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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Smooth, homogeneous, high-purity Nb 3 Sn superconducting RF resonant cavity by seed-free electrochemical synthesis

Abstract Workbench-size particle accelerators, enabled by Nb 3 Sn-based superconducting radio-frequency (SRF) cavities, hold the potential of driving scientific discovery by offering a widely accessible and affordable source of high-energy electrons and x-rays. Thin-film Nb 3 Sn RF superconductors with high quality factors, high operation temperatures, and high-field potentials are critical for these devices. However, surface roughness, non-stoichiometry, and impurities in Nb 3 Sn deposited by conventional Sn-vapor diffusion prevent them from reaching their theoretical capabilities. Here we demonstrate a seed-free electrochemical synthesis that pushes the limit of chemical and physical properties in Nb 3 Sn. Utilization of electrochemical Sn pre-deposits reduces the roughness of converted Nb 3 Sn by five times compared to typical vapor-diffused Nb 3 Sn. Quantitative mappings using chemical and atomic probes confirm improved stoichiometry and minimized impurity concentrations in electrochemically synthesized Nb 3 Sn. We have successfully applied this Nb 3 Sn to the large-scale 1.3 GHz SRF cavity and demonstrated ultra-low BCS surface resistances at multiple operation temperatures, notably lower than vapor-diffused cavities. Our smooth, homogeneous, high-purity Nb 3 Sn provides the route toward high efficiency and high fields for SRF applications under helium-free cryogenic operations.

Physics↗

DOE OSTI2021

Shock compression of silver to 300 GPa: Wave profile measurements and melting transition

Recent in-situ x-ray diffraction (XRD) measurements on laser-shock compressed Ag foils demonstrated a face-centered-cubic to body-centered-cubic transformation at ~150 GPa and melting between 172 and 197 GPa [Phys. Rev. Lett. 124, 235701 (2020)]. As a complement to the XRD work, we conducted plate impact experiments to obtain shock velocity and wave profile measurements on thicker Ag samples shock-compressed to peak stresses between 30 and 300 GPa. The shock velocity–particle velocity results were fitted very well by a linear relation over the entire stress range, providing an accurate determination of the Ag Hugoniot (locus of the stress-volume states achieved under shock compression). For peak stresses below 187 GPa and above 210 GPa—corresponding to the solid and liquid phases, respectively—the wave profiles show clean single waves. No wave profile features related to the fcc-bcc transformation at ~150 GPa were observed, implying minimal volume change for the transformation. For stresses between 187 and 210 GPa, an initial jump was followed by a time-dependent increase in the particle velocity (20–80 ns risetime) to the peak state—corresponding to the solid-liquid mixed phase response. Unlike the solid and liquid response, the mixed-phase response cannot be readily analyzed analytically. Instead, numerical simulations incorporating an accurate multiphase equation of state for Ag—not currently available—are required to analyze the wave profiles measured at 187–210 GPa stresses. Furthermore, the present work shows the potential for using wave profile measurements to examine the melting transition under shock compression.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Transition to Online Cable Insulation Condition Monitoring

Nuclear power plant cables were originally qualified for 40 year life and generally have not required specific test verification to assure service availability through the initial plant qualification period. However, license renewals to 60 and 80 years of operation require a cable aging management program that depends on some form of test and verification to assure fitness for service. Environmental stress (temperature, radiation, chemicals, water, and mechanical) varies dramatically within a nuclear power plant and, in some cases, cables have degraded and required repair or replacement before their qualified end-of-life period. In other cases, cable conditions have been mild and dependable cable performance confirmed to extend well beyond the initial qualified life. Most offline performance-based testing requires cables to be de-coupled and de-energized for specially trained technicians to perform testing. These offline tests constitute an expensive operational burden that limits the economic viability of nuclear power plants. Although initial investment may be higher, new online test practices are emerging as options or complements to offline testing that avoid or minimize the regularly scheduled offline test burden. These online methods include electrical and fiber-optic partial discharge measurement, spread spectrum time or frequency domain reflectometry, distributed temperature profile measurements, and local interdigital capacitance measurement of insulation characteristics. Introduction of these methods must be supported by research to confirm efficacy plus either publicly financed or market driven investment to support the start-up expense of cost-effective instrumentation to monitor cable condition and assure reliable operation. This work summarizes various online cable assessment technologies plus introduces a new cable motor test bed to assess some of these technologies in a controlled test environment.

Glass, Samuel W.↗

DOE OSTI2025

QEpsilon v0.1.0

QEpsilon is a Python package designed to minimize the effort required to build a data-driven quantum master equation of an open quantum system and to perform time evolution of the master equation. Applications of QEpsilon span from quantum computing to condensed matter systems.

Xie, Pinchen [Lawrence Berkeley National Laborator↗

DOE OSTI2021

Label-free DNA biosensing by topological light confinement

Large-area and transparent all-dielectric metasurfaces sustaining photonic bound states in the continuum (BICs) provide a set of fundamental advantages for ultrasensitive biosensing. BICs bridge the gap of large effective mode volume with large experimental quality factor. Relying on the transduction mechanism of reactive sensing principle, herein, we first numerically study the potential of subwavelength confinement driven by topological decoupling from free space radiation for BIC-based biosensing. Then, we experimentally combine this capability with minimal and low-cost optical setup, applying the devised quasi-BIC resonator for PNA/DNA selective biosensing with real-time monitoring of the binding event. A sensitivity of 20 molecules per micron squared is achieved, i.e. ≃0.01 pg. Further enhancement can easily be envisaged, pointing out the possibility of single-molecule regime. This work aims at a precise and ultrasensitive approach for developing low-cost point-of-care tools suitable for routine disease prescreening analyses in laboratory, also adaptable to industrial production control.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Adaptive variational quantum minimally entangled typical thermal states for finite temperature simulations

Scalable quantum algorithms for the simulation of quantum many-body systems in thermal equilibrium are important for predicting properties of quantum matter at finite temperatures. Here we describe and benchmark a quantum computing version of the minimally entangled typical thermal states (METTS) algorithm for which we adopt an adaptive variational approach to perform the required quantum imaginary time evolution. The algorithm, which we name AVQMETTS, dynamically generates compact and problem-specific quantum circuits, which are suitable for noisy intermediate-scale quantum (NISQ) hardware. We benchmark AVQMETTS on statevector simulators and perform thermal energy calculations of integrable and nonintegrable quantum spin models in one and two dimensions and demonstrate an approximately linear system-size scaling of the circuit complexity. We further map out the finite-temperature phase transition line of the two-dimensional transverse field Ising model. Finally, we study the impact of noise on AVQMETTS calculations using a phenomenological noise model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Interactive Mesh Generation and Comparison Tools for Nuclear Energy Advanced Modeling & Simulation Phase I. Final Report

The promise of computer simulations for nuclear reactor designs is the opportunity to speed up the design process while also improving safety, providing more detail and higher fidelity, reducing cost, minimizing schedule risk, and avoiding many other potential problems; because simulations have the potential to be much less expensive and time-consuming than traditional development – where scale models and physical tests require construction and long lead times – it is possible to consider more alternatives and complete more comprehensive assessments before the design is finalized and constructed. As more design scenarios are studied by simulation, the speed that simulations promise is only available as long as bottlenecks are addressed for these scenarios. A frequent bottleneck is the amount of human labor required to describe the shape of the objects being designed and decompose the shape into simple elements, such as hexahedra, that are small enough to capture the physical phenomena of interest without being so small that even fast computers cannot perform the simulation in a reasonable amount of time. Recently, techniques for creating all-hexahedral decompositions have been developed, but they require some human input. Many simulation codes prefer all-hexahedral decompositions, so we proposed to evaluate the feasibility of these techniques on nuclear reactor geometries to see whether they required too much human input to be commercially viable. Our study concluded that they can be made viable with some additional software tools to reduce the amount of user input required.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Understanding the temperature and humidity dependence of the SiPM characteristics

A miniature version of the ICAL experiment at the India-based Neutrino Observatory, the mini-ICALis in operation at the IICHEP, Madurai. The commissioning work of a Cosmic Muon Veto detector (CMVD)on top of the mini-ICAL is continued using extruded plastic scintillators with embeddedWLS fibers and the SiPM as a photo-transducer. The CMVD is being built to study thefeasibility of a cosmic muon veto for a shallow-depth neutrino experiment. The SiPM is calibratedusing an ultrafast LED driver andthe same calibration techniques will be used during the data taking period with a simplified LED in thesame readout PCB. An experimental setup wasdesigned to characterise the SiPMs in a temperature controlled environment. The readout electronicsinvolves trans-impedance amplifiers and a voltage followers of combined gain 1.24 mV/$\mu$A and adigital storage oscilloscope for the data collection with a minimal distortion of SiPM signal.Various characteristics of the Hamamatsu SiPM (S13360-2050VE), e.g. signal shape, optically correlatedand uncorrelated noise, recovery time etc were studied as a function of $V_{ov}$, number of photoelectrons,the ambient temperature and the humidity. This paper will cover the details of those results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

A distributed temperature profiling system for vertically and laterally dense acquisition of soil and snow temperature

Abstract. Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, frozen-/thawed-layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow depth and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel distributed temperature profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of ±0.1 ∘C and enables improving it to ±0.015 ∘C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 s time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow depth and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Solvent depression of transition temperature to selectively stimulate actuation of shape memory polymer foams

An embodiment of the invention is a shape memory polymer (SMP) foam designed to recover its original shape through exposure to a solvent. Thermo-responsive SMPs are polymers designed to maintain a programmed secondary shape until heated above their transition temperature, upon which the polymer recovers its original, or primary, shape. The thermo-responsive SMP foam is programmed to its secondary shape prior to use, typically compression of the foam to a small volume, and remains in this programmed shape until exposed to a selected solvent such as dimethyl sulfoxide or ethyl alcohol. Upon exposure to the solvent, the transition temperature of the SMP foam decreases below the temperature of the environment and the SMP foam actuates to its primary shape. The SMP foam is tailored to actuate upon exposure to specific solvents while minimizing or preventing actuation when exposed to water or other solvents. This selective solvent actuation can be used to increase working time of a SMP foam device, that is, the time allowed for use of a device without undesired actuation, while maintaining functional SMP actuation. Solvent actuated SMP foams can be used in various applications including, but not limited to, treatment of aneurysms and arterio-venous malformations, tissue engineering, and wound healing.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Solvent depression of transition temperature to selectively stimulate actuation of shape memory polymer foams

Boyle, Anthony↗

DOE OSTI2024

An Efficient Annual-Performance Model of a Geothermal Network for Improved System Design, Operation, and Control: Preprint

Geothermal district energy systems (DES), and specifically geothermal networks, provide a viable solution for decarbonizing residential and commercial heating and cooling loads. District energy systems of all kinds enable a thermal resource with a relatively high capital cost (such as a geothermal borehole field) to be shared among a large number of users. While district heating and cooling has been studied for many years, geothermal networks, fifth generation DES that utilize water-source heat pumps and an ambient temperature loop to meet heating and cooling loads, have not been implemented extensively and thus require additional technical and economic optimization to obtain maximum benefits. This paper presents a newly developed reduced-order model that captures the flow of energy within the network, including the commercial and residential users' electrical usage, at an hourly rate over a year. The model includes building loads, heat pumps, borehole fields, and auxiliary heat/cool input, all connected with an ambient-temperature thermal loop model. In the model, operational control is possible for the borehole fields, circulation pump, and auxiliary system. For a given system, the model can output the complete state parameters for each component, the thermal loop, and the collective system, such as flow rate over time, average thermal loop temperature over time, and total electricity usage. The model can also be used to optimize the system control for maximizing system efficiency or minimizing system operational cost. For example, one initial assessment of the borehole controller for an example system showed that a controller with on/off operation of the borehole field reduces annual electrical usage by 33%, compared with continuous operation mode. Hence, the model can assist in optimizing a given system's operation to get the most value out of a geothermal network installation. Future work will consider the model's application to a demonstration project, including the model validation against operational data and system operation optimization.

ambient-temperature loop↗

DOE OSTI2022

High-order diamond differencing schemes for the Boltzmann Fokker-Planck equation in 3D Cartesian geometries

The Boltzmann Fokker-Planck, an approximate form of the linear Boltzmann equation is commonly used to treat efficiently the transport of charged particles in matter. This paper introduces the application of high-order diamond differencing schemes (HODD), specifically the DD1 and DD2 schemes which are 4- and 6-order accurate respectively, to handle the spatial discretization of that equation in 3D Cartesian geometries. The energy deposition solutions for the coupled transport of electrons and photons presented in this work shows that HODD, compared to classical DD scheme, provides correction to the oscillations and a reduced propensity to yield negative fluxes. They are useful tools to minimize local error, notably in regions with abrupt variations of the flux solution. They also can be used to reduced execution time by decreasing the needed number of voxels to obtain a fixed accuracy. On the tested benchmarks, the DD1 scheme is 87%- 92%-91% more accurate than the classical DD scheme for total, mean per-voxels and maximum deviation of energy deposition values respectively. For comparison, a calculation with 8 times more voxels, requiring roughly 2.5 times more time to execute, is 92%-90%-77% more accurate. (authors)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

POINT: Partially Observable Imitation Network for Traffic Signal Control

Smart traffic signals bring together transportation infrastructure and advance technologies to improve the mobility and efficiency of urban transportation network. Adaptive traffic signal control studies can be categorized into modeling-based approaches and learning-based approaches. In order to take advantages of these two systems, this study developed an offline-online combined Partial Observable Imitation Network for Traffic signal control (POINT). In the offline system, the traffic signal timing optimization problem was formulated as a Mixed Integer Nonlinear Programming (MINLP) given complete traffic information, i.e., second-by-second speeds and locations of all vehicles. Furthermore, the objective of MINLP is to minimize total travel delays considering individual vehicle trajectories under Connected Vehicle (CV) environment. The calculated optimal solutions under various traffic conditions were considered as the ”expert” decisions. In the online system, an imitation neural network model was developed to learn the ”expert” signal plans generated from offline system. Given partial observable traffic conditions in real time, e.g., the aggregate-level of traffic volume, the POINT model can compute the signal timing parameters in the online system. The numerical results demonstrated that the proposed method outperformed other state-of-the-art signal control method under high and unbalanced traffic demand levels in terms of reducing travel delays and queue length.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Cycle-to-cycle variability in spark-assisted compression ignition engines near optimal mean combustion phasing

Stoichiometric spark-assisted compression ignition (SACI) combustion with exhaust gas recirculation (EGR) dilution has demonstrated higher part-load thermal efficiencies compared to spark-ignited engines, while maintaining ultra-low tailpipe emissions. However, SACI is often characterized by high cyclic variability in heat release or torque output, which poses a challenge to its implementation in light-duty vehicles. This paper presents an experimental investigation of cyclic variability in stoichiometric SACI combustion under EGR-dilute conditions, while maintaining mean combustion phasing (θ 50 ) near optimal timing for thermal efficiency. The present work focuses on SACI conditions where the flame-based heat release fraction is between approximately 10% and 40% of the overall heat release, and therefore contributes significantly to the combustion process. For the SACI conditions examined, the variability in θ 50 was driven by variability in autoignition timing, which in turn correlated with the start of measurable heat release (θ 02 ). High variability in θ 50 caused unstable work output due to very late combustion with poor or no end-gas autoignition. The use of a high ignition energy dual-coil offset ignition system had negligible impact in reducing θ 50 variability. Analysis of experimental data from close to 1000 operating conditions showed that the magnitude of θ 50 variability correlates with the flame-based heat release fraction ([Formula: see text]), for a large range of intake pressures, spark timings and exhaust gas recirculation levels. For the SACI conditions examined, combustion phasing variability was largely determined by flame-based combustion and particularly the initial flame formation (θ IGN-02 ), and minimally by the end-gas autoignition heat release. The analysis also demonstrated that θ 50 variability is amplified as the contribution of the flame to the overall heat release increases.

Engineering↗

DOE OSTI2021

Sensor System and Observer Algorithm Co-Design For Modern Internal Combustion Engine Air Management Based on H2 Optimization

This paper outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on H 2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the H 2 norm of the observer error and the weighted l 1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited engine using exhaust gas circulation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best H 2 estimation performance. Sensor redundancy is also analyzed based on the algorithm results. This algorithm is applicable for any type of modern internal combustion engines to reduce system design time and experimental efforts typically required for selecting optimal sensor sets.

Zhang, Xu↗

DOE OSTI2023

Multi-Objective Adaptive Mesh Refinement Using Reinforcement Learning

Finite element methods approximate the solution to a partial differential equation (PDE) on a mesh consisting of many elements. In general, using more, smaller elements results in a lower error in the approximation. However, it is often possible to lower the error substantially by only refining, or decreasing the size of, the few elements in the mesh that have the highest error. Adaptive mesh refinement (AMR) is a process that selectively refines regions of a mesh with high error to achieve a desired accuracy in as few degrees of freedom (DOFs) as possible. AMR is favorable compared to uniform refinement, which refines all elements of the mesh equally, because it can often achieve the same accuracy without wasting extra computation time on refinement of elements that already have low error. However, it is difficult to know which elements to refine. In this report we explore ways to choose which elements to refine such that we minimize both the resulting error and the cumulative DOFs used in computation. In particular, we introduce a Pareto-front learning algorithm that trains a policy to give the optimal refinement actions to minimize the cumulative DOFs used to achieve a given target error. Such a policy is useful because it can be deployed on many different problem types where different accuracy levels are desired. Furthermore, training a single policy for a range of target errors allows us to use transfer learning to reduce the required training time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Machine learning based rate optimization under geologic uncertainty

We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.

02 PETROLEUM↗