normalization. These shape-level reproductions ran and tracked the published results, but they lacked the systematic-uncertainty machinery that defines a MINERvA cross section. To supply it, we vendored and built the MINERvA Analysis Toolkit and developed a many-universe systematic-uncertainty tool that produces a portable covariance artifact, a parallel event-loop runner, and a per-run auditability harness. Validated against a published covariance release, the toolchain reproduces the released statistical, flux, and muon-energy-scale terms and shows that they account for roughly 63\% of the total variance, with the remainder unreleased. Using this same infrastructure, we then performed a measurement of our own design, the hadronic recoil-energy distribution of low-energy ($E_\nu<2.5$~GeV) charged-current inclusive events, and found data/simulation shape agreement of $\chi^2/\mathrm{ndf}=1.26$. Together these results show that the platform supports original physics and not only reproductions.
The GRAVNET system is based on a medium frequency (MF) radar (2.22 MGz) with one site consisting of a transmitting and spaced receiving antenna; and two remote receiving sites (approximately 40 km distance, forming an approximately equalateral triangle), also spaced receiving antennas. The winds of the upper middle atmosphere (60 to 110 km) are measured at each of the three sites in real time, using the Spaced Antenna method. Up to 12 profiles per hour were obtained. Spectral analysis was performed separately on northward and eastward velocity components for each site at the same height. Cross spectra are then found between sites. Data selection criteria included the Normalized Phase Discrepancy (NDP).